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A National Institutes of Health (NIH) and U.S. Department of Energy (DOE) Office of Science virtual workshop on shared general topics was held in July of 2021 and reported on in this publication in January of 2023. Following the inaugural 2021 joint meeting representatives from the DOE Office of Science and NIH met to discuss organizing a second joint workshop that would concentrate on radiation detection to bring together teams from both agencies and their grantee populations to stimulate collaboration and efficiency. To meet this scientific mission within the NIH and DOE radiation detection space, the organizers assembled workshop sessions covering the state-of-the-art in cameras, detectors, and sensors for radiation external and internal (diagnostic and therapeutic) to human, data acquisition and electronics, image reconstruction and processing, and the application of artificial intelligence. NIH and DOE are committed to continuing the process of convening a joint workshop every 12-24 months. This Special Report recaps the findings of this second workshop. Beyond showing only the innovations and areas of success, important gaps in our knowledge were defined and presented. We summarize by defining four areas of greatest opportunity and need that emerged from the unique, dynamic dialogue the in-person workshop provided the attendees., (© 2024 American Association of Physicists in Medicine. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: Conventional methods for estimating the noise power spectrum (NPS) often necessitate multiple computed tomography (CT) data acquisitions and are required to satisfy stringent stationarity and ergodicity conditions, which prove challenging in CT imaging systems., Purpose: The aim was to revisit the conventional NPS estimation method, leading to a new framework that estimates local NPS without relying on stationarity or ergodicity, thus facilitating experimental NPS estimations., Methods: The scientific foundation of the conventional CT NPS measurement method, based on the Wiener-Khintchine theorem, was reexamined, emphasizing the critical conditions of stationarity and ergodicity. This work proposes an alternative framework, characterized by its independence from stationarity and ergodicity, and its ability to facilitate local NPS estimations. A spatial average of local NPS over a Region of Interest (ROI) yields the conventional NPS for that ROI. The connections and differences between the proposed alternative method and the conventional method are discussed. Experimental studies were conducted to validate the new method., Results: (1) The NPS estimated using the conventional method was demonstrated to correspond to the spatial average of pointwise NPS from the proposed NPS estimation framework. (2) The NPS estimated over an ROI with the conventional method was shown to be the sum of the NPS estimated from the proposed method and a contribution from measurement uncertainty. (3) Local NPS estimations from the proposed method in this work elucidate the impact of surrounding image content on local NPS variations., Conclusion: The NPS estimation method proposed in this work allows for the estimation of local NPS without relying on stationarity and ergodicity conditions, offering local NPS estimations with significantly improved precision., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied to address the interior reconstruction problems for centered regions of interest (ROIs) with fixed sizes. Developing a method to enable interior tomography with arbitrarily located ROIs with nearly arbitrary ROI sizes inside a scanning field of view (FOV) remains an open question., Purpose: To develop a new pathway to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes using a single trained deep neural network model., Methods: The method consists of two steps. First, an analytical weighted backprojection reconstruction algorithm was developed to perform domain transform from divergent fan-beam projection data to an intermediate image feature space, B ( x ⃗ ) $B(\vec{x})$ , for an arbitrary size ROI at an arbitrary location inside the FOV. Second, a supervised learning technique was developed to train a deep neural network architecture to perform deconvolution to obtain the true image f ( x ⃗ ) $f(\vec{x})$ from the new feature space B ( x ⃗ ) $B(\vec{x})$ . This two-step method is referred to as Deep-Interior for convenience. Both numerical simulations and experimental studies were performed to validate the proposed Deep-Interior method., Results: The results showed that ROIs as small as a diameter of 5 cm could be accurately reconstructed (similarity index 0.985 ± 0.018 on internal testing data and 0.940 ± 0.025 on external testing data) at arbitrary locations within an imaging object covering a wide variety of anatomical structures of different body parts. Besides, ROIs of arbitrary size can be reconstructed by stitching small ROIs without additional training., Conclusion: The developed Deep-Interior framework can enable interior tomographic reconstruction from divergent fan-beam projections for short-scan and super-short-scan acquisitions for small ROIs (with a diameter larger than 5 cm) at an arbitrary location inside the scanning FOV with high quantitative reconstruction accuracy., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis., Purpose: To develop a quality-checked and physics-constrained deep learning (DL) method to estimate material basis images from single-kV CT data without resorting to dual-energy CT acquisition schemes., Methods: Single-kV CT images are decomposed into two material basis images using a deep neural network. The role of this network is to generate a feature space with 64 template features with the same matrix dimensions of the input single-kV CT image. These 64 template image features are then combined to generate the desired material basis images with different sets of combination coefficients, one for each material basis image. Dual-energy CT image acquisitions with two separate kVs were curated to generate paired training data between a single-kV CT image and the corresponding two material basis images. To ensure the obtained two material basis images are consistent with the encoded spectral information in the actual projection data, two physics constraints, that is, (1) effective energy of each measured projection datum that characterizes the beam hardening in data acquisitions and (2) physical factors of scanners such as detector and tube characteristics, are incorporated into the end-to-end training. The entire architecture is referred to as Deep-En-Chroma in this paper. In the application stage, the generated material basis images are sent to a deep quality check (Deep-QC) network to assess the quality of estimated images and to report the pixel-wise estimation errors for users. The models were developed using 5592 training and validation pairs generated from 48 clinical cases. Additional 1526 CT images from another 13 patients were used to evaluate the quantitative accuracy of water and iodine basis images estimated by Deep-En-Chroma., Results: For the iodine basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is -0.25 mg/mL, and the agreement limits are [-0.75 mg/mL, +0.24 mg/mL]. For the water basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is 0.0 g/mL, and the agreement limits are [-0.01 g/mL, 0.01 g/mL]. Across the test cohort, the median [25th, 75th percentiles] root mean square errors between the Deep-En-Chroma and dual-energy material images are 14 [12, 16] mg/mL for the water images and 0.73 [0.64, 0.80] mg/mL for the iodine images. When significant errors are present in the estimated material basis images, Deep-QC can capture these errors and provide pixel-wise error maps to inform users whether the DL results are trustworthy., Conclusions: The Deep-En-Chroma network provides a new pathway to estimating the clinically relevant material basis images from single-kV CT data and the Deep-QC module to inform end-users of the accuracy of the DL material basis images in practice., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation coefficients. These patient models do not account for the detailed anatomical variations of human subjects, and thus, may limit the accuracy of intraview or organ-specific dose modulations in emerging CT technologies., Purpose: The purpose of this work was to show that 3D tomographic patient models can be generated from two-view scout images using deep learning strategies, and the reconstructed 3D patient models indeed enable accurate prescriptions of fluence-field modulated or organ-specific dose delivery in the subsequent CT scans., Methods: CT images and the corresponding two-view scout images were retrospectively collected from 4214 individual CT exams. The collected data were curated for the training of a deep neural network architecture termed ScoutCT-NET to generate 3D tomographic attenuation models from two-view scout images. The trained network was validated using a cohort of 55 136 images from 212 individual patients. To evaluate the accuracy of the reconstructed 3D patient models, radiation delivery plans were generated using ScoutCT-NET 3D patient models and compared with plans prescribed based on true CT images (gold standard) for both fluence-field-modulated CT and organ-specific CT. Radiation dose distributions were estimated using Monte Carlo simulations and were quantitatively evaluated using the Gamma analysis method. Modulated dose profiles were compared against state-of-the-art tube current modulation schemes. Impacts of ScoutCT-NET patient model-based dose modulation schemes on universal-purpose CT acquisitions and organ-specific acquisitions were also compared in terms of overall image appearance, noise magnitude, and noise uniformity., Results: The results demonstrate that (1) The end-to-end trained ScoutCT-NET can be used to generate 3D patient attenuation models and demonstrate empirical generalizability. (2) The 3D patient models can be used to accurately estimate the spatial distribution of radiation dose delivered by standard helical CTs prior to the actual CT acquisition; compared to the gold-standard dose distribution, 95.0% of the voxels in the ScoutCT-NET based dose maps have acceptable gamma values for 5 mm distance-to-agreement and 10% dose difference. (3) The 3D patient models also enabled accurate prescription of fluence-field modulated CT to generate a more uniform noise distribution across the patient body compared to tube current-modulated CT. (4) ScoutCT-NET 3D patient models enabled accurate prescription of organ-specific CT to boost image quality for a given body region-of-interest under a given radiation dose constraint., Conclusion: 3D tomographic attenuation models generated by ScoutCT-NET from two-view scout images can be used to prescribe fluence-field-modulated or organ-specific CT scans with high accuracy for the overall objective of radiation dose reduction or image quality improvement for a given imaging task., (© 2021 American Association of Physicists in Medicine.)

