Search

Background Purpose Methods Results Conclusions The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single‐energy CT. But for multi‐energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time.This work proposes a novel GPU‐based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi‐energy x‐ray exposures.The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy‐shared photon groups. Subsequently, the multi‐directional sampling strategy is utilized to select energy‐and‐direction‐shared photons, which have the same initial direction, from energy‐shared photon groups. Energy‐and‐direction‐shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases.The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation.The proposed GPU‐based shared MC simulation method can achieve fast photon transport calculation for multi‐energy x‐ray exposures. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Computed tomography (CT) relies on the attenuation of x‐rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X‐ray dark‐field (DF) imaging is a recently developed technology that utilizes x‐ray optical gratings to enable small‐angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human‐scale x‐ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefact removal.Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post‐processing for artefact reduction in x‐ray DF CT and how multi‐rotation scans can serve as a pathway for training data.We employed a supervised deep‐learning approach using a three‐dimensional dual‐frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x‐ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose‐compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose‐, nor time‐compatible and, therefore, unfeasible for clinical imaging of patients.The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truth images.Our results showcase the potential of a neural network to reduce streak artifacts in x‐ray DF CT. The image quality is successfully enhanced in dose‐compatible x‐ray DF CT, which plays an essential role for the adoption of x‐ray DF CT into modern clinical radiology. [ABSTRACT FROM AUTHOR]

Background: Motion induced image artifacts have been the focus of many investigations for x‐ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post‐processing algorithms to minimize motion artifacts. Purpose: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image‐space spatial‐ and temporal‐consistency metric (CM) to detect and characterize the local motion. Method: In a non‐rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region‐of‐interests for consistency‐metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Results: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in‐plane motion, cross‐plane motion, cone‐beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. Conclusions: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view‐by‐view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact. [ABSTRACT FROM AUTHOR]

BackgroundRadiation dosimetry is essential for radiation therapy (RT) to ensure that radiation dose is accurately delivered to the tumor. Despite its wide use in clinical intervention, the delivered radiation dose can only be planned and verified via simulation. This makes precision radiotherapy challenging while in-line verification of the delivered dose is still absent in the clinic. X-ray-induced acoustic computed tomography (XACT) has recently been proposed as an imaging tool for in vivo dosimetry.PurposeMost of the XACT studies focus on localizing the radiation beam. However, it has not been studied for its potential for quantitative dosimetry. The aim of this study was to investigate the feasibility of using XACT for quantitative in vivo dose reconstruction during radiotherapy.MethodsVarian Eclipse system was used to generate simulated uniform and wedged 3D radiation field with a size of 4 cm ×$ \times \ $ 4 cm. In order to use XACT for quantitative dosimetry measurements, we have deconvoluted the effects of both the x-ray pulse shape and the finite frequency response of the ultrasound detector. We developed a model-based image reconstruction algorithm to quantify radiation dose in vivo using XACT imaging, and universal back-projection (UBP) reconstruction is used as comparison. The reconstructed dose was calibrated before comparing it to the percent depth dose (PDD) profile. Structural similarity index matrix (SSIM) and root mean squared error (RMSE) are used for numeric evaluation. Experimental signals were acquired from 4 cm ×$ \times \ $ 4 cm radiation field created by Linear Accelerator (LINAC) at depths of 6, 8, and 10 cm beneath the water surface. The acquired signals were processed before reconstruction to achieve accurate results.ResultsApplying model-based reconstruction algorithm with non-negative constraints successfully reconstructed accurate radiation dose in 3D simulation study. The reconstructed dose matches well with the PDD profile after calibration in experiments. The SSIMs between the model-based reconstructions and initial doses are over 85%, and the RMSEs of model-based reconstructions are eight times lower than the UBP reconstructions. We have also shown that XACT images can be displayed as pseudo-color maps of acoustic intensity, which correspond to different radiation doses in the clinic.ConclusionOur results show that the XACT imaging by model-based reconstruction algorithm is considerably more accurate than the dose reconstructed by UBP algorithm. With proper calibration, XACT is potentially applicable to the clinic for quantitative in vivo dosimetry across a wide range of radiation modalities. In addition, XACT's capability of real-time, volumetric dose imaging seems well-suited for the emerging field of ultrahigh dose rate "FLASH" radiotherapy.

Background Purpose Methods Results Conclusions Automatic segmentation of vertebrae in spinal x‐ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions.However, due to the inherent characteristics of x‐ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer‐aided spine image analysis and disease diagnosis applications.In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x‐ray images, to aid doctors in diagnosing spinal‐related diseases. To enhance feature extraction, the network incorporates a Dual‐branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual‐branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task‐irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high‐level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability.In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x‐ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global_accuracy.Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions. [ABSTRACT FROM AUTHOR]

Background Purpose Materials and Methods Results Conclusions Inserting noise into existing patient projection data to simulate lower‐radiation‐dose exams has been frequently used in traditional energy‐integrating‐detector (EID)‐CT to optimize radiation dose in clinical protocols and to generate paired images for training deep‐learning‐based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD‐CT) also requires such a method to accomplish these tasks. However, clinical PCD‐CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log‐normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD‐CT.To develop and validate a projection domain noise insertion algorithm for PCD‐CT that does not require access to the raw count data.A projection‐domain noise model developed originally for EID‐CT was adapted for PCD‐CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD‐CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment.The NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges.A projection‐domain noise insertion algorithm was developed and validated for PCD‐CT to synthesize low‐dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep‐learning‐based methods. [ABSTRACT FROM AUTHOR]

Background: Ionization chambers play an essential role in dosimetry measurements for kilovoltage (kV) x‐ray beams. Despite their widespread use, there is limited data on the absolute values for the polarity correction factors across a range of commonly employed ionization chambers. Purpose: This study aimed to investigate the polarity effects for five different ionization chambers in kV x‐ray beams. Methods: Two plane‐parallel chambers being the Advanced Markus and Roos and three cylindrical chambers; 3D PinPoint, Semiflex and Farmer chamber (PTW, Freiburg, Germany), were employed to measure the polarity correction factors. The kV x‐ray beams were produced from an Xstrahl 300 unit (Xstrahl Ltd., UK). All measurements were acquired at 2 cm depth in a PTW‐MP1 water tank for beams between 60 kVp (HVL 1.29 mm Al) and 300 kVp (HVL 3.08 mm Cu), and field sizes of 2–10 cm diameter for 30 cm focus‐source distance (FSD) and 4 × 4 cm2 – 20 × 20 cm2 for 50 cm FSD. The ionization chambers were connected to a PTW‐UNIDOS electrometer, and the polarity effect was determined using the AAPM TG‐61 code of practice methodology. Results: The study revealed significant polarity effects in ionization chambers, especially in those with smaller volumes. For the plane‐parallel chambers, the Advanced Markus chamber exhibited a maximum polarity effect of 2.5%, whereas the Roos chamber showed 0.3% at 150 KVp with the 10 cm circular diameter open‐ended applicator. Among the cylindrical chambers at the same beam energy and applicator, the Pinpoint chamber exhibited a 3% polarity effect, followed by Semiflex with 1.7%, and Farmer with 0.4%. However, as the beam energy increased to 300 kVp, the polarity effect significantly increased reaching 8.5% for the Advanced Markus chamber and 13.5% for the PinPoint chamber at a 20 × 20 cm2 field size. Notably, the magnitude of the polarity effect increased with both the field size and beam energy, and was significantly influenced by the size of the chamber's sensitive volume. Conclusions: The findings demonstrate that ionization chambers can exhibit substantial polarity effects in kV x‐ray beams, particularly for those chambers with smaller volumes. Therefore, it is important to account for polarity corrections when conducting relative dose measurements in kV x‐ray beams to enhance the dosimetry accuracy and improve patient dose calculations. [ABSTRACT FROM AUTHOR]

Background: Pulmonary emphysema is a part of chronic obstructive pulmonary disease, which is an irreversible chronic respiratory disease. In order to avoid further damage to lung tissue, early diagnosis and treatment of pulmonary emphysema is essential. Purpose: Early pulmonary emphysema diagnosis is difficult with conventional radiographic imaging. Recently, x‐ray phase contrast imaging has proved to be an effective and promising imaging strategy for soft tissue, due to its high sensitivity and multi‐contrast. The aim of this study is to diagnose pulmonary emphysema early utilizing an x‐ray Talbot‐Lau interferometer (TLI). Methods: We successfully established the mouse model of emphysema by porcine pancreatic elastase treatment, and then used the established x‐ray TLI to perform imaging experiments on the mice with different treatment time. The traditional absorption CT and phase contrast CT were obtained simultaneously through TLI. The CT results and histopathology of mice lung in different treatment time were quantitatively analyzed. Results: By imaging mice lungs, it can be found that phase contrast has higher sensitivity than absorption contrast in early pulmonary emphysema. The results show that the phase contrast signal could distinguish the pulmonary emphysema earlier than the conventional attenuation signal, which can be consistent with histological images. Through the quantitative analysis of pathological section and phase contrast CT, it can be found that there is a strong linear correlation. Conclusions: In this study, we quantitatively analyze mean linear intercept of histological sections and CT values of mice. The results show that the phase contrast signal has higher imaging sensitivity than the attenuation signal. X‐ray TLI multi‐contrast imaging is proved as a potential diagnostic method for early pulmonary emphysema in mice. [ABSTRACT FROM AUTHOR]

Background: The increasing use of complex and high dose‐rate treatments in radiation therapy necessitates advanced detectors to provide accurate dosimetry. Rather than relying on pre‐treatment quality assurance (QA) measurements alone, many countries are now mandating the use of in vivo dosimetry, whereby a dosimeter is placed on the surface of the patient during treatment. Ideally, in vivo detectors should be flexible to conform to a patient's irregular surfaces. Purpose: This study aims to characterize a novel hydrogenated amorphous silicon (a‐Si:H) radiation detector for the dosimetry of therapeutic x‐ray beams. The detectors are flexible as they are fabricated directly on a flexible polyimide (Kapton) substrate. Methods: The potential of this technology for application as a real‐time flexible detector is investigated through a combined dosimetric and flexibility study. Measurements of fundamental dosimetric quantities were obtained including output factor (OF), dose rate dependence (DPP), energy dependence, percentage depth dose (PDD), and angular dependence. The response of the a‐Si:H detectors investigated in this study are benchmarked directly against commercially available ionization chambers and solid‐state diodes currently employed for QA practices. Results: The a‐Si:H detectors exhibit remarkable dose linearities in the direct detection of kV and MV therapeutic x‐rays, with calibrated sensitivities ranging from (0.580 ± 0.002) pC/cGy to (19.36 ± 0.10) pC/cGy as a function of detector thickness, area, and applied bias. Regarding dosimetry, the a‐Si:H detectors accurately obtained OF measurements that parallel commercially available detector solutions. The PDD response closely matched the expected profile as predicted via Geant4 simulations, a PTW Farmer ionization chamber and a PTW ROOS chamber. The most significant variation in the PDD performance was 5.67%, observed at a depth of 3 mm for detectors operated unbiased. With an external bias, the discrepancy in PDD response from reference data was confined to ± 2.92% for all depths (surface to 250 mm) in water‐equivalent plastic. Very little angular dependence is displayed between irradiations at angles of 0° and 180°, with the most significant variation being a 7.71% decrease in collected charge at a 110° relative angle of incidence. Energy dependence and dose per pulse dependence are also reported, with results in agreement with the literature. Most notably, the flexibility of a‐Si:H detectors was quantified for sample bending up to a radius of curvature of 7.98 mm, where the recorded photosensitivity degraded by (−4.9 ± 0.6)% of the initial device response when flat. It is essential to mention that this small bending radius is unlikely during in vivo patient dosimetry. In a more realistic scenario, with a bending radius of 15–20 mm, the variation in detector response remained within ± 4%. After substantial bending, the detector's photosensitivity when returned to a flat condition was (99.1 ± 0.5)% of the original response. Conclusions: This work successfully characterizes a flexible detector based on thin‐film a‐Si:H deposited on a Kapton substrate for applications in therapeutic x‐ray dosimetry. The detectors exhibit dosimetric performances that parallel commercially available dosimeters, while also demonstrating excellent flexibility results. [ABSTRACT FROM AUTHOR]

