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PurposePhoton counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition.ApproachThe effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis.ResultsFor single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by ∼10% . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching.ConclusionsThe performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition.

Coronary artery calcification is a significant predictor of cardiovascular disease, with current detection methods like Agatston scoring having limitations in sensitivity. This study aimed to evaluate the effectiveness of a novel CAC quantification method using dual-energy material decomposition, particularly its ability to detect low-density calcium and microcalcifications. A simulation study was conducted comparing the dual-energy material decomposition technique against the established Agatston scoring method and the newer volume fraction calcium mass technique. Detection accuracy and calcium mass measurement were the primary evaluation metrics. The dual-energy material decomposition technique demonstrated fewer false negatives than both Agatston scoring and volume fraction calcium mass, indicating higher sensitivity. In low-density phantom measurements, material decomposition resulted in only 7.41% false-negative (CAC = 0) measurements compared to 83.95% for Agatston scoring. For high-density phantoms, false negatives were removed (0.0%) compared to 20.99% in Agatston scoring. The dual-energy material decomposition technique presents a more sensitive and reliable method for CAC quantification.

Background: Photon‐counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. Purpose: This study proposes a lossy method to compress photon‐counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. Methods: The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon‐count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M < N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy‐bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel‐specific and pixel‐general. The pixel‐specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel‐general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20 cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon‐based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8‐bin data, after which material decomposition was performed. The mean, standard deviation, and contrast‐to‐noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8‐bin data and for the eigenbin data. Results: The pixel‐specific eigenbin method reduced photon‐counting CT data size by a factor of four with <5% change in mean values and a small noise penalty (mean change in noise of <12%, maximum change in noise of 20% for basis images). The pixel‐general eigenbin compression method reduced data size by a factor of 2.67 with <5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty ≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in <5% change in CNR in the VMIs. Conclusion: The eigenbin compression method reduced photon‐counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%–20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon‐counting CT data for applications that require rapid initial reconstructions. [ABSTRACT FROM AUTHOR]

Background: Current photon‐counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x‐ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost‐effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain. Objective: This study aims to establish a comprehensive quantitative imaging framework for three‐energy‐bin proof‐of‐concept photon‐counting CT based on YSO/SiPM detectors developed in our group using multi‐voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification. Methods: We developed a proof‐of‐concept YSO/SiPM‐based benchtop spectral CT system and established a pipeline for three‐energy‐bin photon‐counting CT projection‐domain processing. The empirical A‐table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A‐table methods for material identification in three‐energy‐bin spectral CT was confirmed through both simulations and experimental studies. Results: In both noise‐free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%. Conclusions: Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A‐table method. Quantitative imaging results indicate that the YSO/SiPM‐based photon‐counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification. [ABSTRACT FROM AUTHOR]

To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with >10 years and <2 years of experience in musculoskeletal imaging. Both techniques were evaluated in terms of sensitivity (SE), specificity (SP), positive and negative predictive values (PPVs and NPVs) and accuracy for fracture detection, as confirmed at a 3-month clinical–instrumental follow-up. Inter-observer agreement and examination times were also analyzed. Fractures were confirmed in 18/40 cases. The highest values of diagnostic performance for VNHAP images were obtained in terms of SP (90.9% and 95%) and PPV (87.5% and 92.8%) and for the less experienced operator. No statistically significant differences were observed between the diagnostic accuracy of the two readers in the evaluation of VNHAP images. Inter-observer agreement was moderate (κ = 0.536) for BCT and substantial (κ = 0.680) for VNHAP. Comparing the two operators, a significantly longer examination time for BCT and no significant difference for VNHAP were registered. Our preliminary experience may encourage the employment of VNHAP maps in combination with BCT images in emergency settings. Their use could be time-saving and valuable in terms of diagnostic performance, especially for less experienced operators. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast‐to‐noise ratio (CNR) through optimal weighting of their spectral measurements.This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy‐weighting strategies: pre‐log weighting, post‐log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy‐weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques.We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low‐contrast tasks using three energy‐weighting strategies: pre‐log weighting (weighted sum of energy bin counts), post‐log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection‐ and image‐domain CNR performance of these energy‐weighting strategies. Additionally, the total counts method (a special case of pre‐log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge‐on‐irradiated silicon PCCT prototype to evaluate the image‐domain CNR performance of these energy‐weighting strategies.The results show that pre‐log, post‐log, and MD weighting strategies generate approximately equal projection‐domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy‐weighting strategies also generate approximately equal image‐domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre‐log weighting generates the highest image‐domain CNR for an off‐center contrast task location or inhomogeneous phantoms while also outperforming the total counts method.We derived the expression of projection‐domain maximum achievable CNR using three energy‐weighting strategies. Our results suggest that using pre‐log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off‐center region of interests (ROIs). [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Multi‐organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector‐based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentation performance.We propose UNet‐based multi‐organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral information effectively.The proposed method consists of the following steps: Noise reduction in bin‐wise images, image‐based material decomposition, generating VMIs, and deep learning‐based segmentation. VMIs are synthesized for various x‐ray energies using basis images. The UNet‐based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual‐ and quad‐energy bins. We compared it to the conventional PCCT‐based segmentation, which uses only the (noise‐reduced) bin‐wise images. The experiments were conducted on two cases by adjusting the dose levels.The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin‐wise images from dual‐energy bins; 3D UNet).The proposed method using VMIs improves training stability for multi‐organ segmentation in PCCT, particularly when the number of energy bins is small. [ABSTRACT FROM AUTHOR]