Background: Iodine material images (aka iodine basis images) generated from dual energy computed tomography (DECT) have been used to assess potential perfusion defects in the pulmonary parenchyma. However, iodine material images do not provide the needed absolute quantification of the pulmonary blood pool, as materials with effective atomic numbers (Z eff ) different from those of basis materials may also contribute to iodine material images, thus confounding the quantification of perfusion defects., Purpose: (i) To demonstrate the limitations of iodine material images in pulmonary perfusion defect quantification and (ii) to develop and validate a new quantitative biomarker using effective atomic numbers derived from DECT images., Methods: The quantitative relationship between the perfusion blood volume (PBV) in pulmonary parenchyma and the effective atomic number (Z eff ) spatial distribution was studied to show that the desired quantitative PBV maps are determined by the spatial maps of Z eff as PB V Z eff ( x ) = a Z eff β ( x ) + b , where a, b, and β are three constants. Namely, quantitative PB V Z eff is determined by Z eff images instead of the iodine basis images. Perfusion maps were generated for four human subjects to demonstrate the differences between conventional iodine material image-based PBV (PBV iodine ) derived from two-material decompositions and the proposed PB V Z eff method., Results: Among patients with pulmonary emboli, the proposed PB V Z eff maps clearly show the perfusion defects while the PBV iodine maps do not. Additionally, when there are no perfusion defects present in the derived PBV maps, no pulmonary emboli were diagnosed by an experienced thoracic radiologist., Conclusion: Effective atomic number-based quantitative PBV maps provide the needed sensitive and specific biomarker to quantify pulmonary perfusion defects., (© 2021 American Association of Physicists in Medicine.)

Background: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical cohorts., Purpose: The purpose of this work is to address the previously mentioned challenges in current deep learning methods., Methods: A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse-view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to reconstruct the image. If the noise level from the PICCS reconstruction is not satisfactory, another light duty deep neural network can then be used to reduce noise level. Both extensive numerical simulation data and human subject data have been used to quantitatively and qualitatively assess the performance of the proposed DL-PICCS method in terms of reconstruction accuracy and generalizability., Results: Extensive evaluation studies have demonstrated that: (1) quantitative reconstruction accuracy of DL-PICCS for individual patient is improved when it is compared with the deep learning methods and CS-based methods; (2) the false-positive lesion-like structures and false negative missing anatomical structures in the deep learning approaches can be effectively eliminated in the DL-PICCS reconstructed images; and (3) DL-PICCS enables a deep learning scheme to relax its working conditions to enhance its generalizability., Conclusions: DL-PICCS offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability., (© 2021 American Association of Physicists in Medicine.)

Purpose: A major technical obstacle to bringing x-ray multicontrast (i.e., attenuation, phase, and dark-field) imaging methodology to clinical use is the prolonged data acquisition time caused by the phase stepping procedure. The purpose of this work was to introduce a fast acquisition with seamless stage translation (FASST) technique to a prototype multicontrast breast imaging system for reduced image acquisition time that is clinically acceptable., Methods: The prototype system was constructed based on a Hologic full-field digital mammography + digital breast tomosynthesis combination system. During each FASST acquisition process, a motorized stage holding a diffraction grating travels continuously with a constant velocity, and a train of 15 short x-ray pulses (35 ms each) was delivered by using the Zero-Degree Tomo mode of the Hologic system. Standard phase retrieval was applied to the 15 subimages without spatial interpolation to avoid spatial resolution loss. The method was evaluated using a physical phantom, a bovine udder specimen, and a freshly resected mastectomy specimen. The FASST technique was experimentally compared with single-shot acquisition methods and the standard phase stepping method., Results: The image acquisition time of the proposed method is 3.7 s. In comparison, conventional phase stepping took 105 s using the same prototype imaging system. The mean glandular dose of both methods was matched at 1.3 mGy. No artifacts or spatial resolution loss was observed in images produced by FASST. In contrast, the single-shot methods led to spatial resolution loss and residual moiré artifacts., Conclusions: The FASST technique reduces the data acquisition time of the prototype multicontrast x-ray breast imaging system to 3.7 s, such that it is comparable to a clinical digital breast tomosynthesis exam., (© 2020 American Association of Physicists in Medicine.)

Purpose: In grating-based x-ray differential phase contrast (DPC) imaging, the measured signal amplitude of the phase shift induced by an image object is proportional to the so-called system sensitivity. Therefore, to achieve a better signal-to-noise (SNR) for improved imaging performance, it is generally believed that one should increase the system sensitivity by reducing the period of the analyzer grating or increasing the distance between the phase grating and analyzer grating. The purpose of this work is to theoretically and experimentally demonstrate that there is an optimal system sensitivity to attain the highest SNR for a given task provided that the standard phase-stepping acquisition and phase retrieval methods are used. When system sensitivity goes beyond this optimal value, SNR decreases and the imaging performance deteriorates., Methods: Due to the fundamental fact that the measured phase signal is a cyclic variable, the phase wrapping effect is inevitable in DPC imaging when the system sensitivity increases. The phase wrapping effect appears in both signal and noise measurements. The effect in the signal measurement is manifested in the so-called signal statistical bias and such effect often impacts the accuracy of the measurement. The phase wrapping effect also appears in the noise variance measurement and impacts the precision of the measurement. A thorough theoretical analysis was performed in this work to demonstrate the quantitative impacts of phase wrapping on both signal bias and noise variance and thus on the actual system SNR. The joint effect of phase wrapping in both the signal bias and noise variance yields an optimal system sensitivity to achieve the highest SNR. Both extensive numerical simulation studies and experimental studies were performed to validate the theoretical analysis., Results: Both theoretical analysis and experimental studies show that the SNR of the DPC signal is not always proportional to the sensitivity due to the cyclic nature of the signal and the phase wrapping effect. For a given refraction angle and exposure level, there exists an optimal sensitivity factor that maximizes the SNR, beyond which, increasing the sensitivity will decrease the SNR., Conclusions: Increase of system sensitivity does not always improve x-ray DPC imaging performance provided that the standard phase-stepping acquisition and phase retrieval methods are used., (© 2019 American Association of Physicists in Medicine.)

Purpose: The development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method., Methods: The CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models., Results: The noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV., Conclusions: The statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV., (© 2019 American Association of Physicists in Medicine.)

Purpose: The purpose of this work was to develop a theoretical framework to pinpoint the quantitative relationship between input parameters of deconvolution-based cerebral computed tomography perfusion (CTP) imaging systems and statistical properties of the output perfusion maps., Methods: Deconvolution-based CTP systems assume that the arterial input function, tissue enhancement curve, and flow-scaled residue function k(t) are related to each other through a convolution model, and thus by reversing the convolution operation, k(t) and the associated perfusion parameters can be estimated. The theoretical analysis started by deriving analytical formulas for the expected value and autocovariance of the residue function estimated using the singular value decomposition-based deconvolution method. Next, it analyzed statistical properties of the "max" and "arg max" operators, based on which the signal and noise properties of cerebral blood flow (CBF) and time-to-max ( t max ) are quantitatively related to the statistical model of the estimated residue function [ k * ( t ) ] and system parameters. To validate the theory, CTP images of a digital head phantom were simulated, from which signal and noise of each perfusion parameter were measured and compared with values calculated using the theoretical model. In addition, an in vivo canine experiment was performed to validate the noise model of cerebral blood volume (CBV)., Results: For the numerical study, the relative root mean squared error between the measured and theoretically calculated value is ≤0.21% for the autocovariance matrix of k * ( t ) , and is ≤0.13% for the expected form of k * ( t ) . A Bland-Altman analysis demonstrated no significant difference between measured and theoretical values for the mean or noise of each perfusion parameter. For the animal study, the theoretical CBV noise fell within the 25th and 75th percentiles of the experimental values. To provide an example of the theory's utility, an expansion of the CBV noise formula was performed to unveil the dominant role of the baseline image noise in deconvolution-based CBV. Correspondingly, data of the three canine subjects used in the Part I paper were retrospectively processed to confirm that preferentially partitioning dose to the baseline frames benefits both nondeconvolution- and deconvolution-based CBV maps., Conclusions: Quantitative relationships between the statistical properties of deconvolution-based CTP maps, source image acquisition and reconstruction parameters, contrast injection protocol, and deconvolution parameters are established., (© 2019 American Association of Physicists in Medicine.)

