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OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.

Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to health concerns. But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction. As a result, low signal correction is needed to help reduce artifacts while retaining relevant anatomical structures. Low signal can be encountered in cases where sufficient number of photons do not reach the detector to have confidence in the recorded data. X-ray photons, assumed to have Poisson distribution, have signal to noise ratio proportional to the dose, with poorer SNR in low signal areas. Electronic noise added by the data acquisition system further reduces the signal quality. In this paper we will demonstrate a technique to combat low signal artifacts through adaptive filtration. It entails statistics-based filtering on the uncorrected data, correcting the lower signal areas more aggressively than the high signal ones. We look at local averages to decide how aggressive the filtering should be, and local standard deviation to decide how much detail preservation to apply. Implementation consists of a pre-correction step i.e. local linear minimum mean-squared error correction, followed by a variance stabilizing transform, and finally adaptive bilateral filtering. The coefficients of the bilateral filter are computed using local statistics. Results show that improvements were made in terms of low frequency bias, streaks, local average and standard deviation, modulation transfer function and noise power spectrum.

The iterative reconstruction methods ASiR and ASiR-V have been accepted by hundreds of sites as their standard of care for a variety of protocols and applications. While the reduction in noise has been significant some readers have a preference for the classic image appearance. To maintain the classic image appearance of FBP at the same dose levels used for the standard of care with ASiR-V we introduce, Deep Learning Image Reconstruction (DLIR), a technique using artificial neural networks. This paper demonstrates that DLIR can maintain or improve upon the performance of the conventional iterative reconstruction algorithm (ASiR-V) in terms of low contrast detectability, noise, and spatial resolution.

Purpose: The authors investigated the performance of a recently introduced 160-mm/256-row CT system for low dose quantitative myocardial perfusion (MP) imaging of the whole heart. This platform is equipped with a gantry capable of rotating at 280 ms per full cycle, a second generation of adaptive statistical iterative reconstruction (ASiR-V) to correct for image noise arising from low tube voltage potential/tube current dynamic scanning, and image reconstruction algorithms to tackle beam-hardening, cone-beam, and partial-scan effects. Methods: Phantom studies were performed to investigate the effectiveness of image noise and artifact reduction with a GE Healthcare Revolution CT system for three acquisition protocols used in quantitative CT MP imaging: 100, 120, and 140 kVp/25 mAs. The heart chambers of an anthropomorphic chest phantom were filled with iodinated contrast solution at different concentrations (contrast levels) to simulate the circulation of contrast through the heart in quantitative CT MP imaging. To evaluate beam-hardening correction, the phantom was scanned at each contrast level to measure the changes in CT number (in Hounsfield unit or HU) in the water-filled region surrounding the heart chambers with respect to baseline. To evaluate cone-beam artifact correction, differences in mean water HU between the central and peripheral slices were compared. Partial-scan artifact correction was evaluated from the fluctuation of mean water HU in successive partial scans. To evaluate image noise reduction, a small hollow region adjacent to the heart chambers was filled with diluted contrast, and contrast-to-noise ratio in the region before and after noise correction with ASiR-V was compared. The quality of MP maps acquired with the CT system was also evaluated in porcine CT MP studies. Myocardial infarct was induced in a farm pig from a transient occlusion of the distal left anterior descending (LAD) artery with a catheter-based interventional procedure. MP maps were generated from the dynamic contrast-enhanced (DCE) heart images taken at baseline and three weeks after the ischemic insult. Results: Their results showed that the phantom and animal images acquired with the CT platform were minimally affected by image noise and artifacts. For the beam-hardening phantom study, changes in water HU in the wall surrounding the heart chambers greatly reduced from >±30 to ≤ ± 5 HU at all kVp settings except one region at 100 kVp (7 HU). For the cone-beam phantom study, differences in mean water HU from the central slice were less than 5 HU at two peripheral slices with each 4 cm away from the central slice. These findings were reproducible in the pig DCE images at two peripheral slices that were 6 cm away from the central slice. For the partial-scan phantom study, standard deviations of the mean water HU in 10 successive partial scans were less than 5 HU at the central slice. Similar observations were made in the pig DCE images at two peripheral slices with each 6 cm away from the central slice. For the image noise phantom study, CNRs in the ASiR-V images were statistically higher (p < 0.05) than the non-ASiR-V images at all kVp settings. MP maps generated from the porcine DCE images were in excellent quality, with the ischemia in the LAD territory clearly seen in the three orthogonal views. Conclusions: The study demonstrates that this CT system can provide accurate and reproducible CT numbers during cardiac gated acquisitions across a wide axial field of view. This CT number fidelity will enable this imaging tool to assess contrast enhancement, potentially providing valuable added information beyond anatomic evaluation of coronary stenoses. Furthermore, their results collectively suggested that the 100 kVp/25 mAs protocol run on this CT system provides sufficient image accuracy at a low radiation dose (

High-resolution reconstruction of reaction dynamics in nuclear-physics experiments with radioactive beams requires accurate knowledge of the beam-particle trajectories and the precise alignment of detectors with respect to the reaction target. In many cases, sub-millimeter position measurements of fragile beam-tracking and particle–detector systems are essential. We have constructed a laser-based alignment system (LBAS) which is a non-contact, high-precision alignment tool designed for applications where excellent spatial positioning must be achieved. The working principles and performance of the laser-based alignment system are presented.