Multi‐energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single‐energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user‐selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task‐specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single‐energy CT, the substantial differences between single‐energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications. [ABSTRACT FROM AUTHOR]

Background: Dual‐energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal‐to‐noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning‐based decomposition methods have been reported, these methods are in the supervised‐learning framework requiring paired data for training, which is not readily available in clinical settings. Purpose: This work aims to develop an unsupervised‐learning framework with data‐measurement consistency for image‐domain material decomposition in DECT. Methods: The proposed framework combines iterative decomposition and deep learning‐based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data‐fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low‐noise material‐specific images from the high‐noise images. In this scheme, paired images of DECT and ground‐truth material‐specific images are not required for the model training. Once trained, the generator can perform image‐domain material decomposition with noise suppression in a single step. Results: In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. Conclusions: Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised‐learning framework, which does not require paired data for model training and resolves the issue of lack of ground‐truth data in clinical scenarios. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample's geometry and material composition. Accurately mapping these coefficients to specific materials is a complex task. Traditionally, material decomposition in CT relied on classical algorithms using handcrafted features based on X-ray physics. However, there is a rising trend towards data-driven approaches, particularly deep learning, which offer promising improvements in accuracy and efficiency. This survey explores the transition from classical to data-driven approaches in material-sensitive CT, examining a comprehensive corpus of literature identified through a detailed and reproducible search using Scopus. Our analysis addresses several key research questions: the origin and generation of training datasets, the models and architectures employed, the extent to which deep learning methods reduce the need for domain-specific expertise, and the hardware requirements for training these models. We explore the implications of these findings on the integration of deep learning into CT practices and the potential reduction in the necessity for extensive domain knowledge. In conclusion, this survey highlights a significant shift towards deep learning in material-resolving CT and discusses the challenges and opportunities this presents. The transition suggests a future where data-driven approaches may dominate, offering enhanced precision and robustness in material-resolving CT while potentially transforming the role of domain experts in the field. [ABSTRACT FROM AUTHOR]

With the development of photon counting detectors (PCDs), spectral CT achieves higher performance than in the past, thus has better application prospects. In this paper, an adaptive dual effect decomposition (ADED) algorithm is proposed to perform the quantitative estimation of effective atomic number and electron density. The algorithm is an empirical material identification algorithm that achieves calibration by combining polynomials on the projection data and constraining in the image domain. In addition, we also propose an innovative effective atomic number estimation model with an accurate physical model that adaptively adjusts the parameters according to the x‐ray energy and material type. The performance of the algorithm was verified by experiment and simulation in this paper. Compared to the conventional basis material decomposition algorithm, the proposed ADED algorithm reduces the relative error in the quantification of the effective atomic number from 4.5%–12.0% to 0.3%–4.5%. Overall, the results demonstrate that the proposed algorithm significantly improves the accuracy of quantifying the effective atomic number. [ABSTRACT FROM AUTHOR]

Purpose. Fast kV-switching (FKS) and dual-layer flat-panel detector (DL-FPD) technologies have been actively studied as promising dual-energy spectral imaging solutions for FPD-based cone-beam computed tomography (CT). However, cone-beam CT (CBCT) spectral imaging is known to face challenges in obtaining accurate and robust material discrimination performance. That is because the energy separation by either FKS or DL-FPD, alone, is still limited, along with apparently unpaired signal levels in the effective low- and high-energy projections in real applications, not to mention the x-ray scatter in cone-beam scan which will make the material decomposition almost impossible if no correction is applied. To further improve CBCT spectral imaging capability, this work aims to promote a source-detector joint multi-energy spectral imaging solution which takes advantages of both FKS and DL-FPD, and to conduct a feasibility study on the first tabletop CBCT system with the joint spectral imaging capability developed. Methods. For CBCT, development of multi-energy spectral imaging can be jointly realized by using an x-ray source with a generator whose kilo-voltages can alternate in tens of Hertz (i.e. FKS), and a DL-FPD whose top- and bottom-layer projections corresponds to different effective energy levels. Thanks to the complimentary characteristics inherent in FKS and DL-FPD, the overall energy separation will be significantly better when compared with FKS or DL-FPD alone, and the x-ray photon detection efficiency will be also improved when compared with FKS alone. In this work, a noise performance analysis using the Cramér–Rao lower bound (CRLB) method is conducted. The CRLB for basis material after a projection-domain material decomposition is derived, followed by a set of numerical calculations of CRLBs, for the FKS, the DL-FPD and the joint solution, respectively. To compensate for the slightly angular mismatch between low- and high- projections in FKS, a dual-domain projection completion scheme is implemented. Afterwards material decomposition from the complete projection data is carried out by using the maximum-likelihood method, followed by reconstruction of basis material and virtual monochromatic images (VMI). In this work, the first FKS and DL-FPD jointly enabled multi-energy tabletop CBCT system, to the best of our knowledge, has been developed in our laboratory. To evaluate its spectral imaging performance, a set of physics experiments are conducted, where the multi-energy and head phantoms are scanned using the 80/105/130 kVp switching pairs and projection data are collected using a prototype DL-FPD, whose both top and bottom layer of panels are composed of 550 μ m of cesium iodine (CsI) scintillators with no intermediate metal filter in-between. Results. The numerical simulations show that the joint spectral imaging solution can lead to a significant improvement in energy separation and lower noise levels in most of material decomposition cases. The physics experiments confirmed the feasibility and superiority of the joint spectral imaging, whose CNRs in the selected regions of interest of the multi-energy phantom were boosted by an average improvement of 21.9%, 20.4% for water basis images and 32.8%, 62.8% for iodine images when compared with that of the FKS and DL-FPD, respectively. For the head phantom case, the joint spectral imaging can effectively reduce the streaking artifacts as well, and the standard deviation in the selected regions of interest are reduced by an average decrement of 19.5% and 8.1% for VMI when compared with that of the FKS and DL-FPD, respectively. Conclusions. A feasibility study of the joint spectral imaging solution for CBCT by utilizing both the FKS and DL-FPD was conducted, with the first tabletop CBCT system having such a capability being developed, which exhibits improved CNR and is more effective in avoiding streaking artifacts as expected. [ABSTRACT FROM AUTHOR]