Purpose: In previous works, it has been demonstrated that for filtered backprojection (FBP) reconstruction-based computed tomography (CT) images, the measured CT numbers are biased and the bias level decreases with increasing radiation dose. Low-dose scans typically include noise reduction schemes to reduce noise level. The purpose of this work was to investigate the potential impact of different noise reduction schemes on the CT number bias., Methods: Three different filtration methods: Gaussian, adaptive trimmed mean (ATM), and anisotropic diffusion (AD) were implemented to reduce noise. All filters were independently applied in three different domains: raw counts, log-processed sinogram, or reconstructed image domain. A quality assurance phantom was scanned on a benchtop CT cone beam CT system, at dose levels ranging from 0.6 to 4.0 mGy. The conventional FBP reconstructions were performed to reconstruct CT images for the study of CT number biases. The CT number bias of different material inserts in the phantom was then measured. To further study the overall impact of CT number bias together with the potential consequences of noise reduction schemes on both the spatial resolution and noise characteristics, the task-based detectability of a high-contrast and high spatial resolution imaging task was used as an example to assess the performance of each noise reduction scheme. To qualitatively assess the impact of these noise reduction schemes on image, an anthropomorphic head phantom was also scanned on the benchtop CT system and processed with the above noise reduction schemes to generate images for demonstration., Results: Our results demonstrated the following major findings: (a) CT number bias can be significantly reduced when the noise reduction schemes are implemented in the raw counts domain; CT number bias cannot be reduced when these noise reduction schemes are implemented either in the reconstructed image domain or in the log-processed sinogram domain. (b) The extent of CT number bias reduction is dependent on both the material composition and noise reduction parameters. (c) The overall impact of the noise reduction schemes can be studied using the task-based detectability analysis framework and this framework can be used to select the appropriate parameters in each noise reduction scheme to optimize the performance for a given imaging task., Conclusions: Noise reduction schemes can be used to considerably reduce CT number bias when they are implemented in the raw counts domain; however, their application cannot be arbitrarily extended to either the log-processed sinogram data domain or image domain. Trade-offs between bias reduction and overall image quality must be studied for an optimal performance of a given imaging task., (© 2019 American Association of Physicists in Medicine.)

Purpose: The CT number accuracy, that is, CT number bias, plays an important role in clinical diagnosis. When strategies to reduce radiation dose are discussed, it is important to make sure that the CT number bias is controlled within an acceptable range. The purpose of this paper was to investigate the dependence of CT number bias on radiation dose level and on image contrast (i.e., the difference in CT number between the ROI and the background) in Computed Tomography (CT)., Methods: A lesion-background model was introduced to theoretically study how the CT number bias changes with radiation exposure level and with CT number contrast when a simple linear reconstruction algorithm such as filtered backprojection (FBP) is used. The theoretical results were validated with experimental studies using a benchtop CT system equipped with a photon-counting detector (XC-HYDRA FX50, XCounter AB, Sweden) and a clinical diagnostic MDCT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI, USA) equipped with an energy-integrating detector. The Catphan phantom (Catphan 600, the Phantom Laboratory, Salem, NY, USA) was scanned at different mAs levels and 50 scans were performed for each mAs. The bias of CT number was evaluated for each combination of mAs and ROIs with different contrast levels. An anthropomorphic phantom (ATOM 10-year-old phantom, Model 706, CIRS Inc. Norfolk, VA, USA) with much more heterogeneous object content was used to test the applicability of the theory to the more general image object cases., Results: Both theoretical and experimental studies showed that the CT number bias is inversely proportional to the radiation exposure level yet linearly dependent on the CT number contrast between the lesion and the background, that is, Bias ( μ ^ 1 FBP ) = α mAs ( 1 + β Δ H U ) ., Conclusions: The quantitative accuracy of CT numbers can be problematic and thus needs some extra attention when radiation dose is reduced. In this work, we showed that the bias of the FBP reconstruction increases as mAs is reduced; both positive and negative bias can be observed depending on the contrast difference between a targeted ROI and its surrounding background tissues., (© 2018 American Association of Physicists in Medicine.)

Purpose: Digital breast tomosynthesis (DBT) has been shown to somewhat alleviate the breast tissue overlapping issues of two-dimensional (2D) mammography. However, the improvement in current DBT systems over mammography is still limited. Statistical image reconstruction (SIR) methods have the potential to reduce through-plane artifacts in DBT, and thus may be used to further reduce anatomical clutter. The purpose of this work was to study the impact of SIR on anatomical clutter in the reconstructed DBT image volumes., Methods: An SIR with a slice-wise total variation (TV) regularizer was implemented to reconstruct DBT images which were compared with the clinical reconstruction method (filtered backprojection). The artifact spread function (ASF) was measured to quantify the reduction of the through-plane artifacts level in phantom studies and microcalcifications in clinical cases. The anatomical clutter was quantified by the anatomical noise power spectrum with a power law fitting model: NPS a ( f) = α f -β . The β values were measured from the reconstructed image slices when the two reconstruction methods were applied to a cohort of clinical breast exams (N = 101) acquired using Hologic Selenia Dimensions DBT systems., Results: The full width half maximum (FWHM) of the measured ASF was reduced from 8.7 ± 0.1 mm for clinical reconstruction to 6.5 ± 0.1 mm for SIR which yields a 25% reduction in FWHM in phantom studies and the same amount of ASF reduction was also found in clinical measurements from microcalcifications. The measured β values for the two reconstruction methods were 3.17 ± 0.36 and 2.14 ± 0.39 for the clinical reconstruction method and the SIR method, respectively. This difference was statistically significant (P << 0.001). The dependence of β on slice location using either method was negligible., Conclusions: Statistical image reconstruction enabled a significant reduction of both the through-plane artifacts level and anatomical clutter in the DBT reconstructions. The β value was found to be β≈2.14 with the SIR method. This value stays in the middle between the β≈1.8 for cone beam CT and β≈3.2 for mammography. In contrast, the measured β value in the clinical reconstructions (β≈3.17) remains close to that of mammography., (© 2018 American Association of Physicists in Medicine.)

Purpose: Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods., Methods: Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs., Results: The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work., Conclusions: A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object., (© 2018 American Association of Physicists in Medicine.)

Purpose: Low-signal correction (LSC) in the raw counts domain has been shown to effectively reduce noise streaks in CT because the data inconsistency associated with photon-starved regions may be mitigated prior to the log transformation step. However, a systematic study of the performance of these raw data correction methods is still missing in literature. The purpose of this work was to provide such a systematic study for two well-known low-signal correction schemes using either the adaptive trimmed mean (ATM) filter or the anisotropic diffusion (AD) filter in the raw counts domain., Methods: Image data were acquired experimentally using an anthropomorphic chest phantom and a benchtop cone-beam CT (CBCT) imaging system. Phantom scans were repeated 50 times at a reduced dose level of 0.5 mGy and a reference level of 1.9 mGy. The measured raw counts at 0.5 mGy underwent LSC using the ATM and AD filters. Two relevant parameters were identified for each filter and approximately one hundred operating points in each parameter space were analyzed. Following LSC and log transformation, FDK reconstruction was performed for each case. Noise and spatial resolution properties were assessed across the parameter spaces that define each LSC filter; the results were summarized through 2D contour maps to better understand the trade-offs between these competing image quality features. 2D noise power spectrum (NPS) and modulation transfer function (MTF) were measured locally at two spatial locations in the field-of-view (FOV): a posterior region contaminated by noise streaks and an anterior region away from noise streaks. An isotropy score metric was introduced to characterize the directional dependence of the NPS and MTF (viz., ϵ NPS and ϵ MTF , respectively), with a range from 0 for highly anisotropic to 1 for perfectly isotropic. The noise magnitude and coarseness were also measured., Results: (a) Both the ATM and AD LSC methods were successful in reducing noise streaks, but their noise and spatial resolution properties were found to be highly anisotropic and shift-variant. (b) NPS isotropy scores in the posterior region were generally improved from ϵ NPS = 0.09 for the images without LSC to the range ϵ NPS = (0.11, 0.67) for ATM and ϵ NPS = (0.06, 0.67) for AD, depending on the filter parameters used. (c) The noise magnitude was reduced across the parameter space of either LSC filter whenever a change along the axis of the controlling parameter led to stronger raw data filtration. Changes in noise magnitude were inversely related to changes in spatial resolution along the direction perpendicular to the streaks. No correlation was found, however, between the contour maps of noise magnitude and the NPS isotropy. (d) Both filters influenced the noise coarseness anisotropically, with coarser noise occurring along directions perpendicular to the noise streaks. The anisotropic noise coarseness was intrinsically and directly related to resolution losses in a given direction: coarseness plots mimic the topography of the 2D MTF, i.e., the coarser the noise, the lower the resolution., Conclusions: Both AD and ATM LSC schemes enable low-dose CBCT imaging. However, it was found that noise magnitude and overall spatial resolution vary considerably across the parameter space for each filter, and more importantly these image quality features are highly anisotropic and shift-variant., (© 2018 American Association of Physicists in Medicine.)