Over the past two decades, rapid system and hardware development of x-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. The algorithmic development can generally be classified into three major areas: analytical reconstruction, model-based iterative reconstruction, and application-specific reconstruction. Given the limited scope of this chapter, it is nearly impossible to cover every important development in this field; it is equally difficult to provide sufficient breadth and depth on each selected topic. As a compromise, we have decided, for a selected few topics, to provide sufficient high-level technical descriptions and to discuss their advantages and applications.

Time-resolved cardiac imaging is particularly interesting in the interventional setting since it would provide both image guidance for accurate procedural planning and cardiac functional evaluations directly in the operating room. Imaging the heart in vivo using a slowly rotating C-arm system is extremely challenging due to the limitations of the data acquisition system and the high temporal resolution required to avoid motion artifacts. In this paper, a data acquisition scheme and an image reconstruction method are proposed to achieve time-resolved cardiac cone-beam computed tomography imaging with isotropic spatial resolution and high temporal resolution using a slowly rotating C-arm system. The data are acquired within 14 s using a single gantry rotation with a short scan angular range. The enabling image reconstruction method is the prior image constrained compressed sensing (PICCS) algorithm. The prior image is reconstructed from data acquired over all cardiac phases. Each cardiac phase is then reconstructed from the retrospectively gated cardiac data using the PICCS algorithm. To validate the method, several studies were performed. Both numerical simulations using a hybrid motion phantom with static background anatomy as well as physical phantom studies have been used to demonstrate that the proposed method enables accurate reconstruction of image objects with a high isotropic spatial resolution. A canine animal model scanned in vivo was used to further validate the method.

X-ray scatter is a significant problem in cone-beam computed tomography when thicker objects and larger cone angles are used, as scattered radiation can lead to reduced contrast and CT number inaccuracy. Advances have been made in x-ray computed tomography (CT) by incorporating a high quality prior image into the image reconstruction process. In this paper, we extend this idea to correct scatter-induced shading artifacts in cone-beam CT image-guided radiation therapy. Specifically, this paper presents a new scatter correction algorithm which uses a prior image with low scatter artifacts to reduce shading artifacts in cone-beam CT images acquired under conditions of high scatter. The proposed correction algorithm begins with an empirical hypothesis that the target image can be written as a weighted summation of a series of basis images that are generated by raising the raw cone-beam projection data to different powers, and then, reconstructing using the standard filtered backprojection algorithm. The weight for each basis image is calculated by minimizing the difference between the target image and the prior image. The performance of the scatter correction algorithm is qualitatively and quantitatively evaluated through phantom studies using a Varian 2100 EX System with an on-board imager. Results show that the proposed scatter correction algorithm using a prior image with low scatter artifacts can substantially mitigate scatter-induced shading artifacts in both full-fan and half-fan modes.

Recently dynamic, time-resolved three-dimensional computed tomography angiography (CTA) has been introduced to the neurological imaging community. However, the radiation dose delivered to patients in time-resolved CTA protocol is a high and potential risk associated with the ionizing radiation dose. Thus, minimizing the radiation dose is highly desirable for time-resolved CTA. In order to reduce the radiation dose delivered during dynamic, contrast-enhanced CT applications, we introduce here the CT formulation of HighlY constrained back PRojection (HYPR) imaging. We explore the radiation dose reduction approaches of both acquiring a reduced number of projections for each image and lowering the tube current used during acquisition. We then apply HYPR image reconstruction to produce image sets at a reduced patient dose and with low image noise. Numerical phantom experiments and retrospective analysis of in vivo canine studies are used to assess the accuracy and quality of HYPR reduced dose image sets and validate our approach. Experimental results demonstrated that a factor of 6–8 times radiation dose reduction is possible when the HYPR algorithm is applied to time-resolved CTA exams.

Cone-beam computed tomography (CBCT) has been clinically used to verify patient position and to localize the target of treatment in image-guided radiation therapy (IGRT). However, when the chest and the upper abdomen are scanned, respiratory-induced motion blurring limits the utility of CBCT. In order to mitigate this blurring, respiratory-gated CBCT, i.e. 4D CBCT, was introduced. In 4D CBCT, the cone-beam projection data sets acquired during a gantry rotation are sorted into several respiratory phases. In these gated reconstructions, the number of projections for each respiratory phase is significantly reduced. Consequently, undersampling streaking artifacts are present in the reconstructed images, and the image contrast resolution is also significantly compromised. In this paper, we present a new method to simultaneously achieve both high temporal resolution ( approximately 100 ms) and streaking artifact-free image volumes in 4D CBCT. The enabling technique is a newly proposed image reconstruction method, i.e. prior image constrained compressed sensing (PICCS), which enables accurate image reconstruction using vastly undersampled cone-beam projections and a fully sampled prior image. Using PICCS, a streak-free image can be reconstructed from 10-20 cone-beam projections while the signal-to-noise ratio is determined by a denoising feature of the selected objective function and by the prior image, which is reconstructed using all of the acquired cone-beam projections. This feature of PICCS breaks the connection between the temporal resolution and streaking artifacts' level in 4D CBCT. Numerical simulations and experimental phantom studies have been conducted to validate the method.

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%.