Background: X‐ray multi‐contrast imaging with gratings provides a practical method to detect differential phase and dark‐field contrast images in addition to the x‐ray absorption image traditionally obtained in laboratory or hospital environments. Systems have been developed for preclinical applications in areas including breast imaging, lung imaging, rheumatoid arthritis hand imaging and kidney stone imaging. Purpose: Prevailing x‐ray interferometers for multi‐contrast imaging include Talbot‐Lau interferometers and universal moiré effect‐based phase‐grating interferometers. Talbot‐Lau interferometers suffer from conflict between high interferometer sensitivity and large field of view (FOV) of the object being imaged. A small period analyzer grating is necessary to simultaneously achieve high sensitivity and large FOV within a compact imaging system but is technically challenging to produce for high x‐ray energies. Phase‐grating interferometers suffer from an intrinsic fringe period ranging from a few micrometers to several hundred micrometers that can hardly be resolved by large area flat panel x‐ray detectors. The purpose of this work is to introduce a four‐grating x‐ray interferometer that simultaneously allows high sensitivity and large FOV, without the need for a small period analyzer grating. Methods: The four‐grating interferometer consists of a source grating placed downstream of and close to the x‐ray source, a pair of phase gratings separated by a fixed distance placed downstream of the source grating, and an analyzer grating placed upstream of and close to the x‐ray detector. The object to be imaged is placed upstream of and close to the phase‐grating pair. The distance between the source grating and the phase‐grating pair is designed to be far larger than that between the phase‐grating pair and the analyzer grating to promote simultaneously high sensitivity and large FOV. The method was evaluated by constructing a four‐grating interferometer with an 8 µm period source grating, a pair of phase gratings of 2.4 µm period, and an 8 µm period analyzer grating. Results: The fringe visibility of the four‐grating interferometer was measured to be ≈24% at 40 kV and ≈18% at 50 kV x‐ray tube operating voltage. A quartz bead of 6 mm diameter was imaged to compare the theoretical and experimental phase contrast signal with good agreement. Kidney stone specimens were imaged to demonstrate the potential of such a system for classification of kidney stones. Conclusions: The proposed four‐grating interferometer geometry enables a compact x‐ray multi‐contrast imaging system with simultaneously high sensitivity and large FOV. Relaxation of the requirement for a small period analyzer grating makes it particularly suitable for high x‐ray energy applications such as abdomen and chest imaging. [ABSTRACT FROM AUTHOR]

Background: Misalignment or double‐contouring artifacts can appear in high‐resolution 3D cone beam computed tomography (CBCT) images, potentially indicating geometric accuracy issues in the projection data. Such artifacts may go unnoticed in low‐resolution images and could be associated with changes in the focal spot (FS) position. Purpose: High‐resolution 3D‐CBCT imaging by a mobile imaging device with a large gantry clearance offers more versatility for clinical workflows in image‐guided brachytherapy (IGBT), intraoperative radiation therapy (IORT), and spinal, as well as maxillofacial surgery. However, misalignment or double‐contouring artifacts hinder workflow advancements in these domains. This paper introduces intrinsic calibration and geometrical correction methods as extensions to a well‐established technique for addressing geometrical deviations resulting from factors such as gravity or mechanical inconsistencies. These extensions cover shifts and drifts of the FS depending on FS size selection, temperature, tube current, and tube potential. The proposed methods effectively mitigate artifacts in high‐resolution CBCT images stemming from geometrical inaccuracies in projection data, without requiring additional equipment like a pinhole device. Methods: Geometrical offsets and drifts of the x‐ray tube FS were characterized on a mobile multi‐purpose imaging system, the ImagingRing‐m. A pinhole‐like experiment was simulated by adjusting the movable collimation unit to a small rectangular aperture within the FS size range. The influence of filament selection, that is, FS size, temperature, the relatively low tube currents, as well as tube potential settings have been studied on two different monobloc types sharing the same x‐ray tube insert. The Catphan 504 and an Alderson head phantom were used to assess resulting image artifacts. Results: Switching the FS size to one different from what was used for geometrical (gravitation, mechanical variations) calibration induced the most notable position changes of the x‐ray FS, resulting in double‐contouring artifacts and blurring of high‐resolution 3D‐CBCT images. Incorporating these shifts into a geometrical correction method effectively minimized these artifacts. Thermal drifts exhibited the second largest geometrical changes, comparable to FS size shifts across the thermal operating conditions of the x‐ray system. The proposed thermal drift compensation markedly reduced thermal drift effects. Tube current and potential had little impact within the range of available tube currents, eliminating the need for compensation in current applications. Conclusions: Augmenting the geometrical calibration pipeline with proposed FS drift compensations yielded significant enhancements in image quality for high‐resolution reconstructions. While compensation for thermal effects posed challenges, it proved achievable. The roles of tube current and potential were found to be negligible. [ABSTRACT FROM AUTHOR]

Background: Position verification and motion monitoring are critical for safe and precise radiotherapy (RT). Existing approaches to these tasks based on visible light or x‐ray are suboptimal either because they cannot penetrate obstructions to the patient's skin or introduce additional radiation exposure. The low‐cost mmWave radar is an ideal solution for these tasks as it can monitor patient position and motion continuously throughout the treatment delivery. Purpose: To develop and validate frequency‐modulated continuous wave (FMCW) mmWave radars for position verification and motion tracking during RT delivery. Methods: A 77 GHz FMCW mmWave module was used in this study. Chirp Z Transform‐based (CZT) algorithm was developed to process the intermediate frequency (IF) signals. Absolute distances to flat Solid Water slabs and human shape phantoms were measured. The accuracy of absolute distance and relative displacement were evaluated. Results: Without obstruction, mmWave based on the CZT algorithm was able to detect absolute distance within 1 mm for a Solid Water slab that simulated the reflectivity of the human body. Through obstructive materials, the mmWave device was able to detect absolute distance within 5 mm in the worst case and within 3.5 mm in most cases. The CZT algorithm significantly improved the accuracy of absolute distance measurement compared with Fast Fourier Transform (FFT) algorithm and was able to achieve submillimeter displacement accuracy with and without obstructions. The surface‐to‐skin distance (SSD) measurement accuracy was within 8 mm in the anterior of the phantom. Conclusions: With the CZT signal processing algorithm, the mmWave radar is able to measure the absolute distance to a flat surface within 1 mm. But the absolute distance measurement to a human shape phantom is as large as 8 mm at some angles. Further improvement is necessary to improve the accuracy of SSD measurement to uneven surfaces by the mmWave radar. [ABSTRACT FROM AUTHOR]

Background: The breath‐hold radiotherapy has been increasingly used to mitigate interfractional and intrafractional breathing impact on treatment planning and beam delivery. Previous techniques include body surface measurements or radiopaque metal markers, each having known disadvantages. Purpose: We recently proposed a new markerless technique without the disadvantages, where diaphragm was registered between DRR and fluoroscopic x‐ray projection images every 180 ms during VMAT delivery. An initial validation of the proposed diaphragm tracking system (DiaTrak) was performed using a chest phantom to evaluate its characteristics. Methods: Diaphragm registration was performed between DRR and projection streaming kV x‐ray images of a chest phantom during VMAT delivery. Streaming data including the projection images and the beam angles were transferred from a linac system to an external PC, where the diaphragm registration accuracy and beam‐off latency were measured based on image cross correlation between the DRR and the projection images every 180 ms. Results: It was shown that the average of the beam‐off latency was 249.5 ms and the average of the diaphragm registration error was 0.84 mm Conclusions: Initial validation of the proposed DiaTrak system for multiple breath‐hold VMAT of abdominal tumors has been successfully completed with a chest phantom. The resulting beam‐off latency and the diaphragm registration error were regarded clinically acceptable. [ABSTRACT FROM AUTHOR]

Background: Ocular foreign bodies (OFBs) are a relatively common occurrence in ocular injuries, and a severe risk factor for vision disorders. They are notoriously challenging to identify and localize precisely to allow surgical removal, even with the most recent technological advancements. Purpose: To compare the efficiency of different imaging methods in detecting and localizing OFBs. Methods: We conducted a retrospective analysis of the medical records of patients with OFBs, detected by ultrasound biomicroscopy (UBM) and confirmed during surgery. Patients who presented to our medical center between January 2016 and January 2022 and also underwent computed tomography (CT), X ray, and/or ocular B‐scan ultrasonography (B‐scans) were selected. Results: This study included 134 patients with a history of ocular trauma and OFBs (mean age: 47.25 years, range: 8–78). The mean time interval from injury to UBM examination was 36.31 months (range: 0.2–120 months). Most OFBs were metallic (51.82%) or plant‐based (25.37%); 22.39% of them were located in the sclera, 26.87% in the anterior chamber, and 23.88% in the ciliary body and iris. OFBs ranged in size from 0.10 to 6.67 mm (mean: 1.15 ± 1.10 mm). B‐scans identified OFBs in 37 of the 119 patients examined (31.09%); CT in 52 of 84 patients (61.90%); and radiography in 29 of 50 patients (58.00%). Univariate and multivariate analyses determined that both CT and radiography showed low detection rates for plant‐based versus non‐plant‐based OFBs (CT: p < 0.001; radiography: p = 0.007), small particles (<1.00 mm vs. >1.00 mm; CT: p = 0.001, radiography: p = 0.024), and with eyeball wall locations (vs. intraocular; CT: p < 0.001, radiography: p = 0.021). Similarly, B‐scans were less efficient for plant‐based and eyeball wall‐located OFBs (both p = 0.001), whereas the difference based on dimensions was not significant (p = 0.118). Conclusions: CT, radiography, and B‐scans showed lower detection rates for plant‐based, small, and eyeball wall‐located OFBs. Our findings strongly suggest that UBM could be a more adequate imaging modality when such OFBs are suspected. [ABSTRACT FROM AUTHOR]