Purpose: Although a variety of mathematical observer models have been developed to predict human observer performance for low contrast lesion detection tasks, their predictive power for high contrast and high spatial resolution discrimination imaging tasks, including those in CT bone imaging, could be limited. The purpose of this work was to develop a modified observer model that has improved correlation with human observer performance for these tasks., Methods: The proposed observer model, referred to as the modified ideal observer model (MIOM), uses a weight function to penalize components in the task function that have less contribution to the actual human observer performance for high contrast and high spatial resolution discrimination tasks. To validate MIOM, both human observer and observer model studies were performed, each using exactly the same CT imaging task [discrimination of a connected component in a high contrast (1000 HU) high spatial resolution bone fracture model (0.3 mm)] and experimental CT image data. For the human observer studies, three physicist observers rated the connectivity of the fracture model using a five-point Likert scale; for the observer model studies, a total of five observer models, including both conventional models and the proposed MIOM, were used to calculate the discrimination capability of the CT images in resolving the connected component. Images used in the studies encompassed nine different reconstruction kernels. Correlation between human and observer model performance for these kernels were quantified using the Spearman rank correlation coefficient (ρ). After the validation study, an example application of MIOM was presented, in which the observer model was used to select the optimal reconstruction kernel for a High-Resolution (Hi-Res, GE Healthcare) CT scan technique., Results: The performance of the proposed MIOM correlated well with that of the human observers with a Spearman rank correlation coefficient ρ of 0.88 (P = 0.003). In comparison, the value of ρ was 0.05 (P = 0.904) for the ideal observer, 0.05 (P = 0.904) for the non-prewhitening observer, -0.18 (P = 0.634) for the non-prewhitening observer with eye filter and internal noise, and 0.30 (P = 0.427) for the prewhitening observer with eye filter and internal noise. Using the validated MIOM, the optimal reconstruction kernel for the Hi-Res mode to perform high spatial resolution and high contrast discrimination imaging tasks was determined to be the HD Ultra kernel at the center of the scan field of view (SFOV), or the Lung kernel at the peripheral region of the SFOV. This result was consistent with visual observations of nasal CT images of an in vivo canine subject., Conclusion: Compared with other observer models, the proposed modified ideal observer model provides significantly improved correlation with human observers for high contrast and high spatial resolution CT imaging tasks., (© 2017 American Association of Physicists in Medicine.)

Purpose: In grating-based x-ray multi-contrast imaging, signals of three contrast mechanisms-absorption contrast, differential phase contrast (DPC), and dark-field contrast-can be estimated from the same set of acquired data. The estimated signals, N 0 (related to absorption), N 1 (related to dark-field), and φ (related to DPC) may be intrinsically biased. However, it is yet unclear how large these biases are and how the data acquisition parameters affect the biases in the extracted signals. The purpose of this paper was to address these questions., Methods: The biases of the extracted signals (i.e., N 0 , N 1 and φ) were theoretically studied for a well-known signal estimation method. Experimental data acquired from a grating-based x-ray multi-contrast benchtop imaging system with a photon counting detector were used to validate the theoretical results for the signal biases of the three contrast mechanisms., Results: Both theoretical and experimental studies showed the following results: (1) The bias of signal estimation for the absorption contrast signal is zero; (2) The bias of signal estimation for N 1 is inversely proportional to the number of phase steps and to the average fringe visibility of the grating interferometer, but the ratio between the bias and the signal level (i.e., the relative bias) is independent of the number of phase steps; (3) The bias of signal estimation for φ depends on the mean DPC signal level, the total exposure level of the multi-contrast data acquisition, and the mean fringe visibility of the interferometer., Conclusions: In grating-based x-ray multi-contrast imaging, the estimated absorption contrast signal is unbiased; the estimated dark-field contrast signal is biased, but the relative bias is only dependent on the mean fringe visibility of the interferometer and the exposure level. The estimated DPC signal may be biased, and the bias level depends on the mean signal level, the exposure level, and the interferometer performance., (© 2017 American Association of Physicists in Medicine.)

Purpose: When the scan field of view (SFOV) of a CT system is not large enough to enclose the entire cross-section of the patient, or the patient needs to be positioned partially outside the SFOV for certain clinical applications, truncation artifacts often appear in the reconstructed CT images. Many truncation artifact correction methods perform extrapolations of the truncated projection data based on certain a priori assumptions. The purpose of this work was to develop a novel CT truncation artifact reduction method that directly operates on DICOM images., Materials and Methods: The blooming of pixel values associated with truncation was modeled using exponential decay functions, and based on this model, a discriminative dictionary was constructed to represent truncation artifacts and nonartifact image information in a mutually exclusive way. The discriminative dictionary consists of a truncation artifact subdictionary and a nonartifact subdictionary. The truncation artifact subdictionary contains 1000 atoms with different decay parameters, while the nonartifact subdictionary contains 1000 independent realizations of Gaussian white noise that are exclusive with the artifact features. By sparsely representing an artifact-contaminated CT image with this discriminative dictionary, the image was separated into a truncation artifact-dominated image and a complementary image with reduced truncation artifacts. The artifact-dominated image was then subtracted from the original image with an appropriate weighting coefficient to generate the final image with reduced artifacts. This proposed method was validated via physical phantom studies and retrospective human subject studies. Quantitative image evaluation metrics including the relative root-mean-square error (rRMSE) and the universal image quality index (UQI) were used to quantify the performance of the algorithm., Results: For both phantom and human subject studies, truncation artifacts at the peripheral region of the SFOV were effectively reduced, revealing soft tissue and bony structure once buried in the truncation artifacts. For the phantom study, the proposed method reduced the relative RMSE from 15% (original images) to 11%, and improved the UQI from 0.34 to 0.80., Conclusion: A discriminative dictionary representation method was developed to mitigate CT truncation artifacts directly in the DICOM image domain. Both phantom and human subject studies demonstrated that the proposed method can effectively reduce truncation artifacts without access to projection data., (© 2016 American Association of Physicists in Medicine.)

Purpose: Noise characteristics of clinical multidetector CT (MDCT) systems can be quantified by the noise power spectrum (NPS). Although the NPS of CT has been extensively studied in the past few decades, the joint impact of the bowtie filter and object position on the NPS has not been systematically investigated. This work studies the interplay of these two factors on the two dimensional (2D) local NPS of a clinical CT system that uses the filtered backprojection algorithm for image reconstruction., Methods: A generalized NPS model was developed to account for the impact of the bowtie filter and image object location in the scan field-of-view (SFOV). For a given bowtie filter, image object, and its location in the SFOV, the shape and rotational symmetries of the 2D local NPS were directly computed from the NPS model without going through the image reconstruction process. The obtained NPS was then compared with the measured NPSs from the reconstructed noise-only CT images in both numerical phantom simulation studies and experimental phantom studies using a clinical MDCT scanner. The shape and the associated symmetry of the 2D NPS were classified by borrowing the well-known atomic spectral symbols s, p, and d, which correspond to circular, dumbbell, and cloverleaf symmetries, respectively, of the wave function of electrons in an atom. Finally, simulated bar patterns were embedded into experimentally acquired noise backgrounds to demonstrate the impact of different NPS symmetries on the visual perception of the object., Results: (1) For a central region in a centered cylindrical object, an s-wave symmetry was always present in the NPS, no matter whether the bowtie filter was present or not. In contrast, for a peripheral region in a centered object, the symmetry of its NPS was highly dependent on the bowtie filter, and both p-wave symmetry and d-wave symmetry were observed in the NPS. (2) For a centered region-ofinterest (ROI) in an off-centered object, the symmetry of its NPS was found to be different from that of a peripheral ROI in the centered object, even when the physical positions of the two ROIs relative to the isocenter were the same. (3) The potential clinical impact of the highly anisotropic NPS, caused by the interplay of the bowtie filter and position of the image object, was highlighted in images of specific bar patterns oriented at different angles. The visual perception of the bar patterns was found to be strongly dependent on their orientation., Conclusions: The NPS of CT depends strongly on the bowtie filter and object position. Even if the location of the ROI with respect to the isocenter is fixed, there can be different symmetries in the NPS, which depend on the object position and the size of the bowtie filter. For an isolated off-centered object, the NPS of its CT images cannot be represented by the NPS measured from a centered object.

Purpose: The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions., Methods: A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results., Results: At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels., Conclusions: The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions.