Rationale and Objectives Existing cardiac imaging methods do not allow for improved temporal resolution when considering a targeted region of interest (ROI). The imaging method presented here enables improved temporal resolution for ROI imaging (namely, a reconstruction volume smaller than the complete field of view). Clinically, temporally targeted reconstruction would not change the primary means of reconstructing and evaluating images, but rather would enable the adjunct technique of ROI imaging, with improved temporal resolution compared with standard reconstruction (∼20% smaller temporal scan window). In gated cardiac computed tomography (CT) scans improved temporal resolution directly translates into a reduction in motion artifacts for rapidly moving objects such as the coronary arteries. Materials and Methods Retrospectively electrocardiogram gated coronary angiography data from a 64-slice CT system were used. A motion phantom simulating the motion profile of a coronary artery was constructed and scanned. Additionally, an in vivo study was performed using a porcine model. Comparisons between the new reconstruction technique and the standard reconstruction are given for an ROI centered on the right coronary artery, and a pulmonary ROI. Results In both a well-controlled motion model and a porcine model results show a decrease in motion induced artifacts including motion blur and streak artifacts from contrast enhanced vessels within the targeted ROIs, as assessed through both qualitative and quantitative observations. Conclusion Temporally targeted reconstruction techniques demonstrate the potential to reduce motion artifacts in coronary CT. Further study is warranted to demonstrate the conditions under which this technique will offer direct clinical utility. Improvement in temporal resolution for gated cardiac scans has implications for improving: contrast enhanced CT angiography, calcium scoring, and assessment of the pulmonary anatomy.

The High Resolution Array (HiRA) is a large solid-angle array of silicon strip-detectors that has been developed for use in a variety of nuclear structure, nuclear astrophysics and nuclear reaction experiments with short lived beta-unstable beams. It consists of 20 identical telescopes each composed of a thin ( 65 μ m ) single-sided silicon strip-detector, a thick (1.5 mm) double-sided silicon strip-detector, and four CsI(Tl) crystals read out by photodiodes. The array can be easily configured to meet the detection requirements of specific experiments. To process the signals from the 1920 strips in the array, an Application Specific Integrated Circuit (ASIC) was developed. The design and performance characteristics of HiRA are described.

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 ( 2 π ) , the short scan mode ( π + full fan angle), and the supershort scan mode [less than ( π + 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 dependencies of the light output response of CsI(Tl) crystals for various charged particle beams are investigated. Measurements were performed using5.5 MeV 241 Am alpha particles, 220 MeV alpha and 110 MeV deuteron beams from the K500 Cyclotron at Texas AM 29.90.+r

Truncation artifacts in CT occur if the object to be imaged extends past the scanner field of view (SFOV). These artifacts impede diagnosis and could possibly introduce errors in dose plans for radiation therapy. Several approaches exist for correcting truncation artifacts, but existing correction algorithms do not accurately recover the skin line (or support) of the patient, which is important in some dose planning methods. The purpose of this paper was to develop an iterative algorithm that recovers the support of the object.The authors assume that the truncated portion of the image is made up of soft tissue of uniform CT number and attempt to find a shape consistent with the measured data. Each known measurement in the sinogram is interpreted as an estimate of missing mass along a line. An initial estimate of the object support is generated by thresholding a reconstruction made using a previous truncation artifact correction algorithm (e.g., water cylinder extrapolation). This object support is iteratively deformed to reduce the inconsistency with the measured data. The missing data are estimated using this object support to complete the dataset. The method was tested on simulated and experimentally truncated CT data.The proposed algorithm produces a better defined skin line than water cylinder extrapolation. On the experimental data, the RMS error of the skin line is reduced by about 60%. For moderately truncated images, some soft tissue contrast is retained near the SFOV. As the extent of truncation increases, the soft tissue contrast outside the SFOV becomes unusable although the skin line remains clearly defined, and in reformatted images it varies smoothly from slice to slice as expected.The support recovery algorithm provides a more accurate estimate of the patient outline than thresholded, basic water cylinder extrapolation, and may be preferred in some radiation therapy applications.

State of the art automatic exposure control modulates the tube current across view angle and Z based on patient anatomy for use in axial full scan reconstructions. Cardiac CT, however, uses a fundamentally different image reconstruction that applies a temporal weighting to reduce motion artifacts. This paper describes a phase based mA modulation that goes beyond axial and ECG modulation; it uses knowledge of the temporal view weighting applied within the reconstruction algorithm to improve dose efficiency in cardiac CT scanning. Using physical phantoms and synthetic noise emulation, we measure how knowledge of sinogram temporal weighting and the prescribed cardiac phase can be used to improve dose efficiency. First, we validated that a synthetic CT noise emulation method produced realistic image noise. Next, we used the CT noise emulation method to simulate mA modulation on scans of a physical anthropomorphic phantom where a motion profile corresponding to a heart rate of 60 beats per minute was used. The CT noise emulation method matched noise to lower dose scans across the image within 1.5% relative error. Using this noise emulation method to simulate modulating the mA while keeping the total dose constant, the image variance was reduced by an average of 11.9% on a scan with 50 msec padding, demonstrating improved dose efficiency. Radiation dose reduction in cardiac CT can be achieved while maintaining the same level of image noise through phase based dose modulation that incorporates knowledge of the cardiac reconstruction algorithm.