Background: Chest x‐ray is widely utilized for the evaluation of pulmonary conditions due to its technical simplicity, cost‐effectiveness, and portability. However, as a two‐dimensional (2‐D) imaging modality, chest x‐ray images depict limited anatomical details and are challenging to interpret. Purpose: To validate the feasibility of reconstructing three‐dimensional (3‐D) lungs from a single 2‐D chest x‐ray image via Vision Transformer (ViT). Methods: We created a cohort of 2525 paired chest x‐ray images (scout images) and computed tomography (CT) acquired on different subjects and we randomly partitioned them as follows: (1) 1800 ‐ training set, (2) 200 ‐ validation set, and (3) 525 ‐ testing set. The 3‐D lung volumes segmented from the chest CT scans were used as the ground truth for supervised learning. We developed a novel model termed XRayWizard that employed ViT blocks to encode the 2‐D chest x‐ray image. The aim is to capture global information and establish long‐range relationships, thereby improving the performance of 3‐D reconstruction. Additionally, a pooling layer at the end of each transformer block was introduced to extract feature information. To produce smoother and more realistic 3‐D models, a set of patch discriminators was incorporated. We also devised a novel method to incorporate subject demographics as an auxiliary input to further improve the accuracy of 3‐D lung reconstruction. Dice coefficient and mean volume error were used as performance metrics as the agreement between the computerized results and the ground truth. Results: In the absence of subject demographics, the mean Dice coefficient for the generated 3‐D lung volumes achieved a value of 0.738 ± 0.091. When subject demographics were included as an auxiliary input, the mean Dice coefficient significantly improved to 0.769 ± 0.089 (p < 0.001), and the volume prediction error was reduced from 23.5 ± 2.7%. to 15.7 ± 2.9%. Conclusion: Our experiment demonstrated the feasibility of reconstructing 3‐D lung volumes from 2‐D chest x‐ray images, and the inclusion of subject demographics as additional inputs can significantly improve the accuracy of 3‐D lung volume reconstruction. [ABSTRACT FROM AUTHOR]

Background: Conventional x‐ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual‐energy (DE) imaging, with its capability to quantify area density of specific materials, is well‐suited to address such limitations, but only if the dual‐energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter. Purpose: In this work, we propose single‐shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual‐layer (DL) detector, which enables motion‐free DE imaging with scatter correction in a single exposure. Methods: The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material‐specific images and two scatter images using four sub‐measurements from the PM encoding. The concept was first demonstrated using simulation of chest x‐ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0–22.5 cm; copper: 0–0.9 mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast. Results: Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13 cm for acrylic and 0.04 mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material‐specific images by 38%–92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background. Conclusions: We demonstrated the potential of SSQI for robust quantitative x‐ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real‐time image guidance and cone‐beam CT). [ABSTRACT FROM AUTHOR]

Background: Deep learning models are being applied to more and more use cases with astonishing success stories, but how do they perform in the real world? Models are typically tested on specific cleaned data sets, but when deployed in the real world, the model will encounter unexpected, out‐of‐distribution (OOD) data. Purpose: To investigate the impact of OOD radiographs on existing chest x‐ray classification models and to increase their robustness against OOD data. Methods: The study employed the commonly used chest x‐ray classification model, CheXnet, trained on the chest x‐ray 14 data set, and tested its robustness against OOD data using three public radiography data sets: IRMA, Bone Age, and MURA, and the ImageNet data set. To detect OOD data for multi‐label classification, we proposed in‐distribution voting (IDV). The OOD detection performance is measured across data sets using the area under the receiver operating characteristic curve (AUC) analysis and compared with Mahalanobis‐based OOD detection, MaxLogit, MaxEnergy, self‐supervised OOD detection (SS OOD), and CutMix. Results: Without additional OOD detection, the chest x‐ray classifier failed to discard any OOD images, with an AUC of 0.5. The proposed IDV approach trained on ID (chest x‐ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0.999 OOD AUC across the three data sets, surpassing all other OOD detection methods. Mahalanobis‐based OOD detection achieved an average OOD detection AUC of 0.982. IDV trained solely with a few thousand ImageNet images had an AUC 0.913, which was considerably higher than MaxLogit (0.726), MaxEnergy (0.724), SS OOD (0.476), and CutMix (0.376). Conclusions: The performance of all tested OOD detection methods did not translate well to radiography data sets, except Mahalanobis‐based OOD detection and the proposed IDV method. Consequently, training solely on ID data led to incorrect classification of OOD images as ID, resulting in increased false positive rates. IDV substantially improved the model's ID classification performance, even when trained with data that will not occur in the intended use case or test set (ImageNet), without additional inference overhead or performance decrease in the target classification. The corresponding code is available at https://gitlab.lrz.de/IP/a‐knee‐cannot‐have‐lung‐disease. [ABSTRACT FROM AUTHOR]

Background: The spatial resolution of energy‐integrating diagnostic CT scanners is limited by interpixel reflectors on the detector, which optically isolate pixels but create dead space. Because the width of the reflector cannot easily be decreased, fill factor diminishes as resolution increases. Purpose: We propose loading (or mixing) a high‐Z element into the reflectors, causing the reflectors to be X‐ray fluorescent. Re‐emitted characteristic X‐rays could be detected in adjacent pixels, increasing the effective fill factor and compensating for fill factor loss with higher‐resolution detectors. The purpose of this work is to understand the physical principles of this approach and to analyze its effectiveness using Monte Carlo simulations. Methods: Detector pixels were modeled using the GEANT4 Monte Carlo package. The width of the reflector was kept constant at 0.1 mm throughout, and we considered pixel pitches between 0.5 and 1 mm. The pixelated scintillator material was gadolinium oxysulfide, 3 mm thick. The baseline reflector material was chosen to be acrylic, and varying concentrations of a high‐Z element were loaded into the material. We assumed that the optical characteristics of pixels were ideal (no absorption within pixels, perfect reflection at boundaries). The detector was irradiated uniformly with 10,000 X‐ray photons to estimate its spectral response. The figure of merit was the variance of the detector signal at zero frequency normalized to that of an ideal single‐bin photon‐counting detector with 100% fill factor. Sensitivity analyses were conducted to understand the effect of varying the high‐Z element concentration and the spectrum. Results: Initial simulations suggested that a k‐edge near 50 keV would be ideal. Gd was therefore selected as the high‐Z material. The relative variances for a conventional energy integrating detector without Gd at 1 mm pixel pitch (81% fill factor) and 0.5 mm pixel pitch (64% fill factor) were 1.38 and 1.74, compared to 1.00 for an ideal photon counting detector, implying a 26% variance penalty for 0.5 mm pitch. When 1 g/cm3 Gd was loaded into the interpixel reflector, the relative variance improved to 1.27 and 1.43, respectively, implying that the variance penalty for including Gd together with 0.5 mm pitch is only 4%. Performance was nearly maximized at 1.0 g/cm3 of Gd, but a concentration of 0.5 g/cm3 of Gd showed most of the benefit. Improvements depend weakly on kV, with lower kV associated with higher improvements. An external anti‐scatter grid was not modeled in our simulations and would reduce the expected benefit, depending greatly on the pitch and dimensionality of the anti‐scatter grid. Conclusions: The losses in fill factor associated with smaller pixel pitch can be reduced if Gd or a similar element could be loaded into the interpixel reflector. These improvements in noise efficiency are yet to be verified experimentally. [ABSTRACT FROM AUTHOR]

Background: Many healthcare institutions have guidelines concerning the usage of protective procedures, and various x‐ray shields have been used to reduce unwanted radiation exposure to medical staff and patients when using x‐rays. Most x‐ray shields are in the form of sheets and lack elasticity, which limits their effectiveness in shielding areas with movement, such as the thyroid. To overcome this limitation, we have developed an innovative elastic x‐ray shield. Purpose: The purpose of this study is to explain the methodology for developing and evaluating a novel elastic x‐ray shield with sufficient x‐ray shielding ability. Furthermore, valuable knowledge and evaluation indices are derived to assess our shield's performance. Methods: Our x‐ray shield was developed through a process of embedding Bi2O3 particles into porous polyurethane. Porous polyurethane with a thickness of 10 mm was dipped into a solution of water, metal particles, and chemical agents. Then, it was air‐dried to fix the metal particles in the porous polyurethane. Thirteen investigational x‐ray shields were fabricated, in which Bi2O3 particles at various mass thicknesses (ranging from 585 to 2493 g/m2) were embedded. To determine the performance of the shielding material, three criteria were evaluated: (1) Dose Reduction Factor (DRF$DRF$), measured using inverse broad beam geometry; (2) uniformity, evaluated from the standard deviation (SD$SD$) of the x‐ray image obtained using a clinical x‐ray imaging detector; and (3) elasticity, evaluated by a compression test. Results: The elastic shield with small pores, containing 1200 g/m2 of the metal element (Bi), exhibited a well‐balanced performance. The DRF$DRF$ was approximately 80% for 70 kV diagnostic x‐rays. This shield's elasticity was −0.62 N/mm, a loss of only 30% when compared to porous polyurethane without metal. Although the non‐uniformity of the x‐ray shield leads to poor shielding ability, it was found that the decrease in the shielding ability can be limited to a maximum of 6% when the shield is manufactured so that the SD$SD$ of the x‐ray image of the shield is less than 10%. Conclusions: It was verified that an elastic x‐ray shield that offers an appropriate reduction in radiation exposure can be produced by embedding Bi2O3 particles into porous polyurethane. Our findings can lead to the development of novel x‐ray shielding products that can reduce the physical and mental stress on users. [ABSTRACT FROM AUTHOR]

Background: Carbon nanotube‐based cold cathode technology has revolutionized the miniaturization of X‐ray tubes. However, current applications of these devices required optimization for large, uniform fields with low intensity. Purpose: This work investigated the feasibility and radiological characteristics of a novel conical X‐ray target optimized for high intensity and high directionality to be used in a compact X‐ray tube. Methods: The proposed device uses an ultrathin, conical tungsten‐diamond target that exhibits significant heat loading while maintaining a small focal spot size and promoting forward‐directedness of the X‐ray field through preferential attenuation of oblique‐angled photons. The electrostatic and thermal properties of the theoretical tube were calculated and analyzed using COMSOL Multiphysics software. The production, transport, and calculation of radiological properties associated with the resultant X‐ray field were performed using the Geant4 toolkit via its wrapper, TOPAS. Results: Heat transfer analysis of this X‐ray tube demonstrated the feasibility of a 200‐kV electron beam bombarding the proposed target at a maximum current of 100 mA using a 1‐ms symmetric duty cycle. The cathode of the X‐ray tube was designed to be segmented into nine switchable electrical segments for modulation of the focal spot size from 0.4‐ to 10.8‐mm. After importing the COMSOL‐derived electron beam into TOPAS for X‐ray production simulations, radiological analysis of the resultant field demonstrated high levels of intrinsic beam collimation while maintaining high intensity. A maximum dose rate of 17,887 cGy/min was calculated for 1‐mm depth in water at 7‐cm distance. Conclusions: The proposed X‐ray tube design can create highly directional X‐ray fields with superior fluence compared to that of current commercial X‐ray tubes of comparable size. [ABSTRACT FROM AUTHOR]