Purpose: Phantom-based objective image quality assessment methods are widely used in the medical physics community. For a filtered backprojection (FBP) reconstruction-based linear or quasilinear imaging system, the use of this methodology is well justified. Many key image quality metrics acquired with phantom studies can be directly applied to in vivo human subject studies. Recently, a variety of image quality metrics have been investigated for model-based iterative image reconstruction (MBIR) methods and several novel characteristics have been discovered in phantom studies. However, the following question remains unanswered: can certain results obtained from phantom studies be generalized to in vivo animal studies and human subject studies? The purpose of this paper is to address this question., Methods: One of the most striking results obtained from phantom studies is a novel power-law relationship between noise variance of MBIR (σ(2)) and tube current-rotation time product (mAs): σ(2) ∝ (mAs)(-0.4) [K. Li et al., "Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance," Med. Phys. 41, 041906 (15pp.) (2014)]. To examine whether the same power-law works for in vivo cases, experimental data from two types of in vivo studies were analyzed in this paper. All scans were performed with a 64-slice diagnostic CT scanner (Discovery CT750 HD, GE Healthcare) and reconstructed with both FBP and a MBIR method (Veo, GE Healthcare). An Institutional Animal Care and Use Committee-approved in vivo animal study was performed with an adult swine at six mAs levels (10-290). Additionally, human subject data (a total of 110 subjects) acquired from an IRB-approved clinical trial were analyzed. In this clinical trial, a reduced-mAs scan was performed immediately following the standard mAs scan; the specific mAs used for the two scans varied across human subjects and were determined based on patient size and clinical indications. The measurements of σ(2) were performed at different mAs by drawing regions-of-interest (ROIs) in the liver and the subcutaneous fat. By applying a linear least-squares regression, the β values in the power-law relationship σ(2) ∝ (mAs)(-β) were measured for the in vivo data and compared with the value found in phantom experiments., Results: For the in vivo swine study, an exponent of β = 0.43 was found for MBIR, and the coefficient of determination (R(2)) for the corresponding least-squares power-law regression was 0.971. As a reference, the β and R(2) values for FBP were found to be 0.98 and 0.997, respectively, from the same study, which are consistent with the well-known σ(2) ∝ (mAs)(-1.0) relationship for linear CT systems. For the human subject study, the measured β values for the MBIR images were 0.41 ± 0.12 in the liver and 0.37 ± 0.12 in subcutaneous fat. In comparison, the β values for the FBP images were 1.04 ± 0.10 in the liver and 0.97 ± 0.12 in subcutaneous fat. The β values of MBIR and FBP obtained from the in vivo studies were found to be statistically equivalent to the corresponding β values from the phantom study within an equivalency interval of [ - 0.1, 0.1] (p < 0.05); across MBIR and FBP, the difference in β was statistically significant (p < 0.05)., Conclusions: Despite the nonlinear nature of the MBIR method, the power-law relationship, σ(2) ∝ (mAs)(-0.4), found from phantom studies can be applied to in vivo animal and human subject studies.

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis-particularly in younger patients-might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT., Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo(TM), GE Healthcare) were used to reconstruct CT images of the airways. For each kV-mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV-mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV-mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings., Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose levels. For FBP, the relative bias and the angular standard deviation of the measured WT increased steeply with decreasing radiation dose. Except for the smallest airway, MBIR enabled significant reduction in both the relative bias and angular standard deviation of the WT, particularly at low radiation dose levels; the SSQ was reduced by 50%-96% by using MBIR. The optimal reconstruction algorithm was found to be MBIR for the seven airways being assessed, and the combined use of MBIR and optimal kV-mAs selection resulted in a radiation dose reduction of 37%-83% compared with a reference scan protocol with a dose level of 1 mGy., Conclusions: The quantification accuracy of airway WT is strongly influenced by radiation dose and reconstruction algorithm. The MBIR algorithm potentially allows the desired WT quantification accuracy to be achieved with reduced radiation dose, which may enable a wider clinical use of MDCT for the assessment of airway WT, particularly for younger patients who may be more sensitive to exposures with ionizing radiation.

Purpose: For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems., Methods: The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d'(kV,mAs) is not lower than the prescribed value of d'R for a given imaging task. Since it is difficult to analytically model the dependence of d' on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d'(kV,mAs) at a variety of kV-mAs combinations, after which the overlay of the dose contours and d' contours is used to graphically determine the optimal kV-mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, d' for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50-700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d'R=16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow., Results: For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d' map and the iso-CNR map were the same for FBP; thus, the use of d'- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d' and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA = 350, dose = 37.5 mGy; for MBIR, kV = 120, mA = 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments., Conclusions: A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance.

Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition., Methods: In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial-temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial-temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial-temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam., Results: In numerical simulations, the 240(∘) short scan angular span was divided into four consecutive 60(∘) angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200(∘), three 66(∘) angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes., Conclusions: In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60(∘) angular subsectors.

Purpose: In x-ray absorption mammography, it has been found that the anatomical background noise can be characterized by a power law dependence on the spatial frequency, NPSa(f) ≈ αf(-β). In this letter, the authors present the first experimental results of the corresponding exponents, β, for differential phase contrast (βDPC) and dark field contrast (βDF) mammography., Methods: A grating-based x-ray multicontrast imaging acquisition benchtop system was used to simultaneously acquire mammograms with three different contrast mechanisms from 15 cadaver breasts under the same x-ray data acquisition conditions. The cadaver breasts were imaged in the coronal plane. The authors' experimental implementation of the well documented method [Burgess, Jacobson, and Judy, Med. Phys. 28, 419-437 (2001)] to extract the exponent β was first validated using anonymized clinical mammograms. Experiments were then used to determine β for the three types of mammograms for each cadaver breast acquired with our multicontrast imaging system: absorption contrast mammogram (βAbs.), differential phase contrast mammogram (βDPC), and dark-field contrast mammogram (βDF)., Results: The measured β values, acquired in the coronal plane with the benchtop multicontrast imaging system are βAbs. = 3.61 ± 0.49, βDPC = 2.54 ± 0.75, and βDF = 1.44 ± 0.49 for absorption, differential phase, and dark field mammogram, respectively., Conclusions: The β values for differential phase contrast and dark field mammography are significantly lower than the measured value of β for the corresponding absorption contrast mammograms. The greatly reduced β value of the anatomical background noise in differential phase contrast and dark field mammograms may suggest potentially improved diagnostic performance for certain types of breast cancer imaging tasks.

Purpose: Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks., Methods: The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDIvol =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo(®), GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'., Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels., Conclusions: Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.

Purpose: Dynamic CT myocardial perfusion imaging has the potential to provide both functional and anatomical information regarding coronary artery stenosis. However, radiation dose can be potentially high due to repeated scanning of the same region. The purpose of this study is to investigate the use of statistical iterative reconstruction to improve parametric maps of myocardial perfusion derived from a low tube current dynamic CT acquisition., Methods: Four pigs underwent high (500 mA) and low (25 mA) dose dynamic CT myocardial perfusion scans with and without coronary occlusion. To delineate the affected myocardial territory, an N-13 ammonia PET perfusion scan was performed for each animal in each occlusion state. Filtered backprojection (FBP) reconstruction was first applied to all CT data sets. Then, a statistical iterative reconstruction (SIR) method was applied to data sets acquired at low dose. Image voxel noise was matched between the low dose SIR and high dose FBP reconstructions. CT perfusion maps were compared among the low dose FBP, low dose SIR and high dose FBP reconstructions. Numerical simulations of a dynamic CT scan at high and low dose (20:1 ratio) were performed to quantitatively evaluate SIR and FBP performance in terms of flow map accuracy, precision, dose efficiency, and spatial resolution., Results: Forin vivo studies, the 500 mA FBP maps gave -88.4%, -96.0%, -76.7%, and -65.8% flow change in the occluded anterior region compared to the open-coronary scans (four animals). The percent changes in the 25 mA SIR maps were in good agreement, measuring -94.7%, -81.6%, -84.0%, and -72.2%. The 25 mA FBP maps gave unreliable flow measurements due to streaks caused by photon starvation (percent changes of +137.4%, +71.0%, -11.8%, and -3.5%). Agreement between 25 mA SIR and 500 mA FBP global flow was -9.7%, 8.8%, -3.1%, and 26.4%. The average variability of flow measurements in a nonoccluded region was 16.3%, 24.1%, and 937.9% for the 500 mA FBP, 25 mA SIR, and 25 mA FBP, respectively. In numerical simulations, SIR mitigated streak artifacts in the low dose data and yielded flow maps with mean error <7% and standard deviation <9% of mean, for 30 × 30 pixel ROIs (12.9 × 12.9 mm(2)). In comparison, low dose FBP flow errors were -38% to +258%, and standard deviation was 6%-93%. Additionally, low dose SIR achieved 4.6 times improvement in flow map CNR(2) per unit input dose compared to low dose FBP., Conclusions: SIR reconstruction can reduce image noise and mitigate streaking artifacts caused by photon starvation in dynamic CT myocardial perfusion data sets acquired at low dose (low tube current), and improve perfusion map quality in comparison to FBP reconstruction at the same dose.