In cone-beam CT the range of projection views measured for each given image voxel is spatially variant. In the corners of the image volume there is less projection data available to be used by the image reconstruction algorithm, due to data truncation in the z direction (i.e. along the scanner axis). Given the desire to increase the fraction of the voxels which may be reconstructed from a given scan there is a desire to incorporate some extrapolated data into the image reconstruction procedure. In this work one approach is described which consists of a two-pass procedure where the first pass image reconstruction is performed over a larger extent in the z direction, a non-linear transform is applied to the initial reconstruction and a forward projection is applied in order to estimate the extrapolated image data. Initial results are presented which compare the method to zeroth order extrapolation and demonstrate that improvement in the reconstruction of the corner regions with a simple numerical phantom and with anatomical phantom data from a prototype wide coverage CT system.

Truncation artifacts arise when the object being imaged extends past the scanned field of view (SFOV). The line integrals which lie beyond the SFOV are unmeasured, and reconstruction with traditional filtered backprojection (FBP) produces bright signal artifacts at the edge of the SFOV and little useful information outside the SFOV. A variety of techniques have been proposed to correct for truncation artifacts by estimating the unmeasured rays. We explore an alternative, iterative correction technique that reduces the artifacts and recovers the support (or outline) of the object that is consistent with the measured rays. We assume that the support is filled uniformly with tissue of a given CT number (for example, water-equivalent soft tissue) and segment the region outside the SFOV in a dichotomous fashion into tissue and air. In general, any choice for the object support will not be consistent with the measured rays in that a forward projection of the image containing the proposed support will not match the measured rays. The proposed algorithm reduces this inconsistency by deforming the object support to better match the measured rays. We initialize the reconstruction using the water cylinder extrapolation algorithm, an existing truncation artifact correction technique, but other starting algorithms can be used. The estimate of the object support is then iteratively deformed to reduce the inconsistency with the measured rays. After several iterations, forward projection is used to estimate the missing rays. Preliminary results indicate that this iterative, support recovery technique is able to produce superior reconstructions in the case of significant truncation compared to water cylinder extrapolation.

BACKGROUND: Flat-panel cone-beam computed tomography (FP-CBCT) has recently been introduced as a clinical feature in neuroangiography radiographic C-arm systems. OBJECTIVE: To introduce a method of positioning a surgical tool such as a needle or ablation probe within a target specified by intraoperative FP-CBCT scanning. METHODS: Two human cadaver and 2 porcine cadaver heads were injected with a mixture of silicone and contrast agent to simulate a contrast-enhanced tumor. Preoperative imaging was performed using a standard 1.5-T magnetic resonance imaging scanner. Intraoperative imaging was used to define the needle trajectory on a GE Innova 4100 flat panel-based neuroangiography C-arm system. RESULTS: Using a combination of FP-CBCT and fluoroscopy, a needle was successfully positioned within each of the simulated contrast-enhanced tumors, as verified by subsequent FP-CBCT scans. CONCLUSIONS: This proof-of-concept study demonstrates the potential utility of combining FP-CBCT scanning with fluoroscopy to position surgical tools when stereotactic devices and image-guided surgery systems are not available. However, further work is required to fully characterize the precision and accuracy of the method in a variety of realistic surgical sites.

Micro-CT scanning has become an accepted standard for anatomical imaging in small animal disease and genome mutation models. Concurrently, perfusion imaging via tracking contrast dynamics after injection of an iodinated contrast agent is a well-established tool for clinical CT scanners. However, perfusion imaging is not yet commercially available on the micro-CT platform due to limitations in both radiation dose and temporal resolution. Recent hardware developments in micro-CT scanners enable continuous imaging of a given volume through the use of a slip-ring gantry. Now that dynamic CT imaging is feasible, data may be acquired to measure tissue perfusion using a micro-CT scanner (CT Imaging, Erlangen, Germany). However, rapid imaging using micro-CT scanners leads to high image noise in individual time frames. Using the standard filtered backprojection (FBP) image reconstruction, images are prohibitively noisy for calculation of voxel-by-voxel perfusion maps. In this study, we apply prior image constrained compressed sensing (PICCS) to reconstruct images with significantly lower noise variance. In perfusion phantom experiments performed on a micro-CT scanner, the PICCS reconstruction enabled a reduction to 1/16 of the noise variance of standard FBP reconstruction, without compromising the spatial or temporal resolution. This enables a significant increase in dose efficiency, and thus, significantly less exposure time is needed to acquire images amenable to perfusion processing. This reduction in required irradiation time enables voxel-by-voxel perfusion maps to be generated on micro-CT scanners. Sample perfusion maps using a deconvolution-based perfusion analysis are included to demonstrate the improvement in image quality using the PICCS algorithm.

The Prior Image Constrained Compressed Sensing (PICCS) algorithm (Med. Phys. 35, pg. 660, 2008) has been applied to several computed tomography applications with both standard CT systems and flat-panel based systems designed for guiding interventional procedures and radiation therapy treatment delivery. The PICCS algorithm typically utilizes a prior image which is reconstructed via the standard Filtered Backprojection (FBP) reconstruction algorithm. The algorithm then iteratively solves for the image volume that matches the measured data, while simultaneously assuring the image is similar to the prior image. The PICCS algorithm has demonstrated utility in several applications including: improved temporal resolution reconstruction, 4D respiratory phase specific reconstructions for radiation therapy, and cardiac reconstruction from data acquired on an interventional C-arm. One disadvantage of the PICCS algorithm, just as other iterative algorithms, is the long computation times typically associated with reconstruction. In order for an algorithm to gain clinical acceptance reconstruction must be achievable in minutes rather than hours. In this work the PICCS algorithm has been implemented on the GPU in order to significantly reduce the reconstruction time of the PICCS algorithm. The Compute Unified Device Architecture (CUDA) was used in this implementation.