The extension of conventional computed tomography known as spectral computed tomography involves utilizing the variations in X-ray attenuation, driven by spectral and material dependencies. This technique enables the virtual decomposition of scanned objects, revealing their elemental constituents. The resultant images provide quantitative information, such as material concentration within the scanned volume. Enhancements in results are achievable through methods that capitalize on the strong correlation among decomposed images, effectively minimizing noise and artifacts. The Rigaku nano3DX submicron tomograph uses a dual-target source, which allows the generation of two distinct X-ray spectra through different target materials. This configuration holds promise for high-resolution applications in spectral tomography, particularly for low-Z materials, where it offers high contrast in the acquired images. The potential of this setup in the context of spectral computed tomography is explored in this contribution, delving into its applications for materials characterized by low atomic numbers. [ABSTRACT FROM AUTHOR]

For decades, there has been a need in the fire safety science community for a reliable source of material properties and standard fire test data to accurately represent solid materials in fire models. The project described herein has made advancements in experimental data collection and property determination for a multitude of materials commonly encountered in the built environment for the purpose of making the data easily accessible and to elevate the base knowledge and tools available for all model practitioners and investigators. This article includes a description of the experimental methods, procedures, and estimated uncertainty in the measurements that have been adopted to collect the data necessary to describe common materials in the most advanced comprehensive pyrolysis models. A case study is provided in which all the experiments to characterize polycarbonate are described, analytical procedures and derived properties are presented, and validation of the properties against experimental data is presented. [ABSTRACT FROM AUTHOR]

Background: Cone‐beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image‐guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity. Purpose: The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low‐cost approach to accurately solving the x‐ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections. Methods: To deal with the intertwined scatter‐spectral challenge, we propose a novel scatter‐decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution‐preserved and noise‐suppressed multi‐energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first‐pass material decomposition from the generated multi‐energy residual projections in non‐penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second‐pass material decomposition for complete data by using a multi‐material spectral correction method. Monte Carlo simulations of a pure‐water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi‐energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method. Results: Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values (ERMSEROI$E^{\text{ROI}}_{\text{RMSE}}$) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone‐beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, the ERMSEROI$E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non‐uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively. Conclusions: We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance. [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]

Purpose: This study aimed to elucidate whether gadolinium contrast in clinically relevant doses can be used with photon‐counting computed tomography (PCCT) as an alternative contrast agent in clinical applications. Material/methods: A CTDI phantom with 3D printed rods filled with different concentrations of gadolinium and iodine contrast was scanned in a PCCT and an energy‐integrated computed tomography (EICT). Attenuation values at different monoenergetic steps were extracted for each contrast concentration. Results: For PCCT, gadolinium reached an attenuation >100 HU (103 HU) at 40 keV with a concentration 5 mmol/L whereas the same level was reached at 50 keV (118 HU) for 10 mmol/L and 90 keV (114 HU) for 25 mmol/L. For iodine, the same level of attenuation was reached at 100 keV (106 HU) with a concentration 8.75 mg I/mL. For EICT the lowest gadolinium contrast concentration needed to reach >100 HU (108 HU) was 10 mmol/L at 50 keV. For 25 mmol/L 100 HU was reached at 100 keV. For iodine contrast 108 HU was reached at 110 keV for 8.75 mg I/mL. Conclusion: No K‐edge potential or difference in attenuation curves between iodine and gadolinium contrast is detected on the first clinical available PCCT. Clinically relevant attenuation levels were barely achieved in this setting with gadolinium concentrations approved for human use. The results of this study suggest that, given current scanning technology, gadolinium is not a clinically useful contrast agent for computed tomography because no K‐edge was detected. [ABSTRACT FROM AUTHOR]