Purpose: To reduce radiation dose in CT imaging, the statistical model based iterative reconstruction (MBIR) method has been introduced for clinical use. Based on the principle of MBIR and its nonlinear nature, the noise performance of MBIR is expected to be different from that of the well-understood filtered backprojection (FBP) reconstruction method. The purpose of this work is to experimentally assess the unique noise characteristics of MBIR using a state-of-the-art clinical CT system., Methods: Three physical phantoms, including a water cylinder and two pediatric head phantoms, were scanned in axial scanning mode using a 64-slice CT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI) at seven different mAs levels (5, 12.5, 25, 50, 100, 200, 300). At each mAs level, each phantom was repeatedly scanned 50 times to generate an image ensemble for noise analysis. Both the FBP method with a standard kernel and the MBIR method (Veo(®), GE Healthcare, Waukesha, WI) were used for CT image reconstruction. Three-dimensional (3D) noise power spectrum (NPS), two-dimensional (2D) NPS, and zero-dimensional NPS (noise variance) were assessed both globally and locally. Noise magnitude, noise spatial correlation, noise spatial uniformity and their dose dependence were examined for the two reconstruction methods., Results: (1) At each dose level and at each frequency, the magnitude of the NPS of MBIR was smaller than that of FBP. (2) While the shape of the NPS of FBP was dose-independent, the shape of the NPS of MBIR was strongly dose-dependent; lower dose lead to a "redder" NPS with a lower mean frequency value. (3) The noise standard deviation (σ) of MBIR and dose were found to be related through a power law of σ ∝ (dose)(-β) with the component β ≈ 0.25, which violated the classical σ ∝ (dose)(-0.5) power law in FBP. (4) With MBIR, noise reduction was most prominent for thin image slices. (5) MBIR lead to better noise spatial uniformity when compared with FBP. (6) A composite image generated from two MBIR images acquired at two different dose levels (D1 and D2) demonstrated lower noise than that of an image acquired at a dose level of D1+D2., Conclusions: The noise characteristics of the MBIR method are significantly different from those of the FBP method. The well known tradeoff relationship between CT image noise and radiation dose has been modified by MBIR to establish a more gradual dependence of noise on dose. Additionally, some other CT noise properties that had been well understood based on the linear system theory have also been altered by MBIR. Clinical CT scan protocols that had been optimized based on the classical CT noise properties need to be carefully re-evaluated for systems equipped with MBIR in order to maximize the method's potential clinical benefits in dose reduction and/or in CT image quality improvement., (© 2014 American Association of Physicists in Medicine.)

Purpose: This paper concerns the feasibility of x-ray differential phase contrast (DPC) tomosynthesis imaging using a grating-based DPC benchtop experimental system, which is equipped with a commercial digital flat-panel detector and a medical-grade rotating-anode x-ray tube. An extensive system characterization was performed to quantify its imaging performance., Methods: The major components of the benchtop system include a diagnostic x-ray tube with a 1.0 mm nominal focal spot size, a flat-panel detector with 96 μm pixel pitch, a sample stage that rotates within a limited angular span of ± 30°, and a Talbot-Lau interferometer with three x-ray gratings. A total of 21 projection views acquired with 3° increments were used to reconstruct three sets of tomosynthetic image volumes, including the conventional absorption contrast tomosynthesis image volume (AC-tomo) reconstructed using the filtered-backprojection (FBP) algorithm with the ramp kernel, the phase contrast tomosynthesis image volume (PC-tomo) reconstructed using FBP with a Hilbert kernel, and the differential phase contrast tomosynthesis image volume (DPC-tomo) reconstructed using the shift-and-add algorithm. Three inhouse physical phantoms containing tissue-surrogate materials were used to characterize the signal linearity, the signal difference-to-noise ratio (SDNR), the three-dimensional noise power spectrum (3D NPS), and the through-plane artifact spread function (ASF)., Results: While DPC-tomo highlights edges and interfaces in the image object, PC-tomo removes the differential nature of the DPC projection data and its pixel values are linearly related to the decrement of the real part of the x-ray refractive index. The SDNR values of polyoxymethylene in water and polystyrene in oil are 1.5 and 1.0, respectively, in AC-tomo, and the values were improved to 3.0 and 2.0, respectively, in PC-tomo. PC-tomo and AC-tomo demonstrate equivalent ASF, but their noise characteristics quantified by the 3D NPS were found to be different due to the difference in the tomosynthesis image reconstruction algorithms., Conclusions: It is feasible to simultaneously generate x-ray differential phase contrast, phase contrast, and absorption contrast tomosynthesis images using a grating-based data acquisition setup. The method shows promise in improving the visibility of several low-density materials and therefore merits further investigation.

Purpose: With the recently expanding interest and developments in x-ray differential phase contrast CT (DPC-CT), the evaluation of its task-specific detection performance and comparison with the corresponding absorption CT under a given radiation dose constraint become increasingly important. Mathematical model observers are often used to quantify the performance of imaging systems, but their correlations with actual human observers need to be confirmed for each new imaging method. This work is an investigation of the effects of stochastic DPC-CT noise on the correlation of detection performance between model and human observers with signal-known-exactly (SKE) detection tasks., Methods: The detectabilities of different objects (five disks with different diameters and two breast lesion masses) embedded in an experimental DPC-CT noise background were assessed using both model and human observers. The detectability of the disk and lesion signals was then measured using five types of model observers including the prewhitening ideal observer, the nonprewhitening (NPW) observer, the nonprewhitening observer with eye filter and internal noise (NPWEi), the prewhitening observer with eye filter and internal noise (PWEi), and the channelized Hotelling observer (CHO). The same objects were also evaluated by four human observers using the two-alternative forced choice method. The results from the model observer experiment were quantitatively compared to the human observer results to assess the correlation between the two techniques., Results: The contrast-to-detail (CD) curve generated by the human observers for the disk-detection experiments shows that the required contrast to detect a disk is inversely proportional to the square root of the disk size. Based on the CD curves, the ideal and NPW observers tend to systematically overestimate the performance of the human observers. The NPWEi and PWEi observers did not predict human performance well either, as the slopes of their CD curves tended to be steeper. The CHO generated the best quantitative agreement with human observers with its CD curve overlapping with that of human observer. Statistical equivalence between CHO and humans can be claimed within 11% of the human observer results, including both the disk and lesion detection experiments., Conclusions: The model observer method can be used to accurately represent human observer performance with the stochastic DPC-CT noise for SKE tasks with sizes ranging from 8 to 128 pixels. The incorporation of the anatomical noise remains to be studied.

Purpose: The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution., Methods: Both numerical simulations with known ground truth and in vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution., Results: In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In the in vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness; images also had a qualitatively more uniform noise spatial distribution when including a statistical model., Conclusions: DR-PICCS enables to reconstruct CT images with lower noise than FBP and the loss of spatial resolution can be mitigated to a large extent. The introduction of statistical modeling in DR-PICCS may improve some noise characteristics, but it also leads to anisotropic spatial resolution properties. A denoising method, such as the directional diffusion filtering, has been demonstrated to reduce anisotropy in spatial resolution effectively when it was combined with DR-PICCS with statistical modeling.