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

C-arm based cone-beam CT (CBCT) has evolved into a routine clinical imaging modality to provide threedimensional tomographic image guidance before, during, and after an interventional procedure. It is often used to update the clinician to the state of the patient anatomy and interventional tool placement. Due to the repeatedly use of CBCT, the accumulated radiation dose in an interventional procedure has become a concern. There is a strong desire from both patients and health care providers to reduce the radiation exposure required for these exams. The overall objective of this work is to propose and validate a method to significantly reduce the total radiation dose used during a CBCT image guided intervention. The basic concept is that the first cone-beam CT scan acquired at the full dose will be used to constrain the reconstruction of the later CBCT scans acquired at a much lower radiation dose. A recently developed new image reconstruction algorithm, Prior Image Constrained Compressed Sensing (PICCS), was used to reconstruct subsequent CBCT images with lower dose. This application differs from other applications of the PICCS algorithm, such as time-resolved CT or fourdimensional CBCT (4DCBCT), because the patient position may be frequently changed from one CBCT scan to another during the procedure. Thus, an image registration step to account for the change in patient position is indispensable for use of the PICCS image reconstruction algorithm. In this paper, the image registration step is combined with the PICCS algorithm to enable radiation dose reduction in CBCT image guided interventions. Experimental results acquired from a clinical C-arm system using a human cadaver were used to validate the PICCS algorithm based radiation dose reduction scheme. Using the proposed method in this paper, it has been demonstrated that, instead of 300 view angles, this technique requires about 20 cone-beam view angles to reconstruct CBCT angiograms. This signals a radiation dose reduction by a factor of approximately fifteen for subsequent acquisitions.

Purpose: To achieve three dimensional isotropic dynamic cardiac CT imaging with high temporal resolution for evaluation of cardiac function with a slowly rotating C-arm system. Method and Materials: A recently introduced extension to compressed sensing, viz. Prior Image Constrained Compressed Sensing (PICCS), in which a prior image is used as a constraint in the reconstruction has enabled this application. An in-vivo animal experiment (e.g. a beagle model) was conducted using an interventional C-arm system. The imaging protocol was as follows: contrast was injected, the contrast equilibrated, breathing was suspended for ~14 seconds during which time 420 equally spaced projections were acquired. This data set was used to reconstruct a fully sampled blurred image volume using the conventional FDK algorithm (e.g. the prior image). Then the data set was retrospectively gated into 19 phases according to the recorded ECG signal (heart rate ~ 95bpm) and images were reconstructed with the PICCS algorithm. Results: Cardiac MR was used as the gold standard due to its high temporal resolution. The same short-axis slice was selected from the PICCS-CT data set and the MR data set. Manual contouring on the peak systolic and peak diastolic frames was performed to assess the ejection fraction contribution from this single plane. The calculated ejection fractions with PICCS-CT agreed well with the MR results. Conclusion: We have demonstrated the ability to use a slowly rotating interventional C-arm system in order to make measurements of cardiac function. The new technique provides high isotropic spatial resolution (~0.5 mm) along with high temporal resolution (~ 33 ms). The evaluation of cardiac function demonstrated a great agreement with single slice cardiac MR.

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they provide accurate physical noise modeling. The newly developed compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. In x-ray CT reconstructions, the CS algorithm can be implemented in the statistical reconstruction framework. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least square and q-GGMRF) to the CS algorithm. In assessing the image quality using these non-linear reconstructions it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. A quality factor which accounts for the noise performance and the spatial resolution was introduced to objectively evaluate the performance of the algorithm under two conditions: 1) constant undersampling factor comparing different algorithms at different dose levels and 2) varying undersampling factors and dose levels for the CS algorithm. To facilitate this comparison the original CS method was also formulated in the framework of the statistical image reconstruction algorithm. This is also a novel aspect of this work. Important conclusions of the measurements are that: for realistic anatomy over 100 projections are needed to avoid streak artifacts even with CS reconstruction, regardless of the algorithm employed it is beneficial to distribute the total dose to many views as long as each view remains quantum noise limited, and the CS method is not appropriate for low dose levels because while it can mitigate streaking artifacts the images being to exhibit a patchy behavior.

The current x-ray source trajectory for C-arm based cone-beam CT is a single arc. Reconstruction from data acquired with this trajectory yields cone-beam artifacts for regions other than the central slice. In this work we present the preliminary evaluation of reconstruction from a source trajectory of two concentric arcs using a flat-panel detector equipped C-arm gantry (GE Healthcare Innova 4100 system, Waukesha, Wisconsin). The reconstruction method employed is a summation of FDK-type reconstructions from the two individual arcs. For the angle between arcs studied here, 30°, this method offers a significant reduction in the visibility of cone-beam artifacts, with the additional advantages of simplicity and ease of implementation due to the fact that it is a direct extension of the reconstruction method currently implemented on commercial systems. Reconstructed images from data acquired from the two arc trajectory are compared to those reconstructed from a single arc trajectory and evaluated in terms of spatial resolution, low contrast resolution, noise, and artifact level.