Modern photon counting detectors allow the calculation of virtual monoenergetic or material decomposed X-ray images but are not yet used for dental panoramic radiography systems. To assess the diagnostic potential and image quality of photon counting detectors in dental panoramic radiography, ethics approval from the local ethics committee was obtained for this retrospective study. Conventional CT scans of the head and neck region were segmented into bone and soft tissue. The resulting datasets were used to calculate panoramic equivalent thickness bone and soft tissue images by forward projection, using a geometry like that of conventional panoramic radiographic systems. The panoramic equivalent thickness images were utilized to generate synthetic conventional panoramic radiographs and panoramic virtual monoenergetic radiographs at various energies. The conventional, two virtual monoenergetic images at 40 keV and 60 keV, and material-separated bone and soft tissue panoramic equivalent thickness X-ray images simulated from 17 head CTs were evaluated in a reader study involving three experienced radiologists regarding their diagnostic value and image quality. Compared to conventional panoramic radiographs, the material-separated bone panoramic equivalent thickness image exhibits a higher image quality and diagnostic value in assessing the bone structure p <. 001 and details such as teeth or root canals p <. 001 . Panoramic virtual monoenergetic radiographs do not show a significant advantage over conventional panoramic radiographs. The conducted reader study shows the potential of spectral X-ray imaging for dental panoramic imaging to improve the diagnostic value and image quality. [ABSTRACT FROM AUTHOR]

To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with >10 years and

Objective. Computed tomography (CT) has advanced since its inception, with breakthroughs such as dual-energy CT (DECT), which extracts additional information by acquiring two sets of data at different energies. As high-flux photon-counting detectors (PCDs) become available, PCD-CT is also becoming a reality. PCD-CT can acquire multi-energy data sets in a single scan by spectrally binning the incident x-ray beam. With this, K-edge imaging becomes possible, allowing high atomic number (high-Z) contrast materials to be distinguished and quantified. In this study, we demonstrated that DECT methods can be converted to PCD-CT systems by extending the method of Bourque et al (2014). We optimized the energy bins of the PCD for this purpose and expanded the capabilities by employing K-edge subtraction imaging to separate a high-atomic number contrast material. Approach. The method decomposes materials into their effective atomic number (Z eff) and electron density relative to water (ρ e ). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with known ρ e values and elemental compositions. Theoretical Z eff values were found for the appropriate energy ranges using the elemental composition of the materials. Z eff varied slightly with energy but was considered a systematic error. An ex vivo bovine tissue sample was decomposed to evaluate the model further and was injected with gold chloride to demonstrate the separation of a K-edge contrast agent. Main results. The mean root mean squared percent errors on the extracted Z eff and ρ e for PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in the ex vivo bovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from the ex vivo tissue sample with K-edge imaging. Significance. PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging. [ABSTRACT FROM AUTHOR]

Purpose: While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images. Methods: This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images. Results: The prediction time was 5.4 s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality. Conclusion: In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner. [ABSTRACT FROM AUTHOR]

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images. [ABSTRACT FROM AUTHOR]

Background: The effective atomic number (Zeff) is widely applied to the identification of unknown materials. One method to determine the Zeff is material‐decomposition‐based spectral X‐ray imaging. The method relies on certain approximations of the X‐ray interaction cross‐sections such as empirical model coefficients. The impact of such approximations on the accuracy of Zeff quantification has not been fully investigated. Purpose: To perform an error analysis of the material‐decomposition‐based Zeff quantification method and propose a coefficient calibration‐in‐groups method to improve the modeling accuracy and reduce the Zeff quantification error. Methods: The model of the material‐decomposition‐based Zeff quantification method relies on the dependence of the interaction cross‐sections (σPE) on the atomic number Z and corresponding coefficient, that is, σPE∝Zm$\sigma _\mathrm{PE}\propto Z^m$. In this work, all the data is from the National Institute of Standards and Technology (NIST) website. First, the coefficient m is calibrated through a logarithmic fitting method to quickly determine the m values for any certain energy and Zeff ranges. Then materials including elements and common compounds with Zeff ranging from 6–20 are selected as the objects whose effective atomic numbers are to be quantified. Different combinations of basis materials are applied to decompose the object materials and their quantification errors are analyzed. With the help of error analysis, the object materials are divided into high‐error and low‐error groups based on the decomposition coefficient ratio amin/amax$a_{min}/a_{max}$, which is found to have a strong correlation with error, and their coefficients are calibrated in groups. Finally, the average errors of three m selection strategies: (1) using an empirical m value of 3.94, which is also considered a standard method; (2) using a single m value, which is calibrated through the logarithmic fitting method; (3) using different m values calibrated in groups, are calculated to test the effectiveness of our method. Results: The approximation of the X‐ray interaction cross‐section leads to certain errors in Zeff quantification and the error distributions for different basis materials are different. The average errors for most basis material combinations (C(6)/Ca(20), C(6)/Al(13), Al(13)/Ca(20), C(6)/Ne(10), Na(11)/P(15)) are lower than 0.5, maintaining good average accuracy. While the average error for S(16)/Ca(20) is up to 0.8461, leading to more misjudgments on atomic number. Meanwhile, the error distribution regularity can be observed. The Pearson's correlation coefficients of absolute errors and decomposition coefficient ratios are 0.743, 0.8432 and 0.7126 for basis material combinations C(6)/Ca(20), C(6)/Al(13) and Al(13)/Ca(20), indicating a good correlation. The method using either empirical m value of 3.94 or single calibrated m value of 4.619 has relatively high average errors. The proposed method using different m values calibrated in groups has the lowest average errors 0.254, 0.203 and 0.169, which are reduced by 21.6%(0.07), 3.8%(0.008) and 62.9%(0.286) respectively compared with the standard method. Conclusions: The error analysis demonstrates that the approximation of X‐ray interaction cross‐sections leads to inevitable errors, while also revealing certain error distribution regularity. The coefficient calibrated‐in‐groups method has better modeling accuracy and has effectively reduced the error compared with the standard method using a single empirical m value of 3.94. [ABSTRACT FROM AUTHOR]