Purpose: Using a grating interferometer, a conventional x-ray cone beam computed tomography (CT) data acquisition system can be used to simultaneously generate both conventional absorption CT (ACT) and differential phase contrast CT (DPC-CT) images from a single data acquisition. Since the two CT images were extracted from the same set of x-ray projections, it is expected that intrinsic relationships exist between the noise properties of the two contrast mechanisms. The purpose of this paper is to investigate these relationships., Methods: First, a theoretical framework was developed using a cascaded system model analysis to investigate the relationship between the noise power spectra (NPS) of DPC-CT and ACT. Based on the derived analytical expressions of the NPS, the relationship between the spatial-frequency-dependent noise equivalent quanta (NEQ) of DPC-CT and ACT was derived. From these fundamental relationships, the NPS and NEQ of the DPC-CT system can be derived from the corresponding ACT system or vice versa. To validate these theoretical relationships, a benchtop cone beam DPC-CT/ACT system was used to experimentally measure the modulation transfer function (MTF) and NPS of both DPC-CT and ACT. The measured three-dimensional (3D) MTF and NPS were then combined to generate the corresponding 3D NEQ., Results: Two fundamental relationships have been theoretically derived and experimentally validated for the NPS and NEQ of DPC-CT and ACT: (1) the 3D NPS of DPC-CT is quantitatively related to the corresponding 3D NPS of ACT by an inplane-only spatial-frequency-dependent factor 1∕f (2), the ratio of window functions applied to DPC-CT and ACT, and a numerical factor C(g) determined by the geometry and efficiency of the grating interferometer. Note that the frequency-dependent factor is independent of the frequency component f(z) perpendicular to the axial plane. (2) The 3D NEQ of DPC-CT is related to the corresponding 3D NEQ of ACT by an f (2) scaling factor and numerical factors that depend on both the attenuation and refraction properties of the image object, as well as C(g) and the MTF of the grating interferometer., Conclusions: The performance of a DPC-CT system is intrinsically related to the corresponding ACT system. As long as the NPS and NEQ of an ACT system is known, the corresponding NPS and NEQ of the DPC-CT system can be readily estimated using additional characteristics of the grating interferometer.

Purpose: Prior image constrained compressed sensing (PICCS) is an image reconstruction framework that takes advantage of a prior image to improve the image quality of CT reconstructions. An interesting question that remains to be investigated is whether or not the introduction of a statistical model of the photon detection in the PICCS reconstruction framework can improve the performance of the algorithm when dealing with high noise projection datasets. The goal of the research presented in this paper is to characterize the noise properties of images reconstructed using PICCS with and without statistical modeling. This paper investigates these properties in the clinical context of time-resolved contrast-enhanced CT., Methods: Both numerical phantom studies and an Institutional Review Board approved human subject study were used in this research. The conventional filtered backprojection (FBP), and PICCS with and without the statistical model were applied to each dataset. The prior image used in PICCS was generated by averaging over FBP reconstructions from different time frames of the time-resolved CT exam, thus reducing the noise level. Numerical studies were used to evaluate if the noise characteristics are altered for varying levels of noise, as well as for different object shapes. The dataset acquired in vivo was used to verify that the conclusions reached from numerical studies translate adequately to a clinical case. The results were analyzed using a variety of qualitative and quantitative metrics such as the universal image quality index, spatial maps of the noise standard deviations, the noise uniformity, the noise power spectrum, and the model-observer detectability., Results: The noise characteristics of PICCS were shown to depend on the noise level contained in the data, the level of eccentricity of the object, and whether or not the statistical model was applied. Most differences in the characteristics were observed in the regime of low incident x-ray fluence. No substantial difference was observed between PICCS with and without statistics in the high fluence domain. Objects with a semi-major axis ratio below 0.85 were more accurately reconstructed with lower noise using the statistical implementation. Above that range, for mostly circular objects, the PICCS implementation without the statistical model yielded more accurate images and a lower noise level. At all levels of eccentricity, the noise spatial distribution was more uniform and the model-observer detectability was greater for PICCS with the statistical model. The human subject study was consistent with the results obtained using numerical simulations., Conclusions: For mildly eccentric objects in the low noise regime, PICCS without the noise model yielded equal or better noise level and image quality than the statistical formulation. However, in a vast majority of cases, images reconstructed using statistical PICCS have a noise power spectrum that facilitated the detection of model lesions. The inclusion of a statistical model in the PICCS framework does not always result in improved noise characteristics.

Purpose: To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise and streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI., Methods: Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA., Results: Images reconstructed using FBP showed a highly nonuniform spatial distribution of noise. This spatial nonuniformity led to large fluctuations in the temporal direction. In the numerical phantom study, the level of noise was shown to vary by as much as 87% within a given image, and as much as 110% between different time frames for a ROI far from isocenter. The spatially nonuniform noise pattern was shown to correlate with the source trajectory and the object structure. In contrast, images reconstructed using SIR showed a highly uniform spatial distribution of noise, leading to smaller unexpected noise fluctuations in the temporal direction when a short scan angular range was used. In the numerical phantom study, the noise varied by less than 37% within a given image, and by less than 20% between different time frames. Also, the noise standard deviation in SIR images was on average half of that of FBP images. In the in vivo studies, the deviation observed between quantitative perfusion metrics measured from low-dose scans and high-dose scans was mitigated when SIR was used instead of FBP to reconstruct images., Conclusions: (1) Images reconstructed using FBP suffered from nonuniform spatial noise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Images reconstructed using SIR had a much lower and more uniform noise level and thus can be used as a potential solution to address the FBP nonuniformity. (3) Given the improvement in the accuracy of the perfusion metrics when using SIR, it may be desirable to use a statistical reconstruction framework to perform low-dose dynamic CT MPI.

Purpose: The purpose of this work is to demonstrate that multicontrast computed tomography (CT) imaging can be performed using a Talbot-Lau interferometer without phase stepping, thus allowing for an acquisition scheme like that used for standard absorption CT., Methods: Rather than using phase stepping to extract refraction, small-angle scattering (SAS), and absorption signals, the two gratings of a Talbot-Lau interferometer were rotated slightly to generate a moiré pattern on the detector. A Fourier analysis of the moiré pattern was performed to obtain separate projection images of each of the three contrast signals, all from the same single-shot of x-ray exposure. After the signals were extracted from the detector data for all view angles, image reconstruction was performed to obtain absorption, refraction, and SAS CT images. A physical phantom was scanned to validate the proposed data acquisition method. The results were compared with a phantom scan using the standard phase stepping approach., Results: The reconstruction of each contrast mechanism produced the expected results. Signal levels and contrasts match those obtained using the phase stepping technique., Conclusions: Absorption, refraction, and SAS CT imaging can be achieved using the Talbot-Lau interferometer without the additional overhead of long scan time and phase stepping.

Purpose: Prior image constrained compressed sensing (PICCS) is an image reconstruction framework which incorporates an often available prior image into the compressed sensing objective function. The images are reconstructed using an optimization procedure. In this paper, several alternative unconstrained minimization methods are used to implement PICCS. The purpose is to study and compare the performance of each implementation, as well as to evaluate the performance of the PICCS objective function with respect to image quality., Methods: Six different minimization methods are investigated with respect to convergence speed and reconstruction accuracy. These minimization methods include the steepest descent (SD) method and the conjugate gradient (CG) method. These algorithms require a line search to be performed. Thus, for each minimization algorithm, two line searching algorithms are evaluated: a backtracking (BT) line search and a fast Newton-Raphson (NR) line search. The relative root mean square error is used to evaluate the reconstruction accuracy. The algorithm that offers the best convergence speed is used to study the performance of PICCS with respect to the prior image parameter α and the data consistency parameter λ. PICCS is studied in terms of reconstruction accuracy, low-contrast spatial resolution, and noise characteristics. A numerical phantom was simulated and an animal model was scanned using a multirow detector computed tomography (CT) scanner to yield the projection datasets used in this study., Results: For λ within a broad range, the CG method with Fletcher-Reeves formula and NR line search offers the fastest convergence for an equal level of reconstruction accuracy. Using this minimization method, the reconstruction accuracy of PICCS was studied with respect to variations in α and λ. When the number of view angles is varied between 107, 80, 64, 40, 20, and 16, the relative root mean square error reaches a minimum value for α ≈ 0.5. For values of α near the optimal value, the spatial resolution of the reconstructed image remains relatively constant and the noise texture is very similar to that of the prior image, which was reconstructed using the filtered backprojection (FBP) algorithm., Conclusions: Regarding the performance of the minimization methods, the nonlinear CG method with NR line search yields the best convergence speed. Regarding the performance of the PICCS image reconstruction, three main conclusions can be reached. (1) The performance of PICCS is optimal when the weighting parameter of the prior image parameter is selected to be near α = 0.5. (2) The spatial resolution measured for static objects in images reconstructed using PICCS from undersampled datasets is not degraded with respect to the fully-sampled reconstruction for α near its optimal value. (3) The noise texture of PICCS reconstructions is similar to that of the prior image, which was reconstructed using the conventional FBP method.