Recently, foundational mathematical theory, compressed sensing (CS), has been developed which enables accurate reconstruction from greatly undersampled frequency information (Candes et. al. and Donoho). Using numerical phantoms it has been demonstrated that CS reconstruction (e.g. minimizing the ℓ1 norm of the discrete gradient of the image) offers promise for computed tomography. However, when using experimental CT projection data the undersampling factors enabled were smaller than in numerical simulations. An extension to CS has recently been proposed wherein a prior image is utilized as a constraint in the image reconstruction procedure (i.e. Prior Image Constrained Compressed Sensing - PICCS). Experimental results are demonstrated here from a clinical C-arm system, highlighting one application of PICCS in reducing radiation exposure during interventional procedures while preserving high image quality. In this study a range of view angles has been investigated from very limited angle aquisitions (e.g. tomosythesis) to undersampled CT acquisitions.

In this paper, we present shift-invariant filtered backprojection (FBP) cone-beam image reconstruction algorithms for a cone-beam CT system based on a clinical C-arm gantry. The source trajectory consists of two concentric arcs which is complete in the sense that the Tuy data sufficiency condition is satisfied. This scanning geometry is referred to here as a CC geometry (each arc is shaped like the letter "C"). The challenge for image reconstruction for the CC geometry is that the image volume is not well populated by the familiar doubly measured (DM) lines. Thus, the well-known DM-line based image reconstruction schemes are not appropriate for the CC geometry. Our starting point is a general reconstruction formula developed by Pack and Noo which is not dependent on the existence of DM-lines. For a specific scanning geometry, the filtering lines must be carefully selected to satisfy the Pack-Noo condition for mathematically exact reconstruction. The new points in this paper are summarized here. (1) A mathematically exact cone-beam reconstruction algorithm was formulated for the CC geometry by utilizing the Pack-Noo image reconstruction scheme. One drawback of the developed exact algorithm is that it does not solve the long-object problem. (2) We developed an approximate image reconstruction algorithm by deforming the filtering lines so that the long object problem is solved while the reconstruction accuracy is maintained. (3) In addition to numerical phantom experiments to validate the developed image reconstruction algorithms, we also validate our algorithms using physical phantom experiments on a clinical C-arm system.

There exists a strong desire for a platform in which researchers may investigate planar tomosynthesis (i.e. all source positions reside in a single plane that is parallel to the reconstructed image planes) trajectories directly on an interventional C-arm system. In this work we describe an experimental system designed to accomplish this aim, as well as the potential of this system for testing multiple aspects of the tomosynthetic image acquisition process. The system enables one to evaluate the effect of the physical imaging parameters on the image quality, as well as the effect of the reconstruction algorithm utilized. The experimental data collection for this work is from the Innova 4100 (Flat-panel based interventional C-arm system manufactured by GE Healthcare). The system is calibrated using a phantom with known geometrical placement of multiple small metallic spheres. Initial performance was assessed with three physical phantoms and performance was assessed by varying: the reconstruction algorithm (backprojection, filtered backprojection), the half tomographic angle (15°, 25°, 35°), and the angular sampling (20,40,80 views / acquisition). Initial results demonstrate the ability to well differentiate simulated vessels separated by 1 cm, even with the modest half tomographic angle of 15° and modest sampling of 20 views/acquisition.

A framework for image reconstruction from planar tomosynthesis trajectories (i.e. all source positions reside in a single plane) is presented. The parallel beam geometry is a convenient starting point in deriving reconstruction algorithms, both analytic and iterative, as the relation between frequency space and image space is well known. We present a method for utilizing parallel beam reconstruction algorithms in an internally consistent manner. The concept of a virtual image object is utilized. This virtual image object has the property that cone-beam projections through the real object are directly related to parallel-beam projections of the virtual object. The virtual object may then be reconstructed using any algorithm derived for parallel beam projections. Finally, an affine transform may be applied to the virtual image object in order to generate the reconstruction result. In the implementation described here the backprojection operation is performed such that the real image object is reconstructed without introducing a rebinning in image space. Image reconstruction comparisons are given for a standard filtered backprojection (FBP) type algorithm where parallel projections were assumed in the algorithm derivation. Numerical simulations were performed for a C-arm type geometry and parallel beam FBP reconstructions using the virtual object are compared with the standard backprojection algorithm. Finally, a comparison was made between the new parallel beam reconstruction and the standard approximation where the cone-beams are assumed to be approximately parallel beams and a cone-beam backprojection is employed. A reduction in streaking artifacts was observed using the new algorithm compared with the standard approximation.

Existing cardiac imaging methods do not allow for improved temporal resolution when considering a targeted region of interest (ROI). The imaging method presented here enables improved temporal resolution for ROI imaging (namely, a reconstruction volume smaller than the complete field of view). Clinically, temporally targeted reconstruction would not change the primary means of reconstructing and evaluating images, but rather would enable the adjunct technique of ROI imaging, with improved temporal resolution compared with standard reconstruction ( approximately 20% smaller temporal scan window). In gated cardiac computed tomography (CT) scans improved temporal resolution directly translates into a reduction in motion artifacts for rapidly moving objects such as the coronary arteries.Retrospectively electrocardiogram gated coronary angiography data from a 64-slice CT system were used. A motion phantom simulating the motion profile of a coronary artery was constructed and scanned. Additionally, an in vivo study was performed using a porcine model. Comparisons between the new reconstruction technique and the standard reconstruction are given for an ROI centered on the right coronary artery, and a pulmonary ROI.In both a well-controlled motion model and a porcine model results show a decrease in motion induced artifacts including motion blur and streak artifacts from contrast enhanced vessels within the targeted ROIs, as assessed through both qualitative and quantitative observations.Temporally targeted reconstruction techniques demonstrate the potential to reduce motion artifacts in coronary CT. Further study is warranted to demonstrate the conditions under which this technique will offer direct clinical utility. Improvement in temporal resolution for gated cardiac scans has implications for improving: contrast enhanced CT angiography, calcium scoring, and assessment of the pulmonary anatomy.