Background: Dual‐energy (DE) x‐ray angiography with photon‐counting detectors (PCDs) may enable single‐exposure DE imaging of coronary vasculature. Purpose: To compare the iodine signal‐difference‐to‐noise ratio (SDNR) of single‐exposure DE angiography with digital subtraction angiography (DSA) and kV‐switching DE angiography for matched patient x‐ray exposure. Methods: In a phantom study, we determined the technique parameters that maximized the iodine SDNR per root entrance air kerma for DSA, kV‐switching DE angiography and single‐exposure DE angiography. We measured SDNR from images of a phantom consisting of an iodine step‐wedge immersed in a water tank of either 20 or 30 cm in thickness. We also imaged a phantom with simulated vessels embedded in background clutter and measured vessel SDNR. For this second phantom, we also applied anti‐correlated noise reduction (ACNR) and calculated the resulting iodine SDNR. All images were acquired using a cadmium telluride PCD with two energy bins and analog charge summing for charge sharing suppression. The energy‐discrimination capabilities were only used for the single‐exposure DE approach. Optimized techniques were compared in terms of SDNR per root air kerma for two levels of x‐ray scatter. Results: For the same patient x‐ray exposure, the SDNR of single‐exposure DE imaging without ACNR was 75% to 85% of that of kV‐switching DE imaging (also without ACNR) and DSA, the latter two of which had nearly equal SDNR. The single‐exposure DE approach required ∼50% of the tube load of the kV‐switching approach to achieve the same SDNR. For matched patient air kermas, the single exposure approach required only ∼25% of the tube load of the kV‐switching approach. ACNR increased SDNR by 2.4 and 3.0 for kV‐switching and single‐exposure DE imaging, respectively. Conclusions: Photon‐counting, single‐exposure DE angiography can suppress soft tissues and provide iodine SDNR levels comparable to DSA and kV‐switching DE angiography for matched patient radiation exposures. When ACNR is used to reduce DE image noise, the SDNR of single‐exposure DE imaging and kV‐switching DE imaging exceed that of DSA by more than a factor of two. Compared to kV‐switching DE imaging, single‐exposure DE imaging requires substantially lower tube loading to achieve the same SDNR. [ABSTRACT FROM AUTHOR]

Background: The bowtie‐filter in cone‐beam CT (CBCT) causes spatially nonuniform x‐ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient‐dependent as well as system‐specific. Purpose: In this study, we propose a dual‐domain network for reducing the bowtie‐filter‐induced artifacts in CBCT images. Methods: In the projection domain, the network compensates for the filter‐induced beam‐hardening that are highly related to the eclipse artifacts. The output of the projection‐domain network was used for image reconstruction and the reconstructed images were fed into the image‐domain network. In the image domain, the network further reduces the remaining cupping artifacts that are associated with the scatter. A single image‐domain‐only network was also implemented for comparison. Results: The proposed approach successfully enhanced soft‐tissue contrast with much‐reduced image artifacts. In the numerical study, the proposed method decreased perceptual loss and root‐mean‐square‐error (RMSE) of the images by 84.5% and 84.9%, respectively, and increased the structure similarity index measure (SSIM) by 0.26 compared to the original input images on average. In the experimental study, the proposed method decreased perceptual loss and RMSE of the images by 87.2% and 92.1%, respectively, and increased SSIM by 0.58 compared to the original input images on average. Conclusions: We have proposed a deep‐learning‐based dual‐domain framework to reduce the bowtie‐filter artifacts and to increase the soft‐tissue contrast in CBCT images. The performance of the proposed method has been successfully demonstrated in both numerical and experimental studies. [ABSTRACT FROM AUTHOR]

Background: Prior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X‐ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects. Purpose: This work builds up on prior established CHO methods and introduces a new approach to switch from the already established "multiple‐sample" CHO implementation to a "single‐sample" technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index. Methods: To assess the proposed method, two image data sets were acquired with a clinical X‐ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple‐sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD) levels. Results: For the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single‐sample approach versus the multiple‐sample method was 0.997 ± 0.056 (range 0.884–1.159). The average single‐direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple‐sample method was 0.38 ± 0.43 (range 0.03–2.20). For the single‐sample approach, the average width was 2.52 ± 0.63 (range 1.11–5.44). For the anthropomorphic background image set, the results were consistent with classical quantum‐limited signal‐to‐noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphic background. Conclusions: A new single‐sample variant of the CHO model to assess the performance of X‐ray angiography imaging systems is proposed. The new approach is consistent with quantum‐limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinical care. [ABSTRACT FROM AUTHOR]

Background: Microscopic imaging of cartilage is a key tool for the study and development of treatments for osteoarthritis. When cellular and sub‐cellular resolution is required, histology remains the gold standard approach, albeit limited by the lack of volumetric information as well as by processing artifacts. Cartilage imaging with the sub‐cellular resolution has only been demonstrated in the synchrotron environment. Purpose: To provide a proof‐of‐concept demonstration of the capability of a laboratory‐based x‐ray phase‐contrast microscope to resolve sub‐cellular features in a cartilage sample. Methods: This work is based on a laboratory‐based x‐ray microscope using intensity‐modulation masks. The structured nature of the beam, resulting from the mask apertures, allows the retrieval of three contrast channels, namely, transmission, refraction and dark‐field, with resolution depending only on the mask aperture width. An ex vivo equine cartilage sample was imaged with the x‐ray microscope and results were validated with synchrotron tomography and histology. Results: Individual chondrocytes, that is, cells responsible for cartilage formation, could be detected with the laboratory‐based microscope. The complementarity of the three retrieved contrast channels allowed the detection of sub‐cellular features in the chondrocytes. Conclusions: We provide the first proof‐of‐concept of imaging cartilage tissue with sub‐cellular resolution using a laboratory‐based x‐ray microscope. [ABSTRACT FROM AUTHOR]

Background: Electron synchrotrons produce x‐ray beams with dose rates orders of magnitude greater than conventional x‐ray tubes and with beam sizes on the order of a few millimeters. These characteristics put severe challenges on current dosimeters to accurately realize absorbed dose or air kerma. Purpose: This work seeks to investigate the suitability of a novel aluminum‐based calorimeter to determine absorbed dose to water with an uncertainty significantly smaller than currently possible with conventional detectors. A lower uncertainty in the determination of absolute dose rate would impact both therapeutic applications of synchrotron‐produced x‐ray beams and research investigations. Methods: A vacuum‐based calorimeter prototype with an aluminum core was built, matching the beam profile of the 140 keV monochromatic x‐ray beam, produced by the Canadian Light Source Biomedical Imaging and Therapy beamline. The choice of material and overall calorimeter design was optimized using FEM thermal modeling software while Monte Carlo radiation transport simulations were used to model the impact of interactions of the radiation beam with the detector components. Results: Corrections for both the thermal conduction and radiation transport effects were of the order of 3% and the simplicity of the geometry, combined with the monochromatic nature of the incident x‐ray beam, meant that the uncertainty in each correction was ≤0.5%. The calorimeter performance was found to be repeatable over multiple irradiations of 1 Gy at the ± 0.6% level, and no systematic dependence on environmental effects or total dose was observed. Conclusion: The combined standard uncertainty in the determination of absorbed dose to aluminum was estimated to be 0.8%, indicating that absorbed dose to water, the ultimate quantity of interest, could be determined with an uncertainty on the order of 1%. This value is an improvement over current techniques used for synchrotron dosimetry and comparable with the state‐of‐the art for conventional kV x‐ray dosimetry. [ABSTRACT FROM AUTHOR]

PurposeAdvances in X-ray phase-contrast imaging can obtain excellent soft-tissue contrast of phase-shift, attenuation, and small-angle scatter. Here, we present fringe patterns for different design parameters of X-ray bi-prism interferometry imaging systems. Our aim is to develop bi-prism interferometry imaging systems with excellent polychromatic performance that produce high-contrast fringes with spatially incoherent X-ray illumination. We also introduce a novel X-ray tube design that uses temporal multiplexing to provide simultaneous virtual "electronic phase stepping" that replace "mechanical phase stepping" popular with grating-based interferometry setups.MethodsIn our investigation, we develop expressions for the irradiance distribution pattern of a bi-prism interferometer composed of multiple point sources and multiple bi-prisms. These expressions are used to plot fringe patterns for X-ray design parameters, including size of point source, number of point sources, and point source separation, and bi-prism design parameters including material, angle, number of bi-prisms, period, and bi-prism array to X-ray source and detector distances.ResultsResults show that the fringe patterns for a bi-prism interferometry system are not longitudinally periodic as with grating interferometers that produce a Talbot-Lau carpet. It is also shown that at 59 keV X-rays the bi-prism material should be something comparable to nickel to obtain reasonable fringe visibility.ConclusionThe irradiance distribution pattern demonstrates that bi-prism interferometry may provide comparable or improved fringe visibility to that of gratings. Special care is given to present our findings within the context of previous advancements. A single-shot image acquisition approach using a temporal multiplexed, high-power X-ray source provides virtual electronic phase stepping without focal spot sweeping. This provides multiple images, each at the same exposure and the same projection view, from different fringe locations that allow one to derive the attenuation, phase, and dark-field images of the sample without mechanical phase stepping of a grating.

PurposeIn this work, we present tomographic simulations of a new hardware concept for X-ray phase-contrast interferometry wherein the phase gratings are replaced with an array of Fresnel biprisms, and Moiré fringe analysis is used instead of "phase stepping" popular with grating-based setups.MethodsProjections of a phantom consisting of four layers of parallel carbon microfibers is simulated using wave optics representation of X-ray electromagnetic waves. Simulated projections of a phantom with preferential scatter perpendicular to the direction of the fibers are performed to analyze the extraction of small-angle scatter from dark-field projections for the following: (1) biprism interferometry using Moiré fringe analysis; (2) grating interferometry using phase stepping with eight grating steps; and (3) grating interferometry using Moiré fringe analysis. Dark-field projections are modeled as projections of voxel intensities represented by a fixed finite vector basis set of scattering directions. An iterative MLEM algorithm is used to reconstruct, from simulated projection data, the coefficients of a fixed set of seven basis vectors at each voxel representing the small-angle scatter distribution.ResultsResults of reconstructed vector coefficients are shown comparing the three simulated imaging configurations. The single-exposure Moiré fringe analysis shows not only an increase in noise because of one-eighth the number of projection samples but also is obtained with less dose and faster acquisition times. Furthermore, replacing grating interferometry with biprism interferometry provides better contrast-to-noise.ConclusionThe simulations demonstrate the feasibility of the reconstruction of dark-field data acquired with a biprism interferometry system. With the potential of higher fringe visibility, biprism interferometry with Moiré fringe analysis might provide equal or better image quality to that of phase stepping methods with less imaging time and lower dose.