Background: Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast‐to‐noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store. Purpose: This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors. Methods: We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi‐empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér–Rao lower bound (CRLB) due to the energy‐weighted bin compression, for MD and VMI tasks over a range of material area density ρA,m${\rho }_{A,m}$ (0–40 g/cm2 water, 0–2.16 g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively. Results: The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K‐edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively. Conclusions: We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information. [ABSTRACT FROM AUTHOR]

BACKGROUND: Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet. OBJECTIVE: This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, and thus helps determining the optimal system settings. METHODS: To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance. RESULTS: Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging. CONCLUSIONS: Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings. [ABSTRACT FROM AUTHOR]

Background: Thermochemical ablation (TCA) is a minimally invasive therapy under development for hepatocellular carcinoma. TCA simultaneously delivers an acid (acetic acid, AcOH) and base (sodium hydroxide, NaOH) directly into the tumor, where the acid/base chemical reaction produces an exotherm that induces local ablation. However, AcOH and NaOH are not radiopaque, making monitoring TCA delivery difficult. Purpose: We address the issue of image guidance for TCA by utilizing cesium hydroxide (CsOH) as a novel theranostic component of TCA that is detectable and quantifiable with dual‐energy CT (DECT). Materials and methods: To quantify the minimum concentration of CsOH that can be positively identified by DECT, the limit of detection (LOD) was established in an elliptical phantom (Multi‐Energy CT Quality Assurance Phantom, Kyoto Kagaku, Kyoto, Japan) with two DECT technologies: a dual‐source system (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and a split‐filter, single‐source system (SOMATOM Edge, Siemens Healthineers). The dual‐energy ratio (DER) and LOD of CsOH were determined for each system. Cesium concentration quantification accuracy was evaluated in a gelatin phantom before quantitative mapping was performed in ex vivo models. Results: On the dual‐source system, the DER and LOD were 2.94 and 1.36‐mM CsOH, respectively. For the split‐filter system, the DER and LOD were 1.41‐ and 6.11‐mM CsOH, respectively. The signal on cesium maps in phantoms tracked linearly with concentration (R2 = 0.99) on both systems with an RMSE of 2.56 and 6.72 on the dual‐source and split‐filter system, respectively. In ex vivo models, CsOH was detected following delivery of TCA at all concentrations. Conclusions: DECT can be used to detect and quantify the concentration of cesium in phantom and ex vivo tissue models. When incorporated in TCA, CsOH performs as a theranostic agent for quantitative DECT image‐guidance. [ABSTRACT FROM AUTHOR]

Material decomposition (MD) is an application of dual-energy computed tomography (DECT) that decomposes DECT images into specific material images. However, the direct inversion method used in MD often amplifies noise in the decomposed material images, resulting in lower image quality. To address this issue, we propose an image-domain MD method based on the concept of deep image prior (DIP). DIP is an unsupervised learning method that can perform different tasks without using a large training dataset with known targets (i.e., basis material images). We retrospectively recruited patients who underwent non-contrast brain DECT scans and investigated the feasibility of using the proposed DIP-based method to decompose DECT images into two (i.e., bone and soft tissue) and three (i.e., bone, soft tissue, and fat) basis materials. We evaluated the decomposed material images in terms of signal-to-noise ratio (SNR) and modulation transfer function (MTF). The proposed DIP-based method showed greater improvement in SNR in the decomposed soft-tissue images compared to the direct inversion method and the iterative method. Moreover, the proposed method produced similar MTF curves in both two- and three-material decompositions. Additionally, the proposed DIP-based method demonstrated better separation ability than the other two studied methods in the case of three-material decomposition. Our results suggest that the proposed DIP-based method is capable of unsupervisedly generating high-quality basis material images from DECT images. [ABSTRACT FROM AUTHOR]