Motion contamination in computed tomography projection data causes significant artifacts in the reconstructed images. If during the tomographic acquisition the object is relatively stationary during a portion of the acquisition, and then moves significantly, the projection data after the motion will be inconsistent with the projection data during the period of relative stationarity. The fan-beam data consistency condition (FDCC) provides a means to directly estimate motion contaminated projection data based on all of the projection data acquired. Thus, the FDCC may be used to combat many types of motion contamination in computed tomography. This approach to motion artifact correction is novel as none of the previous methods for artifact correction utilized a direct estimation of motion contaminated data. Additionally, this methodology depends upon only a small amount of a priori information and is not based on a motion model. Another distinguishing feature of this method is that it operates directly in the projection space, and is completely independent of the reconstruction algorithm utilized. An example of clinical relevance of coronary motion artifact reduction is presented using both simulated projection data as well as projection data acquired with a porcine model using a state-of-the-art 64 row volumetric CT scanner. Significant reduction in motion related artifacts is achieved in both the simulation case and the porcine model.

In this study we develop a novel ECG-gated method of HYPR (HighlY constrained backPRojection) CT reconstruction for low-dose myocardial perfusion imaging and present its first application in a porcine model. HYPR is a method of reconstructing time-resolved images from view-undersampled projection data. Scanning and reconstruction techniques were explored using x-ray projections from a 50 sec contrast-enhanced axial scan of a 47 kg swine on a 64-slice MDCT system. Scans were generated with view undersampling factors from 2 to 10. A HYPR reconstruction algorithm was developed in which a fully-sampled composite image is generated from views collected from multiple cardiac cycles within a diastolic window. A time frame image for a heartbeat was produced by modifying the composite with projections from the cycle of interest. Heart rate variations were handled by automatically selecting cardiac window size and number of cycles per composite within defined limits. Cardiac window size averaged 35% of the R-R interval for 2x undersampling and increased to 64% R-R using 10x undersampling. The selected window size and cycles per composite was sensitive to synchrony between heart rate, gantry rate, and the view undersampling pattern. Temporal dynamics and perfusion metrics measured in conventional short-scan (FBP) images were well-reproduced in the undersampled HYPR time series. Mean transit times determined from HYPR myocardial time-density curves agreed to within 8% with the FBP results. The results indicate potential for an order of magnitude reduction in dose requirement per image in cardiac perfusion CT via undersampled scanning and ECG-gated HYPR reconstruction.

In this paper, a shift-invariant filtered backprojection cone-beam image reconstruction algorithm is derived, based upon Katsevich's general inversion scheme, and validated for the source trajectory of two concentric circles. The source trajectory is complete according to Tuy's data sufficiency condition and is used as the basis for an exact image reconstruction algorithm. The algorithm proceeds according to the following steps. First, differentiate the cone-beam projection data with respect to the detector coordinates and with respect to the source trajectory parameter. The data are then separately filtered along three different orientations in the detector plane with a shift-invariant Hilbert kernel. Eight different filtration groups are obtained via linear combinations of weighted filtered data. Voxel-based backprojection is then carried out from eight sets of view angles, where separate filtered data are backprojected from each set according to the backprojection sets' associated filtration group. The algorithm is first derived for a scanning configuration consisting of two concentric and orthogonal circles. By performing an affine transformation on the image object, the developed image reconstruction algorithm has been generalized to the case where the two concentric circles are not orthogonal. Numerical simulations are presented to validate the reconstruction algorithm and demonstrate the dose advantage of the equal weighting scheme.

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.

When an imaging task is specified, the design of a cone-beam CT scanner includes specifications of the scanning trajectory and corresponding image reconstruction algorithms, requirements on the detector size, and requirements on the x-ray tubes. Given the limited flat-panel detector readout speed and the need of short scanning time in a clinical setting, the available number of total view angles is normally limited to several hundred. It is known that when all the focal spots are distributed along a circular trajectory, the cone-beam artifacts are present in the reconstructed out-of-plane images when the cone-angle is relatively large. In order to mitigate or eliminate the cone-beam artifacts, the source trajectory should be complete in the sense of satisfying the so-called Tuy data sufficiency condition. However, assuming a constant number of view angles, a complete source trajectory will potentially lead to a lower view sampling rate and cause view aliasing artifacts. Therefore, for a given imaging task and a given total number of view angles, it is important to study the tradeoff between the view sampling rate and the completeness of the scanning source trajectories. In this paper, we numerically and experimentally studied the above tradeoff. Specifically, numerical simulations were conducted to study this tradeoff using three different source trajectories: (1) a circular trajectory, (2) a helical trajectory, and (3) a two-concentric-orthogonal-circle trajectory. A single x-ray tube and a flat panel imager mounted on an optical bench was utilized to experimentally study the tradeoff between the circular source trajectory and the helical source trajectory. For the complete source trajectories, some novel cone-beam image reconstruction algorithms have been utilized to reconstruct images and compare image quality in numerical simulations and benchtop experiments.