Purpose: As a counterpart of 4DCT in the treatment planning stage of radiotherapy treatment, 4D cone beam computed tomography (4DCBCT) method has been proposed to verify tumor motion trajectories before radiation therapy treatment delivery. Besides 4DCBCT acquisition using slower gantry rotation speed or multiple rotations, a new method using the prior image constrained compressed sensing (PICCS) image reconstruction method and the standard 1-min data acquisition were proposed. In this paper, the PICCS-4DCBCT method was combined with deformable registration to validate its capability in motion trajectory extraction using physical phantom data, simulated human subject data from 4DCT and in vivo human subject data., Methods: Two methods were used to validate PICCS-4DCBCT for the purpose of respiratory motion delineation. The standard 1-min gantry rotation Cone Beam CT acquisition was used for both methods. In the first method, 4DCBCT projection data of a physical motion phantom were acquired using an on-board CBCT acquisition system (Varian Medical Systems, Palo Alto, CA). Using a deformable registration method, the object motion trajectories were extracted from both FBP and PICCS reconstructed 4DCBCT images, and compared against the programmed motion trajectories. In the second method, using a clinical 4DCT dataset, Cone Beam CT projections were simulated by forward projection. Using a deformable registration method, the tumor motion trajectories were extracted from the reconstructed 4DCT and PICCS-4DCBCT images. The performance of PICCS-4DCBCT is assessed against the 4DCT ground truth. The breathing period was varied in the simulation to study its effect on motion extraction. For both validation methods, the root mean square error (RMSE) and the maximum of the errors (MaxE) were used to quantify the accuracy of the extracted motion trajectories. After the validation, a clinical dataset was used to demonstrate the motion delineation capability of PICCS-4DCBCT for human subjects., Results: In both validation studies, the RMSEs of the extracted motion trajectories from PICCS-4DCBCT images are less than 0.7 mm, and their MaxEs are less than 1 mm, for all three directions. In comparison, FBP-4DCBCT shows considerably larger RMSEs in the physical phantom based validation. PICCS-4DCBCT also shows insensitivity to the breathing period in the 4DCT based validation. For the in vivo human subject study, high quality 3D motion trajectory of the tumor was obtained from PICCS-4DCBCT images and showed consistency with visual observation., Conclusions: These results demonstrate accurate delineation of tumor motion trajectory can be achieved using PICCS-4DCBCT and the standard 1-min data acquisition.

Purpose: The recently proposed prior image constrained compressed sensing (PICCS) method has been applied in cardiac MDCT to improve the temporal resolution by approximately a factor of 2, by using projection data acquired from half of the standard short-scan angular range to reconstruct images with improved temporal resolution. The method was referred to as temporal resolution improvement using PICCS (TRI-PICCS). The primary purpose of this article is to study (1) the relationship between the performance of the TRI-PICCS algorithm and the angular range of projection data used in image reconstruction; (2) the relationship between the performance of the TRI-PICCS algorithm and the motion orientations and motion patterns of moving objects; and (3) the relationship between the performance of the TRI-PICCS algorithm and various heart rates., Methods: A hybrid phantom consisting of realistic cardiac anatomy and eight moving objects with known motion profiles to simulate coronary arteries was constructed by superimposing the analytical projection data of eight simulated moving vessels to the in vivo projection data from a cardiac MDCT scan. The motion profiles of the moving objects may independently change orientations, period, and amplitude. A prior image was reconstructed using a short-scan filtered backprojection method from a gated short-scan data set for each given motion profile. The TRI-PICCS method was applied to improve temporal resolution for each configuration of given motion profiles of moving objects and given active angular range specified by the target temporal resolution. To quantitatively study the performance, figures of merit were introduced to quantify signal intensity deficit, image distortion, and residual motion artifacts, respectively., Results: The performance of the TRI-PICCS method is the same when the projection data are taken from 100 degrees to 120 degrees. The performance of the TRI-PICCS method is independent of location and motion orientations. The performance of the TRI-PICCS method does not significantly degrade for heart rates up to 100 bpm with a gantry rotation speed of 350 ms per rotation., Conclusions: The TRI-PICCS method can be used to systematically improve temporal resolution for MDCT cardiac imaging by a factor of 2-2.3 and the performance of the TRI-PICCS method is insensitive to motion locations and motion orientations. The TRI-PICCS method enables a single-source MDCT scanner with 350 ms or faster gantry speed to scan patients with heart rates as high as 100 bpm.

The current paradigm for temporal resolution improvement is to add more source-detector units and/or increase the gantry rotation speed. The purpose of this article is to present an innovative alternative method to potentially improve temporal resolution by approximately a factor of 2 for all MDCT scanners without requiring hardware modification. The central enabling technology is a most recently developed image reconstruction method: Prior image constrained compressed sensing (PICCS). Using the method, cardiac CT images can be accurately reconstructed using the projection data acquired in an angular range of about 120 degrees, which is roughly 50% of the standard short-scan angular range (approximately 240 degrees for an MDCT scanner). As a result, the temporal resolution of MDCT cardiac imaging can be universally improved by approximately a factor of 2. In order to validate the proposed method, two in vivo animal experiments were conducted using a state-of-the-art 64-slice CT scanner (GE Healthcare, Waukesha, WI) at different gantry rotation times and different heart rates. One animal was scanned at heart rate of 83 beats per minute (bpm) using 400 ms gantry rotation time and the second animal was scanned at 94 bpm using 350 ms gantry rotation time, respectively. Cardiac coronary CT imaging can be successfully performed at high heart rates using a single-source MDCT scanner and projection data from a single heart beat with gantry rotation times of 400 and 350 ms. Using the proposed PICCS method, the temporal resolution of cardiac CT imaging can be effectively improved by approximately a factor of 2 without modifying any scanner hardware. This potentially provides a new method for single-source MDCT scanners to achieve reliable coronary CT imaging for patients at higher heart rates than the current heart rate limit of 70 bpm without using the well-known multisegment FBP reconstruction algorithm. This method also enables dual-source MDCT scanner to achieve higher temporal resolution without further hardware modifications.

Cone-beam computed tomography (CBCT) using an "on-board" x-ray imaging device integrated into a radiation therapy system has recently been made available for patient positioning, target localization, and adaptive treatment planning. One of the challenges for gantry mounted image-guided radiation therapy (IGRT) systems is the slow acquisition of projections for cone-beam CT (CBCT), which makes them sensitive to any patient motion during the scans. Aiming at motion artifact reduction, four-dimensional CBCT (4D CBCT) techniques have been introduced, where a surrogate for the target's motion profile is utilized to sort the cone-beam data by respiratory phase. However, due to the limited gantry rotation speed and limited readout speed of the on-board imager, fewer than 100 projections are available for the image reconstruction at each respiratory phase. Thus, severe undersampling streaking artifacts plague 4D CBCT images. In this paper, the authors propose a simple scheme to significantly reduce the streaking artifacts. In this method, a prior image is first reconstructed using all available projections without gating, in which static structures are well reconstructed while moving objects are blurred. The undersampling streaking artifacts from static structures are estimated from this prior image volume and then can be removed from the phase images using gated reconstruction. The proposed method was validated using numerical simulations, experimental phantom data, and patient data. The fidelity of stationary and moving objects is maintained, while large gains in streak artifact reduction are observed. Using this technique one can reconstruct 4D CBCT datasets using no more projections than are acquired in a 60 s scan. At the same time, a temporal gating window as narrow as 100 ms was utilized. Compared to the conventional 4D CBCT reconstruction, streaking artifacts were reduced by 60% to 70%.

A novel exact fan-beam image reconstruction formula is presented and validated using both phantom data and clinical data. This algorithm takes the form of the standard ramp filtered backprojection (FBP) algorithm plus local compensation terms. This algorithm will be referred to as a locally compensated filtered backprojection (LCFBP). An equal weighting scheme is utilized in this algorithm in order to properly account for redundantly measured projection data. The algorithm has the desirable property of maintaining a mathematically exact result for: the full scan mode (2pi), the short scan mode (pi+ full fan angle), and the supershort scan mode [less than (pi+ full fan angle)]. Another desirable feature of this algorithm is that it is derivative-free. This feature is beneficial in preserving the spatial resolution of the reconstructed images. The third feature is that an equal weighting scheme has been utilized in the algorithm, thus the new algorithm has better noise properties than the standard filtered backprojection image reconstruction with a smooth weighting function. Both phantom data and clinical data were utilized to validate the algorithm and demonstrate the superior noise properties of the new algorithm.

The sum of all attenuation data acquired in one view of parallel-beam projections is a view angle independent constant. This fact is known as a data consistency condition on the two-dimensional Radon transforms. It plays an important role in tomographic image reconstruction and artifact correction. In this paper, a novel fan-beam data consistency condition (FDCC) is derived and presented. Using the FDCC, individual projection data in one view of fan-beam projections can be estimated from filtering all other projection data measured from different view angles. Numerical simulations are performed to validate the new FDCC in correcting ring artifacts caused by malfunctioning detector cells.