Background: Flat panel detector (FPD) based cone‐beam computed tomography (CT) has made tremendous progress in the last two decades, with many new and advanced medical and industrial applications keeping emerging from diagnostic imaging and image guidance for radiotherapy and interventional surgery. The current cone‐beam CT (CBCT), however, is still suboptimal for head CT scan which requires a high standard of image quality. While the dual‐layer FPD technology is under extensive development and is promising to further advance CBCT from qualitative anatomic imaging to quantitative dual‐energy CT, its potential of enabling head CBCT applications has not yet been fully investigated. Purpose: The relatively moderate energy separation from the dual‐layer FPD and the overall low signal level especially at the bottom‐layer detector, could raise significant challenges in performing high‐quality dual‐energy material decomposition (MD). In this work, we propose a hybrid, physics and model guided, MD algorithm that attempts to fully use the detected x‐ray signals and prior‐knowledge behind head CBCT using dual‐layer FPD. Methods: Firstly, a regular projection‐domain MD is performed as initial results of our approach and for comparison as conventional method. Secondly, based on the combined projection, a dual‐layer multi‐material spectral correction (dMMSC) is applied to generate beam hardening free images. Thirdly, the dMMSC corrected projections are adopted as a physics‐model based guidance to generate a hybrid MD. A set of physics experiments including fan‐beam scan and cone‐beam scan using a head phantom and a Gammex Multi‐Energy CT phantom are conducted to validate our proposed approach. Results: The combined reconstruction could reduce noise by about 10% with no visible resolution degradation. The fan‐beam studies on the Gammex phantom demonstrated an improved MD performance, with the averaged iodine quantification error for the 5–15 mg/ml iodine inserts reduced from about 5.6% to 3.0% by the hybrid method. On fan‐beam scan of the head phantom, our proposed hybrid MD could significantly reduce the streak artifacts, with CT number nonuniformity (NU) in the selected regions of interest (ROIs) reduced from 23 Hounsfield Units (HU) to 4.2 HU, and the corresponding noise suppressed from 31 to 6.5 HU. For cone‐beam scan, after scatter correction (SC) and cone‐beam artifact reduction (CBAR), our approach can also significantly improve image quality, with CT number NU in the selected ROI reduced from 24.2 to 6.6 HU and the noise level suppressed from 22.1 to 8.2 HU. Conclusions: Our proposed physics and model guided hybrid MD for dual‐layer FPD based head CBCT can significantly improve the robustness of MD and suppress the low‐signal artifact. This preliminary feasibility study also demonstrated that the dual‐layer FPD is promising to enable head CBCT spectral imaging. [ABSTRACT FROM AUTHOR]

Background: Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One‐Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data. Purpose: This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon‐counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large‐scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects. Methods: Head CT data were acquired on an early prototype clinical PCCT system using an edge‐on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two‐step projection‐domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two‐step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft‐tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two‐step methods for a range of regularization constraint settings. Results: cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin, when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft‐tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values. Conclusions: The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large‐scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two‐step approach. [ABSTRACT FROM AUTHOR]

The accurate screening of osteoporosis is important for identifying persons at risk. The diagnosis of bone conditions using dual X-ray absorptiometry is limited to extracting areal bone mineral density (BMD) and fails to provide any structural information. Computed tomography (CT) is excellent for morphological imaging but not ideal for material quantification. Advanced photon-counting detector CT (PCD-CT) possesses high spectral sensitivity and material decomposition capabilities to simultaneously determine qualitative and quantitative information. In this study, we explored the diagnostic utility of PCD-CT to provide high-resolution 3-D imaging of bone microarchitecture and composition for the sensitive diagnosis of bone in untreated and ovariectomized rats. PCD-CT accurately decomposed the calcium content within hydroxyapatite phantoms (r = 0.99). MicroCT analysis of tibial bone revealed significant differences in the morphological parameters between the untreated and ovariectomized samples. However, differences in the structural parameters of the mandible between the treatment groups were not observed. BMD determined with microCT and calcium concentration decomposed using PCD-CT differed significantly between the treatment groups in both the tibia and mandible. Quantitative analysis with PCD-CT is sensitive in determining the distribution of calcium and water components in bone and may have utility in the screening and diagnosis of bone conditions such as osteoporosis. [ABSTRACT FROM AUTHOR]

Purpose: The present study aimed to determine whether the water content change in the medullary cavity of occult rib fractures by spectral computed tomography (CT). Methods: The material decomposition (MD) images were reconstructed using the water-hydroxyapatite basis material pairs from spectral CT. The water contents of the medullary cavity in subtle or occult rib fractures and the symmetrical sites of the contralateral ribs were measured, and their difference was calculated. The absolute value of the water content difference was compared to patients without trauma. An independent samples t-test was adopted to compare the consistency of the water content in the medullary cavity of the normal ribs. Intergroup and pairwise comparisons were applied to the difference in water content among the subtle/occult fractures and normal ribs, followed by receiver operating characteristic curve calculations. p < 0.05 was considered to have a statistically significant difference. Results: A total of 100 subtle fractures, 47 occult fractures, and 96 pairs of normal ribs were included in this study. The water content of the medullary cavity in the subtle and occult fractures was both higher than that in their symmetrical sites with the difference value of 31.06 ± 15.03 mg/cm3 and 27.83 ± 11.40 mg/cm3, respectively. These difference values between the subtle and occult fractures were not statistically significant (p = 0.497). For the normal ribs, the bilateral water contents were not statistically different (p > 0.05) with a difference value of 8.05 ± 6.13 mg/cm3. The increased water content of fractured ribs was higher than that of normal ribs (p < 0.001). According to the classification based on whether the ribs were fractured, the area under the curve was 0.94. Conclusions: The water content measured on MD images in spectral CT in the medullary cavity increased as a response to subtle/occult rib fractures. [ABSTRACT FROM AUTHOR]