Background: Vascular diseases are often treated minimally invasively. The interventional material (stents, guidewires, etc.) used during such percutaneous interventions are visualized by some form of image guidance. Today, this image guidance is usually provided by 2D X‐ray fluoroscopy, that is, a live 2D image. 3D X‐ray fluoroscopy, that is, a live 3D image, could accelerate existing and enable new interventions. However, existing algorithms for the 3D reconstruction of interventional material require either too many X‐ray projections and therefore dose, or are only capable of reconstructing single, curvilinear structures. Purpose: Using only two new X‐ray projections per 3D reconstruction, we aim to reconstruct more complex arrangements of interventional material than was previously possible. Methods: This is achieved by improving a previously presented deep learning‐based reconstruction pipeline, which assumes that the X‐ray images are acquired by a continuously rotating biplane system, in two ways: (a) separation of the reconstruction of different object types, (b) motion compensation using spatial transformer networks. Results: Our pipeline achieves submillimeter accuracy on measured data of a stent and two guidewires inside an anthropomorphic phantom with respiratory motion. In an ablation study, we find that the aforementioned algorithmic changes improve our two figures of merit by 75 % (1.76 mm → 0.44 mm) and 59 % (1.15 mm → 0.47 mm) respectively. A comparison of our measured dose area product (DAP) rate to DAP rates of 2D fluoroscopy indicates a roughly similar dose burden. Conclusions: This dose efficiency combined with the ability to reconstruct complex arrangements of interventional material makes the presented algorithm a promising candidate to enable 3D fluoroscopy. [ABSTRACT FROM AUTHOR]

Purpose: High dose rate (HDR) brachytherapy rapidly delivers dose to targets with steep dose gradients. This treatment method must adhere to prescribed treatment plans with high spatiotemporal accuracy and precision, as failure to do so may degrade clinical outcomes. One approach to achieving this goal is to develop imaging techniques to track HDR sources in vivo in reference to surrounding anatomy. This work investigates the feasibility of using an isocentric C‐arm x‐ray imager and tomosynthesis methods to track Ir‐192 HDR brachytherapy sources in vivo over time (4D). Methods: A tomosynthesis imaging workflow was proposed and its achievable source detectability, localization accuracy, and spatiotemporal resolution were investigated in silico. An anthropomorphic female XCAT phantom was modified to include a vaginal cylinder applicator and Ir‐192 HDR source (0.5 × 0.5 × 5.0 mm3), and the workflow was carried out using the MC‐GPU Monte Carlo image simulation platform. Source detectability was characterized using the reconstructed source signal‐difference‐to‐noise‐ratio (SDNR), localization accuracy by the absolute 3D error in its measured centroid location, and spatiotemporal resolution by the full‐width‐at‐half‐maximum (FWHM) of line profiles through the source in each spatial dimension considering a maximum C‐arm angular velocity of 30° per second. The dependence of these parameters on acquisition angular range (θtot = 0°–90°), number of views, angular increment between views (Δθ = 0°–15°), and volumetric constraints imposed in reconstruction was evaluated. Organ voxel doses were tallied to derive the workflow's attributable effective dose. Results: The HDR source was readily detected and its centroid was accurately localized with the proposed workflow and method (SDNR: 10–40, 3D error: 0–0.144 mm). Tradeoffs were demonstrated for various combinations of image acquisition parameters; namely, increasing the tomosynthesis acquisition angular range improved resolution in the depth‐encoded direction, for example from 2.5 mm to 1.2 mm between θtot = 30o and θtot = 90o, at the cost of increasing acquisition time from 1 to 3 s. The best‐performing acquisition parameters (θtot = 90o, Δθ = 1°) yielded no centroid localization error, and achieved submillimeter source resolution (0.57 × 1.21 × 5.04 mm3 apparent source dimensions, FWHM). The total effective dose for the workflow was 263 µSv for its required pre‐treatment imaging component and 7.59 µSv per mid‐treatment acquisition thereafter, which is comparable to common diagnostic radiology exams. Conclusions: A system and method for tracking HDR brachytherapy sources in vivo using C‐arm tomosynthesis was proposed and its performance investigated in silico. Tradeoffs in source conspicuity, localization accuracy, spatiotemporal resolution, and dose were determined. The results suggest this approach is feasible for localizing an Ir‐192 HDR source in vivo with submillimeter spatial resolution, 1–3 second temporal resolution and minimal additional dose burden. [ABSTRACT FROM AUTHOR]

Background: The advancement of x‐ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi‐contrast imaging and low‐dose coronary angiography, these multi‐channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains. Purpose: We outline and demonstrate a new Multi‐Channel Reconstruction (MCR) Toolkit for GPU‐based analytical and iterative reconstruction of preclinical and clinical multi‐energy and dynamic x‐ray CT data. To promote open science, open‐source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr‐toolkit‐public). Methods: The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone‐beam CT (CBCT) and 3rd generation, cylindrical multi‐detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone‐parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi‐channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank‐sparse kernel regression (RSKR) and patch‐based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi‐GPU parallelization of the reconstruction operators is supported to manage reconstruction times. Results: Denoising with RSKR and pSVT and post‐reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon‐counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi‐energy (ME), time resolved (TR), and combined multi‐energy and time‐resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual‐source, dual‐energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%–99% efficiency in scaling computation from one to four GPUs for these reconstruction problems. Conclusions: The MCR Toolkit provides a robust solution for temporal and spectral x‐ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications. [ABSTRACT FROM AUTHOR]

Background: High precision radiotherapy with small irradiator size has potential in many treatment applications involving small shallow targets, with small animal radio‐neuromodulation as an intriguing example. A focused kV technique based on novel usage of polycapillary x‐ray lenses can focus x‐ray beams to <0.2 mm in diameter, which is ideal for such uses. Purpose: Such an application also requires high resolution CT images for treatment planning and setup. In this work, we demonstrate the feasibility of using a virtual focal spot generated with an x‐ray lens to perform high‐resolution CBCT acquisition. Method: The experiment with x‐ray lens was set up on an x‐ray tabletop system to generate a virtual focal spot. The flood field images with and without the x‐ray lens were first compared. A pinhole image was acquired for the virtual focal spot and compared with the one acquired with the conventional focal spot without the lens. The planar imaging resolution with and without the lens were evaluated using a line pair resolution phantom. The spatial resolution of the two settings were estimated by reconstructing a 0.15‐mm wire phantom and comparing its full width half maximum (FWHM). A CBCT scan of a rodent head was also acquired to further demonstrate the improved resolution using the x‐ray lens. Result: The proposed imaging setup with x‐ray lens had a limited exposure area of 5 cm by 5 cm on the detector, which was suitable for guiding radio‐neuromodulation to a small target in rodent brain. Compared to conventional imaging acquisition with a measured x‐ray focal spot of 0.395 mm FWHM, the virtual focal spot size was measured at 0.175 mm. The reduction in focal spot size with lens leads to an almost doubled planar imaging resolution and a 26% enhancement in 3D spatial resolution. A realistic CBCT acquisition of a rodent head mimicked the imaging acquisition step for radio‐neuromodulation and further showed the improved visualization for fine structures. Conclusion: This work demonstrated that the focused kV x‐ray technique was capable of generating small focal spot size of <0.2 mm, which substantially improved x‐ray imaging resolution for small animal imaging. [ABSTRACT FROM AUTHOR]

Background: In x‐ray computed tomography (CT), the gain inconsistency of detector units leads to ring artifacts in the reconstructed images, seriously destroys the image structure, and is not conducive to image recognition. In addition, to reduce radiation dose and scanning time, especially photon counting CT, low‐dose CT is required, so it is important to reduce the noise and suppress ring artifacts in low‐dose CT images simultaneously. Purpose: Deep learning is an effective method to suppress ring artifacts, but there are still residual artifacts in corrected images. And the feature recognition ability of the network for ring artifacts decreases due to the effect of noise in the low‐dose CT images. In this paper, a method is proposed to achieve noise reduction and ring artifact removal simultaneously. Methods: To solve these problems, we propose a ring artifact correction method for low‐dose CT based on detector shifting and deep learning in this paper. Firstly, at the CT scanning stage, the detector horizontally shifts randomly at each projection to alleviate the ring artifacts as front processing. Thus, the ring artifacts are transformed into dispersed noise in front processed images. Secondly, deep learning is used for dispersed noise and statistical noise reduction. Results: Both simulation and real data experiments are conducted to evaluate the proposed method. Compared to other methods, the results show that the proposed method in this paper has better effect on removing ring artifacts in the low‐dose CT images. Specifically, the RMSEs and SSIMs of the two sets of simulated and experiment data are better compared to the raw images significantly. Conclusions: The method proposed in this paper combines detector shifting and deep learning to remove ring artifacts and statistical noise simultaneously. The results show that the proposed method is able to get better performance. [ABSTRACT FROM AUTHOR]

Background: In interventional radiology procedures, the operator typically stands on the right side of the patient's right thigh to manipulate devices through the femoral sheath. Because the standard x‐ray protective clothing is designed as sleeveless and scatter radiations from the patient are mainly incident from the left‐anterior direction to the operator, the arm hole of the clothing may be a significant unprotected area, contributing to an increase in the operator's organ doses and effective dose. Purpose: This study aimed to compare the organ doses and effective dose received by the interventional radiologist when wearing the standard x‐ray protective clothing and when wearing the modified clothing with an additional shoulder guard. Methods: The experimental setup aimed to simulate actual clinical practice in interventional radiology. The patient phantom was located at the beam center to generate scatter radiation. An adult female anthropomorphic phantom loaded with 126 nanoDots (Landauer Inc., Glenwood, IL) was used to measure organ and effective doses to the operator. The standard wrap‐around type x‐ray protective clothing offered 0.25‐mm lead‐equivalent protection, and the frontal overlap area offered 0.50‐mm lead‐equivalent protection. The shoulder guard was custom‐made with a material providing x‐ray protection equivalent to lead of 0.50 mm thickness. The organ and effective doses were compared between the operator wearing the standard protective clothing and the one wearing the modified clothing with a shoulder guard. Results: After adding the shoulder guard, doses to the lungs, bone marrow, and esophagus decreased by 81.9%, 58.6%, and 58.7%, respectively, and the effective dose to the operator decreased by 47.7%. Conclusions: Widespread use of modified x‐ray protective clothing with shoulder guards can significantly decrease the overall occupational radiation risk in interventional radiology. [ABSTRACT FROM AUTHOR]

Background: EBT4 was newly released for radiotherapy quality assurance to improve the signal‐to‐noise ratio in radiochromic film dosimetry. It is important to know its dose‐response characteristics before its use in the clinic. Purpose: This study aims to investigate and compare the dose‐response curves of the Gafchromic EBT4 film for megavoltage and kilovoltage x‐ray beams with different dose levels, scanning spatial resolutions, and sizes of region of interest (ROI). Methods: EBT4 film (Lot#07052201) calibration strips (3.5 × 20 cm2) were exposed to a 10×10 cm2 open field at doses of 0, 63, 125, 500, 750, 1000 cGy using 6 MV photon beam. EBT4 film strips from the same lot were then exposed to each x‐ray beam (6 MV, 6 MV FFF, 10 MV FFF, 15 MV, and 70 kV) at six dose values (50, 100, 300, 600, 800, 1000 cGy). A full sheet (25 × 20 cm2) of EBT4 film was irradiated at each energy with 300 cGy for profile comparison with the treatment planning calculation. At two different spatial resolutions of 72 and 300 dpi, each film piece was scanned three consecutive times in the center of an Epson 10000XL flatbed scanner in 48‐bit color. The scanned images were analyzed using FilmQA Pro. For each scanned image, an ROI of 2 × 2 cm2 at the field center was selected to obtain the average pixel value with its standard deviation in the ROI. An additional ROI of 1 cm diameter circle was also used to evaluate the impact of ROI shape and size, especially for FFF beams. The dose value, average dose‐response value, and associated uncertainty were determined for each energy and relative responses were analyzed. The Student's t‐test was performed to evaluate the statistical significance of the dose‐response values with different color channels, ROI shapes, and spatial resolutions. Results: The dose‐response curves for the five x‐ray energies were compared in three color channels. Weak energy dependence was found among the megavoltage beams. No significant differences (average ∼1.1%) were observed for all doses in this study among 6 MV, 6 MV FFF, 10 MV FFF, and 15 MV beams, regardless of spatial resolution and color channel. However, a statistically significant difference in dose‐response was observed up to 12% between 70 kV and 6 MV beams. Conclusions: The dose‐response curves for Gafchromic EBT4 films were nearly independent of the energy of the photon beams among 6 MV, 6 MV FFF, 10 MV FFF, and 15 MV. For very low‐energy photons (e.g., 70 kV), a separate calibration from the same low‐energy x‐ray is necessary. [ABSTRACT FROM AUTHOR]