To investigate the feasibility of quantifying the chemical composition of coronary artery plaque in terms of water, lipid, protein, and calcium contents using dual-energy computed tomography (CT) in a simulation study. A CT simulation package was developed based on physical parameters of a clinical CT scanner. A digital thorax phantom was designed to simulate coronary arterial plaques in the range of 2-5 mm in diameter. Both non-calcified and calcified plaques were studied. The non-calcified plaques were simulated as a mixture of water, lipid, and protein, while the calcified plaques also contained calcium. The water, lipid, protein, and calcium compositions of the plaques were selected to be within the expected clinical range. A total of 95 plaques for each lesion size were simulated using the CT simulation package at 80 and 135 kVp. Half-value layer measurements were made to make sure the simulated dose was within the range of clinical dual energy scanning protocols. Dual-energy material decomposition using a previously developed technique was performed to determine the volumetric fraction of water, lipid, protein, and calcium contents in each plaque. For non-calcified plaque, the total volume conservation provides the third constrain for three-material decomposition with dual energy CT. For calcified plaque, a fourth criterion was introduced from a previous report suggesting a linear correlation between water and protein contents in soft tissue. For non-calcified plaque, the root mean-squared error (RMSE) of the image-based decomposition was estimated to be 0.7%, 1.5%, and 0.3% for water, lipid, and protein contents, respectively. As for the calcified plaques, the RMSE of the 5 mm plaques were estimated to be 5.6%, 5.7%, 0.2%, and 3.1%, for water, lipid, calcium, and protein contents, respectively. The RMSE increases as the plaque size reduces. The simulation results indicate that chemical composition of coronary arterial plaques can be quantified using dual-energy CT. By accurately quantifying the content of a coronary plaque lesion, our decomposition method may provide valuable insight for the assessment and stratification of coronary artery disease.

Objective. Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions. Approach. We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm. Main results. Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term. Significance. We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy. [ABSTRACT FROM AUTHOR]

Objectives. To characterize for the first time in vivo a novel bismuth-based nanoparticular contrast agent developed for preclinical applications. Then, to design and test in vivo a multi-contrast protocol for functional cardiac imaging using the new bismuth nanoparticles and a well-established iodine-based contrast agent. Approach. A micro-computed tomography scanner was assembled and equipped with a photon-counting detector. Five mice were administered with the bismuth-based contrast agent and systematically scanned over 5 h to quantify the contrast enhancement in relevant organs of interest. Subsequently, the multi-contrast agent protocol was tested on three mice. Material decomposition was performed on the acquired spectral data to quantify the concentration of bismuth and iodine in multiple structures, e.g. the myocardium and vasculature. Main results. In the vasculature, the bismuth agent provides a peak enhancement of 1100 HU and a half-life of about 260 min. After the injection, it accumulates in the liver, spleen and intestinal wall reaching a CT value of 440 HU about 5 h post injection. Phantom measurements showed that the bismuth provides more contrast enhancement than iodine for a variety of tube voltages. The multi-contrast protocol for cardiac imaging successfully allowed the simultaneous decomposition of the vasculature, the brown adipose tissue and the myocardium. Significance. The new bismuth-based contrast agent was proven to have a long circulation time suitable for preclinical applications and to provide more contrast than iodine agents. The proposed multi-contrast protocol resulted in a new tool for cardiac functional imaging. Furthermore, thanks to the contrast enhancement provided in the intestinal wall, the novel contrast agent may be used to develop further multi contrast agent protocols for abdominal and oncological imaging. [ABSTRACT FROM AUTHOR]

BACKGROUND: Photon counting spectral CT is a significant direction in the development of CT technology and material identification is an important application of spectral CT. However, spectrum estimation in photon counting spectral CT is highly complex and may affect quantification accuracy of material identification. OBJECTIVE: To address the problem of energy spectrum estimation in photon-counting spectral CT, this study investigates empirical material decomposition algorithms to achieve accurate quantitative decomposition of the effective atomic number. METHODS: The spectrum is first calibrated using the empirical dual-energy calibration (EDEC) method and the effective atomic number is then quantitatively estimated based on the EDEC method. The accuracy of estimating the effective atomic number of materials under different calibration conditions is investigated by designing different calibration phantoms, and accurate quantitation is achieved using suitable calibration settings. Last, the validity of this method is verified through simulations and experimental studies. RESULTS: The results demonstrate that the error in estimating the effective atomic number is reduced to within 4% for low and medium Z materials, thereby enabling accurate material identification. CONCLUSION: The empirical dual-energy correction method can solve the problem of energy spectrum estimation in photon counting spectral CT. Accurate effective atomic number estimation can be achieved with suitable calibration. [ABSTRACT FROM AUTHOR]

Standard dual-energy computed tomography (CT) uses two different x-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrades or increasing radiation exposure. This paper proposes a different dual-energy CT imaging method that is enabled by PET. Instead of using a second x-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray attenuation image is then combined with the low-energy x-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which would not compromise the PET activity reconstruction too much but may significantly affect the performance of the gamma-ray attenuation image for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available x-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results demonstrate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging.

Abstract Thermal ablation procedures, such as high intensity focused ultrasound and radiofrequency ablation, are often used to eliminate tumors by minimally invasively heating a focal region. For this task, real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings. Current computed tomography (CT) thermometry is based on energy-integrated CT, tissue-specific experimental data, and linear relationships between attenuation and temperature. In this paper, we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest. In our feasibility study, distilled water, 50 mmol/L CaCl2, and 600 mmol/L CaCl2 are chosen as the base materials. Their attenuations are measured in four discrete energy bins at various temperatures. The neural network trained on the experimental data achieves a mean absolute error of 3.97 °C and 1.80 °C on 300 mmol/L CaCl2 and a milk-based protein shake respectively. These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dissimilar to our base materials.

Purpose: The purpose of this study was to evaluate the diagnostic accuracy when using various base material pairs (BMPs) in dual-energy computed tomography (DECT), and to establish corresponding diagnostic standards for assessing bone status through comparison with quantitative computed tomography (QCT). Methods: This prospective study enrolled a total of 469 patients who underwent both non-enhanced chest CT scans under conventional kVp and abdominal DECT. The bone densities of hydroxyapatite (water), hydroxyapatite (fat), hydroxyapatite (blood), calcium (water), and calcium (fat) (DHAP (water), DHAP (fat), DHAP (blood), DCa (water), and DCa (fat)) in the trabecular bone of vertebral bodies (T11–L1) were measured, along with bone mineral density (BMD) via QCT. Intraclass correlation coefficient (ICC) analysis was used to assess the agreement of the measurements. Spearman's correlation test was performed to analyze the relationship between the DECT- and QCT-derived BMD. Receiver operator characteristic (ROC) curves were generated to determine the optimal diagnostic thresholds of various BMPs for diagnosing osteopenia and osteoporosis. Results: A total of 1371 vertebral bodies were measured, and QCT identified 393 with osteoporosis and 442 with osteopenia. Strong correlations were observed between DHAP (water), DHAP (fat), DHAP (blood), DCa (water), and DCa (fat) and the QCT-derived BMD. DHAP (water) showed the best predictive capability for osteopenia and osteoporosis. The area under the ROC curve, sensitivity, and specificity for identifying osteopenia were 0.956, 86.88%, and 88.91% with DHAP (water) ≤ 107.4 mg/cm3, respectively. The corresponding values for identifying osteoporosis were 0.999, 99.24%, and 99.53% with DHAP (water) ≤ 89.62 mg/cm3, respectively. Conclusions: Bone density measurement using various BMPs in DECT enables the quantification of vertebral BMD and the diagnosis of osteoporosis, with DHAP (water) having the highest diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Background: Noise amplification in material decomposition is an issue for exploiting photon‐counting computed tomography (PCCT). Regularization techniques and neighborhood filters have been widely used, but degraded spatial resolution and bias are concerns. Purpose: This paper proposes likelihood‐based bilateral filters that can be applied to pre‐estimated basis sinograms to reduce the noise while minimally affecting spatial resolution and accuracy. Methods: The proposed method needs system models (e.g., incident spectrum, detector response) to calculate the likelihood. First, it performs maximum likelihood (ML)‐based estimation in the projection domain to obtain basis sinograms. The estimated basis sinograms suffer from severe noise but are asymptotically unbiased without degrading spatial resolution. Then it calculates the neighborhood likelihoods for a given measurement at the center pixel using the neighborhood estimates and designs the weights based on the distance of likelihoods. It is also analyzed in terms of statistical inference, and then two variations of the filter are introduced: one that requires the significance level instead of the empirical hyperparameter. The other is a measurement‐based filter, which can be applied when accurate estimates are given without the system models. The proposed methods were validated by analyzing the local property of noise and spatial resolution and the global trend of noise and bias using numerical thorax and abdominal phantoms for a two‐material decomposition (water and bone). They were compared to the conventional neighborhood filters and the model‐based iterative reconstruction with an edge‐preserving penalty applied in the basis images. Results: The proposed method showed comparable or superior performance for the local and global properties to conventional methods in many cases. The thorax phantom: The full width at half maximum (FWHM) decreased by −2%–31% (−2 indicates that it increased by 2% compared to the best performance from conventional methods), and the global bias was reduced by 2%–19% compared to other methods for similar noise levels (local: 51% of the ML, global: 49%) in the water basis image. The FWHM decreased by 8%–31%, and the global bias was reduced by 9%–44% for similar noise levels (local: 44% of the ML, global: 36%) in the CT image at 65 keV. The abdominal phantom: The FWHM decreased by 10%–32%, and the global bias was reduced by 3%–35% compared to other methods for similar noise levels (local: 66% of the ML, global: 67%) in the water basis image. The FWHM decreased by up to −11%–47%, and the global bias was reduced by 13%–35% for similar noise levels (local: 71% of the ML, global: 70%) in the CT image at 60 keV. Conclusions: This paper introduced the likelihood‐based bilateral filters as a post‐processing method applied to the ML‐based estimates of basis sinograms. The proposed filters effectively reduced the noise in the basis images and the synthesized monochromatic CT images. It showed the potential of using likelihood‐based filters in the projection domain as a substitute for conventional regularization or filtering methods. [ABSTRACT FROM AUTHOR]