Background: Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image‐based disease retrieval system will not only make full use of existing data, but also help physicians to prognosis the diseases. Medical image retrieval is represented by the classification and localization of common thorax diseases in x‐ray images. Although extensive efforts have been put into this field, there are still many challenges. Purpose: Most of the existing fine‐grained image research methods just apply existing deep learning frameworks in extracting the image features. However, these high‐level features mainly focus on the global representations of the object, rather than simultaneously considering the local ones. It requires fine‐grained details to classify the images with similar lesion areas. Thus, it is necessary to combine the global features and local ones to make the features more discriminative. On the other hand, training CNN models based on current existing strategies have a high time complexity, and is hard to get the discriminative features mentioned above. In addition, the visual retrieval method of fine‐grained medical images still has the problem of insufficient sample data with accurate annotation information. Methods: To address above challenges, we introduced a novel fine‐grained medical images retrieval method. First, a centralized contrastive loss (CCLoss) is proposed as our metric learning loss function. Parameters are updated by using the center point, which not only improves the distinguishing performance of features, but also effectively reduces the time complexity of the algorithm. In addition, a weakly supervised progressive feature extraction method is proposed to gradually extract the combined features. And the attention mechanism module is applied to screen the target information after the initial positioning for fine refinement, so as to separate the features with a high degree of discrimination. The retrieval of 14 different chest diseases is evaluated on the chest x‐ray datasets. Results: Compared with the existing research methods, the proposed method shows a better retrieval result for Recall@8 by 2.26%∼4.6%$\%{\sim }4.6\%$ and achieves a very efficient training speed which is 100 times faster than the pair‐wise loss‐based training strategy. We also assessed the effects of Recall@k (k = 2, 4, 6, 8) for progressive features extracted from different steps to obtain a model with the best retrieval performance. Conclusions: The proposed model is capable of learning discriminative representations from chest x‐ray datasets, and it achieves better performance compared with other state‐of‐the‐art methods. Therefore, the developed model would be useful in the diagnosis of common thorax disease or unknown chest disease. [ABSTRACT FROM AUTHOR]

Background: Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image‐guided radiotherapy (IGRT). Purpose: Our group has proposed that an automated image‐review algorithm be inserted into the IGRT process as an interlock to detect off‐by‐one vertebral body errors. This study presents the development and multi‐institutional validation of a convolutional neural network (CNN)‐based approach for such an algorithm using patient image data from a planar stereoscopic x‐ray IGRT system. Methods: X‐rays and digitally reconstructed radiographs (DRRs) were collected from 429 spine radiotherapy patients (1592 treatment fractions) treated at six institutions using a stereoscopic x‐ray image guidance system. Clinically‐applied, physician approved, alignments were used for true‐negative, "no‐error" cases. "Off‐by‐one vertebral body" errors were simulated by translating DRRs along the spinal column using a semi‐automated method. A leave‐one‐institution‐out approach was used to estimate model accuracy on data from unseen institutions as follows: All of the images from five of the institutions were used to train a CNN model from scratch using a fixed network architecture and hyper‐parameters. The size of this training set ranged from 5700 to 9372 images, depending on exactly which five institutions were contributing data. The training set was randomized and split using a 75/25 split into the final training/ validation sets. X‐ray/ DRR image pairs and the associated binary labels of "no‐error" or "shift" were used as the model input. Model accuracy was evaluated using images from the sixth institution, which were left out of the training phase entirely. This test set ranged from 180 to 3852 images, again depending on which institution had been left out of the training phase. The trained model was used to classify the images from the test set as either "no‐error" or "shifted", and the model predictions were compared to the ground truth labels to assess the model accuracy. This process was repeated until each institution's images had been used as the testing dataset. Results: When the six models were used to classify unseen image pairs from the institution left out during training, the resulting receiver operating characteristic area under the curve values ranged from 0.976 to 0.998. With the specificity fixed at 99%, the corresponding sensitivities ranged from 61.9% to 99.2% (mean: 77.6%). With the specificity fixed at 95%, sensitivities ranged from 85.5% to 99.8% (mean: 92.9%). Conclusion: This study demonstrated the CNN‐based vertebral body misalignment model is robust when applied to previously unseen test data from an outside institution, indicating that this proposed additional safeguard against misalignment is feasible. [ABSTRACT FROM AUTHOR]

Background: Radiographic X‐ray imaging is a common clinical examination. Current objective methods for quantifying image quality for radiographs struggle to capture the combined impact of factors throughout the imaging chain on the perceived image quality. Therefore, there is a need to further develop metrics that correlate with image quality as perceived by the observer. Objectives: We proposed the image feature index (IFI) to comprehensively quantify radiographic X‐ray image quality. We also aimed to study the correlation between IFI and observer‐perceived image quality for chest radiographs. Materials and methods: The IFI algorithm was developed, which measured the amount of information, textural features, and noise in the image. A total of 70 chest phantom radiographs were generated under 60–120 kV and 0.2–80 mAs. A vendor‐proprietary exposure index (EI) and dose area product (DAP) were extracted from the DICOM header in addition to calculating IFI for each image to investigate the relationships between IFI, EI and DAP. The quality of the images was rated by three observers, and the correlation between IFI and subjective score of image quality was tested. Next, a retrospective study using a random sample of 50 clinical chest radiographs was performed, and the correlation between IFI and subjective score was tested. The correlation was determined by the Spearman test. Results: The curves of IFI versus DAP and IFI versus EI both demonstrated a similar three‐stage form where IFI is above zero: in the first stage, IFI increases rapidly with increased DAP or EI, whereas in the second stage, the slope of the curves decreased towards an asymptote, that is, minimal gain in IFI with increased DAP or EI—until they hit the inflection point and then descended sharply in the third stage. For both phantom and clinical chest images, IFI demonstrated good correlation with subjective score (r = 0.9084 for phantom images, r = 0.8153 for clinical images). Conclusions: IFI is a feasible and efficient descriptor for image quality for chest radiographs. Future studies with larger sample sizes and sample types are needed to confirm the feasibility of IFI for other exam types and anatomical views, thus fulfilling and extending the potential applications of IFI in quality control and radiation dose reduction. [ABSTRACT FROM AUTHOR]

Background: Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality,especially for multinucleon ions that can fragment,such as helium. Purpose: This work proposes a particle imaging filter, referred to as the Prior Filter,based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. Methods: The Prior Filter uses Fermi–Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3𝜎 filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories.Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of -the-art method including an implementation of the 3𝜎 filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. Results: The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5–6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3𝜎 filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of -the-art filtering method. Conclusions: The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction. [ABSTRACT FROM AUTHOR]

Background: Chest X-ray (CXR) images are commonly used to show the internal structure of the human body without invasive intervention. The quality of CXR is an important factor as it affects the accuracy of a clinical diagnosis. Unfortunately, it is difficult to always get good quality CXR scans due to noises and scatters. Purpose: Recently, wavelet directional CycleGAN (WavCycleGAN) has shown promising results in image restoration tasks by removing noise and artifacts without sacrificing high-frequency components of the input image. Unfortunately, WavCycleGAN directly reconstructs wavelet directional images that require a wavelet transform in both the training and test phases, resulting in additional processing steps and unnatural artifacts originating from the wavelet domain image.In addition,WavCycleGAN can only process artifact-related subbands, so it is difficult to apply WavCycleGAN when different levels of artifacts are present in all subbands. To address this, here we present a novel unsupervised CXR image restoration scheme with similar or even better artifact removal performance than WavCycleGAN in spite of wavelet transform being only applied in the training phase. Methods: We introduce a novel wavelet subband discriminator which can be combined with CycleGAN or switchable CycleGAN, where wavelet transform is applied only in the training phase for discriminators to match the distribution of wavelet subband components. In our framework, the image restoration network can be still applied in the image domain to prevent unnatural artifacts of the wavelet domain image with the help of the image-domain cycle-consistency loss. In addition, using wavelet subband discriminator makes it possible to remove artifacts in all subbands by utilizing frequency-specific wavelet subband discriminators. Results: Through extensive experiments for noise and scatter removal in CXRs, we confirm that our method provides competitive performance compared to existing approaches without additional processing steps in the test phase. Furthermore, we show that our wavelet subband discriminator combined with the switchable CycleGAN can provide the flexibility by generating different levels of artifact removal. Conclusions: The proposed wavelet subband discriminator can be combined with the existing CycleGAN or switchable CycleGAN structures to construct an efficient unsupervised CXR image reconstruction. The advantage of our wavelet subband discriminator-based CXR image restoration is that, unlike traditional WavCycleGAN, it does not require any additional processing steps in the testing phase and does not generate unnatural artifacts originating from the wavelet domain image. We believe that our wavelet subband discriminator can be applied to various CXR image applications. [ABSTRACT FROM AUTHOR]

PurposeThe aim of this study is to investigate the feasibility of x-ray-induced acoustic computed tomography (XACT) as an image guidance tool for tracking x-ray beam location and monitoring radiation dose delivered to the patient during stereotactic partial breast irradiation (SPBI).MethodsAn in-house simulation workflow was developed to assess the ability of XACT to act as an in vivo dosimetry tool for SPBI. To evaluate this simulation workflow, a three-dimensional (3D) digital breast phantom was created by a series of two-dimensional (2D) breast CT slices from a patient. Three different tissue types (skin, adipose tissue, and glandular tissue) were segmented and the postlumpectomy seroma was simulated inside the digital breast phantom. A treatment plan was made with three beam angles to deliver radiation dose to the seroma in breast to simulate SPBI. The three beam angles for 2D simulations were 17°, 90° and 159° (couch angles were 0 degrees) while the angles were 90 degrees (couch angles were 0°, 27°, 90°) in 3D simulation. A multi-step simulation platform capable of modelling XACT was developed. First, the dose distribution was converted to an initial pressure distribution. The propagation of this pressure disturbance in the form of induced acoustic waves was then modeled using the k-wave MATLAB toolbox. The waves were then detected by a hemispherical-shaped ultrasound transducer array (6320 transducer locations distributed on the surface of the breast). Finally, the time-varying pressure signals detected at each transducer location were used to reconstruct an image of the initial pressure distribution using a 3D time-reversal reconstruction algorithm. Finally, the reconstructed XACT images of the radiation beams were overlaid onto the structure breast CT.ResultsIt was found that XACT was able to reconstruct the dose distribution of SPBI in 3D. In the reconstructed 3D volumetric dose distribution, the average doses in the GTV (Gross Target Volume) and PTV (Planning Target Volume) were 86.15% and 80.89%, respectively. When compared to the treatment plan, the XACT reconstructed dose distribution in the GTV and PTV had a RMSE (root mean square error) of 2.408 % and 2.299 % over all pixels. The 3D breast XACT imaging reconstruction with time-reversal reconstruction algorithm can be finished within several minutes.ConclusionsThis work explores the feasibility of using the novel imaging modality of XACT as an in vivo dosimeter for SPBI radiotherapy. It shows that XACT imaging can provide the x-ray beam location and dose information in deep tissue during the treatment in real time in 3D. This study lays the groundwork for a variety of future studies related to the use of XACT as a dosimeter at different cancer sites.

Background: Assessing the performance of spectroscopic x-ray detectors (SXDs) requires measurement of the frequency-dependent detective quantum efficiency (DQE). Analytical expressions of the task-based DQE and task-independent DQE of SXDs have been presented in the literature, but standardizable experimental methods for measuring them have not. The task-based DQE quantifies the efficiency with which an SXD uses the x-ray quanta incident upon it to either quantify or detect a basis material (e.g., soft tissue or bone) of interest. The task-independent DQE is akin to the conventional DQE in that it is independent of the basis material to be detected or quantified. Purpose: The purpose of this paper is to develop an experimental framework to present a method for experimental analysis of the DQE of SXDs, including the task-based DQE and task-independent DQE. Methods: We develop methods to measure the frequency-dependent DQE for task of quantifying or detecting a perturbation in a known basis material. We also develop methods for measuring a task-independent DQE. We show that the task-based DQEs and the task-independent DQE can be measured using a modest extension of the methods prescribed by International Electrotechnical Commission (IEC). Specifically, measuring the task-independent DQE requires measuring the modulation transfer function (MTF) and noise power spectrum (NPS) of each energy-bin image, in addition to the cross NPS between energy-bin images. Measuring the task-based DQEs requires an additional measurement of the transmission fraction through a thin basis-material absorber. We implemented the developed methods using standardized IEC x-ray spectra, aluminum (Al) and polymethyl methacrylyte (PMMA) basis materials, and a cadmium telluride (CdTe) SXD equipped with two energy bins and analog charge summing (ACS) for charge-sharing suppression. We also performed a regression analysis to determine whether or not the task-independent DQE is predictive of the task-based DQEs. Results: Experimental results of the task-based DQEs were consistent with simulation results presented in the literature. In general, and as expected, ACS increased the task-based DQEs and task-independent DQE. This effect was most pronounced for quantification tasks, in some instances yielding a five-fold increase in the DQE. For both spectra, with and without ACS for charge sharing correction, the task-based DQEs were linearly related to the task-independent DQE, as demonstrated by R2-values ranging from 0.89 to 1.00. Conclusions: We have extended experimental DQE analysis to SXDs that count photons in multiple energy bins in a single x-ray exposure. The developed framework is an extension of existing IEC methods,and provides a standardized approach to assessing the performance of SXDs. [ABSTRACT FROM AUTHOR]

Background: Treatment of locally advanced pancreatic cancer (LAPC) has long been calling for advances in technology of radiotherapy. Patients who received radiotherapy still had high risks of local recurrence, while suffering from gastrointestinal side effects. Based on the inherent characteristics of the x-ray and γ-Ray radiation techniques, here we proposed and investigated an unexplored radiation therapy. Purpose: To investigate the potential clinical benefit of a novel x-ray and γ-Ray combination radiation technique in patients with LAPC. Methods: Retrospective intensity-modulated radiotherapy (IMRT) treatment plans of 10 LAPC patients were randomly selected to compare with dual-modality plans. The prescribed dose to PGTV was 60.2 Gy. The PGTV dose was further escalated in dual-modality plan while maintaining clinically tolerable dose to organs at risk (OARs). Dosimetric comparisons were made and analyzed for three treatment plans (tomotherapy, standard dual-modality plan, escalated dual-modality plan) to assess the ability to increase dose to target volume while minimizing dose in adjacent OARs. Finally, radiobiological models were utilized for comparison. Results: All strategies resulted in dosimetrically acceptable plans. Dual-modality plans were present with similar conformity index (CI) and significantly lower gradient index (GI) compared with tomotherapy (3.64 ± 0.37 vs. 4.14 ± 0.61, p = 0.002; 3.64 ± 0.42 vs. 4.14 ± 0.61, p = 0.003). Dmean of PGTV (65.46 ± 3.13 vs. 61.56 ± 1.00, p = 0.009; 77.98 ± 5.86 vs. 61.56 ± 1.00, p < 0.001) and PCTV (55.04 ± 2.14 vs. 53.93 ± 1.67, p = 0.016; 58.24 ± 3.24 vs. 53.93 ± 1.67, p = 0.001) were significantly higher, while Dmean of the stomach was reduced in both dual-modality plans (17.98 ± 10.23 vs. 19.34 ± 9.75, p = 0.024; 17.62 ± 9.92 vs. 19.34 ± 9.75, p = 0.040). The lower V30Gy in the liver (4.83 ± 5.87 vs. 6.23 ± 6.68, p = 0.015; 4.90 ± 5.93 vs. 6.23 ± 6.68, p = 0.016) and lower V45Gy of the small intestine (3.35 ± 3.30 vs. 4.06 ± 3.87, p = 0.052) were found in dual-modality plans. Meanwhile, radiobiological models demonstrated higher probability of tumor control (29.27% ± 9.61% vs. 18.34% ± 4.70%, p < 0.001; 44.67% ± 18.16% vs. 18.34% ± 4.70%, p = 0.001) and lower probability of small intestine complication (2.16% ± 2.30% vs. 1.25% ± 2.72%, p = 0.048) in favor of dual-modality strategy. Conclusions: A novel dual-modality strategy of x-ray and γ-Ray combination radiation appears reliable for target dose escalation and normal tissue dose reduction. This strategy might be beneficial for local tumor control and the protection of normal organs in patients with LAPC. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, the FLASH effect, in which ultrahigh dose rate (UHDR) radiotherapy (RT) can significantly reduce toxicity to normal tissue while maintaining antitumor efficacy, has been verified in many studies and even applied in human clinical cases. This work evaluates whether a room-temperature radio-frequency (RF) linear accelerator (linac) system can produce UHDR high-energy X-rays exceeding a dose rate of 40 Gy/s at a clinical source-surface distance (SSD), exploring the possibility of a compact and economical clinical FLASH RT machine suitable for most hospital treatment rooms. Methods: A 1.65 m long S-band backward-traveling-wave (BTW) electron linac was developed to generate high-current electron beams, supplied by a commercial klystron-based power source. A tungsten-copper electron-to-photon conversion target for UHDR X-rays was designed and optimized with Monte Carlo (MC) simulations using Geant4 and thermal finite element analysis (FEA) simulations using ANSYS. EBT3 and EBT-XD radiochromic films, which were calibrated with a clinical machine Varian VitalBeam,were used for absolute dose measurements. A PTW ionization chamber detector was used to measure the relative total dose and a plane-parallel ionization chamber detector was used to measure the relative normalized dose of each pulse. Results: The BTW linac generated 300-mA-pulse-current 11 MeV electron beams with 29 kW mean beam power, and the conversion target could sustain this high beam power within a maximum irradiation duration of 0.75 s.The mean energy of the produced X-rays was 1.66 MeV in the MC simulation. The measured flat-filter-free (FFF) maximum mean dose rate of the room-temperature linac exceeded 80 Gy/s at an SSD of 50 cm and 45 Gy/s at an SSD of 67.9 cm, both at a 2.1 cm depth of the water phantom. The FFF radiation fields at 50 cm and 67.9 cmSSD at a 2.1 cm depth of the water phantom showed Gaussian-like distributions with 14.3 and 20 cm full-width at half -maximum (FWHM) values, respectively. Conclusion: This work demonstrated the feasibility of UHDR X-rays produced by a room-temperature RF linac, and explored the further optimization of system stability. It shows that a simple and compact UHDR X-ray solution can be facilitated for both FLASH-RT scientific research and clinical applications. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work was to determine the water-equivalent thickness of Superflab bolus material for narrow and broad field-of-view (FOV) x-ray geometries at diagnostic x-ray energies. Methods: Transmission measurements were performed for incremental thicknesses of Superflab bolus material and water in narrow and broad FOV x-ray geometries. The transmission data was fit to a non-linear model for x-ray transmission - the Archer model. Water-equivalent thickness of Superflab was calculated based upon fitting parameters to transmission curves for 75, 95, and 115 kV x-ray tube voltages. Measured x-ray transmission factors for water and Superflab were used to determine the water equivalence of Superflab. Results: For all x-ray tube voltages and geometries, the water equivalence of Superflab was greater than one, indicating that Superflab is more attenuating than water. This effect was stronger for broad FOV geometries. At 95 kV, 30 cm of Superflab corresponded to 32.0 cm of water in the narrow FOV geometry, and 34.3 cm of water in the broad FOV geometry. The Archer model fitting parameters and Superflab water equivalence are reported for all x-ray beam conditions explored in this work. Conclusions: Superflab bolus material is more attenuating than water at diagnostic x-ray energies. The Archer model and its respective fitting parameters reported in this work may be used to estimate the water-equivalent thickness of Superflab for diagnostic x-ray spectra. [ABSTRACT FROM AUTHOR]

Background: EOSedge™* (EOS Imaging, Paris, France) is an X-ray imaging system using automatic exposure control (AEC) with tube current modulation, in order to optimize dose deposition in patients. Purpose: This study aims at characterizing EOSedge organ dose deposition in comparison to a digital radiography (DR) system and the previous EOS system (EOS-1st generation), in relation to their respective image quality levels. Method: Organ doses were measured in an anthropomorphic female adult phantom and a 5-year-old pediatric phantom using optically stimulated luminescence (OSL) dosimeters, which were carefully calibrated within the studied energy range. Organ doses were recorded on the EOSedge and the Fuji Visionary DRF (Fujifilm Medical Systems U.S.A., Inc, Lexington, MA). The resulting effective doses were compared to the EOS-1st-generation values present in the literature. Image quality assessment was carried out on end-user images. Quantitative image quality metrics were computed for all tested modalities on a quality assurance phantom. Qualitative assessment of EOSedge image quality was based on anthropomorphic phantom acquisitions against the EOS-1st-generation system, and on clinical images against the tested DR system. Results: For a full-spine exam, and on the female adult phantom (respectively, the pediatric phantom), an effective dose of 92 Sv (respectively, 32 Sv) was obtained on EOSedge, and 572 Sv (respectively, 179 Sv) on the DR system; these values were compared to effective dose values of 290 Sv (respectively, 200 Sv) from the literature on EOS-1st generation, leading to an effective dose reduction factor of 6 with respect to the DR system, and of 3-6 with respect to EOS-1st generation. EOSedge provides the best compromise between contrast-to-noise ratio (CNR) and dose, with more consistent CNR values than the other tested modalities, in a range of attenuation from 10 to 40 cm of poly(methyl methacrylate) (PMMA). Within this range, EOSedge is also comparable to DR for 10 and 20 cm of PMMA, and better than DR for 30 and 40 cm of PMMA, both in terms of spatial resolution and low-contrast detection. The anatomical landmarks of interest in the follow-up of spinal deformities can be detected in all tested modalities. Conclusion: Results showed that EOSedge provides significant dose reduction factors for full spine imaging in both adults and children compared to the other tested modalities, without compromising image quality. We believe that this work could help raise awareness on the capabilities of modern X-ray systems, when equipped with appropriate AEC strategies, to perform ultra-low-dose, long-axis images. [ABSTRACT FROM AUTHOR]

Background: Photon-counting x-ray detectors may enable single-exposure dual-energy (DE) x-ray angiography. Purpose: The purpose of this paper is to experimentally optimize the energy thresholds and tube voltage for single-exposure DE x-ray angiography. Methods: We optimized single-exposure DE x-ray angiography using the iodine signal-difference-to-noise ratio (SDNR) per root patient air kerma (1) as a figure of merit. We measured the iodine SDNR by imaging an iodine stepwedge immersed in a water tank with a depth of 30 cmin the direction of x-ray propagation. The stepwedge was imaged using tube voltages ranging from 90 to 150 kV and a cadmium telluride (CdTe) x-ray detector with two energy bins and analog charge summing for charge sharing suppression. The energy threshold that separates the two energy bins was varied from approximately 35 keV to approximately 75% of the maximum energy of the x-ray beam. Curve fitting was used to determine the threshold that maximized SDNR/v 1. The effect of scatter was determined from measurements of the scatter-to-primary ratios (SPRs) of the low-energy and high-energy images and a semi-empirical model of the relationship between SDNR and SPR. Using the optimal parameters, we imaged a phantom with vessel-simulating structures and background clutter. Results: The optimal energy thresholds increased monotonically from ~50 to ~85 keV over the range of tube voltages considered. For tube voltages greater than 90 kV, the optimal energy thresholds consistently allocated approximately two thirds of all detected primary photons to the low energy bin; this ratio was preserved without scatter. Consistent with prior modeling studies, SDNR/v increased monotonically with tube voltage from 90 to 150 kV; SDNR/v at 150 kV was approximately 38% higher than that at 90 kV for an iodine area density of ~50 mg/cm2.Scatter reduced SDNR by approximately 25% for SPRs of ~1 and 0.4 in low-energy and high-energy images, respectively. Conclusions: Achieving optimal image quality in single-exposure DE angiography with photon-counting x-ray detectors will require high tube voltages (i.e., >130 kV) and, for thick patients, energy thresholds that allocate approximately two thirds of all primary photons to the low-energy image. Future work will compare the image quality of singe-exposure photon-counting and kV-switching approaches to DE x-ray angiography. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a novel methodology for developing anthropomorphic breast phantoms for use in X-ray-based imaging modalities. Methods: "Hyperization" is a quasi-stippling mapping operation in which regions of varying grayscale values in a 2D image are transformed into regions of varying holes on a surface. The holes can be cut or engraved on the sheet of paper using a high-resolution laser cutter/engraver. In hyperization, the main parameters are the size and the distance between the holes. Here, we introduce the concept and chronicle the development and characterization of a proof-of-concept prototype. In this study, we hypothesized that a resulting "Hyperia" phantom would be a realistic representative of a patient's breast tissue: it would exhibit similar X-ray properties and show textural complexities. We used breast computed tomography (bCT) images of real patients as the input models. Using a previously developed segmentation method, the input CT images were segmented into different tissue classes (skin, adipose, and fibroglandular). The segmented images were then "Hyperized".A series of Monte Carlo simulations were conducted to find the optimal hyperization parameters. Different laser cutter/engraver systems and substrate materials were explored to find a viable option for developing an entire Hyperia breast phantom. The resulting phantom was imaged on a prototype breast CT system, and the resulting images were evaluated based on physical properties and similarity to the original patient data. Results: The simulation results indicate close similarities - both in the distribution of different tissue types and the resulting CT numbers - between the patient bCT image and the bCT of the Hyperia phantom, regardless of the breast size and density: the Pearson correlation coefficient (1) ranged from 0.88 in a BIRADS A breast to 0.94 in BIRADS C and D breasts (1 of 1.00 suggests perfect structural similarity), and the volumetric mean squared error ranged from 0.0033 (in BIRADS D breast) to 0.0059 (in BIRADS A), suggesting good agreement between the resulting CT numbers. For fabricating the slices, the office paper was found to be an optimal substrate material, with the Hyperization parameters of (a, ß) = (0.200 mm, 0.400 mm). Conclusion:A novel phantom can be used for X-ray-based breast cancer imaging systems. The main advantage is that only one material is used for creating a contrast between different tissue types in an image. [ABSTRACT FROM AUTHOR]

Background: Metal artifacts have been an outstanding issue in computed tomography (CT) since its first uses in the clinic and continue to interfere. Metal artifact reduction (MAR) methods continue to be proposed and photon‐counting detectors (PCDs) have recently been the subject of research toward this purpose. PCDs offer the ability to distinguish the energy of incident x‐rays and sort them in a set number of energy bins. High‐energy data captured using PCDs have been shown to reduce metal artifacts in reconstructions due to reduced beam hardening. Purpose: High‐energy reconstructions using PCD‐CT have their drawbacks, such as reduced image contrast and increased noise. Here, we demonstrate a MAR algorithm, trace replacement MAR (TRMAR), in which the data corrupted by metal artifacts in full energy spectrum projections are corrected using the high‐energy data captured during the same scan. The resulting reconstructions offer similar MAR to that seen in high‐energy reconstructions, but with improved image quality. Methods: Experimental data were collected using a bench‐top PCD‐CT system with a cadmium zinc telluride PCD. Simulations were performed to determine the optimal high‐energy threshold and to test TRMAR in simulations using the XCAT phantom and a biological sample. For experiments a 100‐mm diameter cylindrical phantom containing vials of water, two screws, various densities of Ca(ClO4)2, and a spatial resolution phantom was imaged with and without the screws. The screws were segmented in the initial reconstruction and forward projected to identify them in the sinogram space in order to perform TRMAR. The resulting reconstructions were compared to the control and to reconstructions corrected using normalized metal artifact reduction (NMAR). Additionally, a beef short rib was imaged with and without metal to provide a more realistic phantom. Results: XCAT simulations showed a reduction in the streak artifact from −978 HU in uncorrected images to −10 HU with TRMAR. The magnitude of the metal artifact in uncorrected images of the 100‐mm phantom was −442 HU, compared to the desired −81 HU with no metal. TRMAR reduced the magnitude of the artifact to −142 HU, with NMAR reducing the magnitude to −96 HU. Relative image noise was reduced from 176% in the high‐energy image to 56% using TRMAR. Density quantification was better with NMAR, with the Ca(ClO4)2 vial affected most by metal artifacts showing 0.8% error compared to 2.1% with TRMAR. Small features were preserved to a greater extent with TRMAR, with the limiting spatial frequency at 20% of the MTF fully maintained at 1.31 lp/mm, while with NMAR it was reduced to 1.22 lp/mm. Images of the beef short rib showed better delineation of the shape of the metal using TRMAR. Conclusions: NMAR offers slightly better performance compared to TRMAR in streak reduction and image quality metrics. However, TRMAR is less susceptible to metal segmentation errors and can closely approximate the reduction in the streak metal artifact seen in NMAR at 1/3 the computation time. With the recent introduction of PCD‐CT into the clinic, TRMAR offers notable potential for fast, effective MAR. [ABSTRACT FROM AUTHOR]

Purpose: To describe the scattered radiation spectra inside an x‐ray room for different scattering conditions. Methods: Monte Carlo simulations of an x‐ray room using phantoms of different size, varying field sizes, and a range of mono‐energetic beams were carried out. For each energy, the particle fluence spectrum of scattered photons was collected at different spherical zones to describe the radiation reaching the different boundaries of the x‐ray room. The effect on the scattered spectrum of the room floor was also considered. Results: The scattered spectra for mono‐energetic primary beams at a given spherical zone give rise to oriented energy spread kernels (OESKs) that can be used to calculate the scattered spectrum for any poly‐energetic beam at that zone. Despite the large differences, which can be seen in the OESKs when the scattering conditions vary, an important invariance is also observed: the position of the broad scatter peak for a given primary energy and zone. Conclusions: The result of breaking down the calculation of the scattered radiation spectrum into the different factors that influence it allows estimating the spectrum in a wide range of situations. The invariant position of the broad scatter peak can be used to estimate the highest energy of the scattered photons for a given primary energy and zone, which may determine radiation shielding needs. [ABSTRACT FROM AUTHOR]

Background: All photon counting detectors have a characteristic count rate over which their performance degrades. Degradation in the clinical setting takes the form of increased noise, reduced material quantification accuracy, and image artifacts. Count rate is a function of patient attenuation, beam filtration, scanner geometry, and X‐ray technique. Purpose: To guide protocol and technology development in the photon counting space, knowledge of clinical count rates spanning the complete range of clinical indications and patient sizes is needed. In this paper, we use clinical data to characterize the range of computed tomography (CT) count rates. Methods: We retrospectively gathered 1980 patient exams spanning the entire body (head/neck/chest/abdomen/extremity) and sampled 36 951 axial image slices. We assigned the tissue labels air/lung/fat/soft tissue/bone to each voxel for each slice using CT number thresholds. We then modeled four different bowtie filters, 70/80/100/120/140 kV spectra, and a range of mA values. We forward‐projected each slice to obtain detector‐incident count rates, using the geometry of a GE Revolution Apex scanner. Our analysis divided the detector into thirds: the central one‐third, one‐third of the detector split into two equal regions adjacent to the central third, and the final one‐third divided equally between the outer detector edges. We report the 99th percentile of counts to mimic the upper limits of count rates making passing through a patient as a function of patient water equivalent diameter. We also report the percentage of patient scans, by body region, over different count rate thresholds for all combinations of bowtie and beam energy. Results: For routine exam types, we recorded count rates of approximately 3.5 × 108 counts/mm2/s in the torso, extremities, and brain. For neck scans, we observed count rates near 6 × 108 counts/mm2/s. Our simulations of 1000 mA, appropriately mimicking the mA needs for fast pediatric, fast thoracic, and cardiac scanning, resulted in count rates of over 10 × 108 counts/mm2/s for the torso, extremities, and brain. At 1000 mA, for the neck region, we observed count rates close to 2 × 109 counts/mm2/s. Importantly, we saw only a small change in maximum count rate needs over patient size, which we attribute to patient mis‐positioning with respect to the bowtie filters. As expected, combinations of kV and bowtie filter with higher beam energies and wider/less attenuating bowtie fluence profiles lead to higher count rates relative to lower energies. The 99th–50th percentile count rate changed the most for the torso region, with a maximum variation of 3.9 × 108 to 1.2 × 107 counts/mm2/s. The head/neck/extremity regions had less than a 50% change in count rate from the 99th to 50th percentiles. Conclusions: Our results are the first to use a large patient cohort spanning all body regions to characterize count rates in CT. Our results should be useful in helping researchers understand count rates as a function of body region and mA for various combinations of bowtie filter designs and beam energies. Our results indicate clinical rates >1 × 109 counts/mm2/s, but they do not predict the image quality impact of using a detector with lower characteristic count rates. [ABSTRACT FROM AUTHOR]