X-ray cone-beam computed tomography (CBCT) is of importance in image-guided intervention (IGI) and image-guided radiation therapy (IGRT). In this paper, we present a cone-beam CT data acquisition system using a GE INNOVA 4100 (GE Healthcare Technologies, Waukesha, Wisconsin) clinical system. This new cone-beam data acquisition mode was developed for research purposes without interfering with any clinical function of the system. It provides us a basic imaging pipeline for more advanced cone-beam data acquisition methods. It also provides us a platform to study and overcome the limiting factors such as cone-beam artifacts and limiting low contrast resolution in current C-arm based cone-beam CT systems. A geometrical calibration method was developed to experimentally determine parameters of the scanning geometry to correct the image reconstruction for geometric non-idealities. Extensive phantom studies and some small animal studies have been conducted to evaluate the performance of our cone-beam CT data acquisition system.

A novel exact fan-beam image reconstruction formula is presented and validated using both mathematical phantom data and clinical data. This algorithm takes the form of the standard ramp filtered backprojection (FBP) algorithm plus local compensation terms. 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 (2 ), the short scan mode ( + full fan angle), and the super-short scan mode (less than ( + full fan angle)). Another desirable feature of this algorithm is that it is derivative-free. The derivative-free nature of thi s algorithm distinguishes it from other exact fan-beam FBP algorithms. Key Words : fan-beam, artifacts, super-short, FBP I. INTRODUCTION In x-ray computed tomography (CT), the fan beam filtered backprojection (FBP) image reconstruction algorithm has been used extensively due to the fact that it is computatio nally efficient. The standard FBP algorithm was derived for a full scan (angular range =2 ) along a circular trajectory. The desire for faster acquisition and improved temporal resolution led to the discovery of the short scan mode (angular range = +full fan angle) which uses standard FBP algorithm with Parker’s weighting scheme

In this paper, we present a new algorithm designed for a specific data truncation problem in fan-beam CT. We consider a scanning configuration in which the fan-beam projection data are acquired from an asymmetrically positioned half-sized detector. Namely, the asymmetric detector only covers one half of the scanning field of view. Thus, the acquired fan-beam projection data are truncated at every view angle. If an explicit data rebinning process is not invoked, this data acquisition configuration will reek havoc on many known fan-beam image reconstruction schemes including the standard filtered backprojection (FBP) algorithm and the super-short-scan FBP reconstruction algorithms. However, we demonstrate that a recently developed fan-beam image reconstruction algorithm which reconstructs an image via filtering a backprojection image of differentiated projection data (FBPD) survives the above fan-beam data truncation problem. Namely, we may exactly reconstruct the whole image object using the truncated data acquired in a full scan mode (2pi angular range). We may also exactly reconstruct a small region of interest (ROI) using the truncated projection data acquired in a short-scan mode (less than 2pi angular range). The most important characteristic of the proposed reconstruction scheme is that an explicit data rebinning process is not introduced. Numerical simulations were conducted to validate the new reconstruction algorithm.

In this paper, a new image reconstruction scheme is presented based on Tuy's cone-beam inversion scheme and its fan-beam counterpart. It is demonstrated that Tuy's inversion scheme may be used to derive a new framework for fan-beam and cone-beam image reconstruction. In this new framework, images are reconstructed via filtering the backprojection image of differentiated projection data. The new framework is mathematically exact and is applicable to a general source trajectory provided the Tuy data sufficiency condition is satisfied. By choosing a piece-wise constant function for one of the components in the factorized weighting function, the filtering kernel is one dimensional, viz. the filtering process is along a straight line. Thus, the derived image reconstruction algorithm is mathematically exact and efficient. In the cone-beam case, the derived reconstruction algorithm is applicable to a large class of source trajectories where the pi-lines or the generalized pi-lines exist. In addition, the new reconstruction scheme survives the super-short scan mode in both the fan-beam and cone-beam cases provided the data are not transversely truncated. Numerical simulations were conducted to validate the new reconstruction scheme for the fan-beam case.

Conventionally, the FDK algorithm is used to reconstruct images from cone-beam projections in many imaging systems. One advantage of this algorithm is its shift-invariant feature in the filtering process. In this paper, a new cone-beam reconstruction algorithm is derived for a single arc source trajectory. Examples of the arc trajectory include the full circular scan mode, a short-scan mode and a super-short-scan mode depending upon the angular range of the scanning path. Since the single arc does not satisfy Tuy's data sufficiency condition, there is no mathematically exact algorithm. However, one advantage of this reconstruction is that the shift-invariance property has been preserved despite the lack of a mathematically complete data set. The new algorithm includes backprojections from three adjacent segments of the arc defined by T1(vector x), T2(vector x) and T3(vector x). Each backprojection step consists of a weighted combination of 1D Hilbert filtering of the modified cone-beam data along horizontal and non-horizontal directions. The non-horizontal filtering is a new feature of this FBP algorithm. For the full circle scanning path, this algorithm reduces to the conventional FDK algorithm plus a term involving a first order derivative filter. Numerical simulations have been performed to validate the algorithm.

The feasibility of making regional perfusion measurements using a tomosynthetic digital subtraction angiography (TDSA) acquisition has been demonstrated. The study of tomosynthetic perfusion measurements was motivated by the clinical desire for perfusion measurements in an interventional angiography suite. These pilot studies were performed using the scanning-beam digital x-ray (SBDX) system which is an inverse-geometry imaging device which utilizes an electromagnetically-scanned x-ray source, and a small CdTe direct conversion photon counting detector. The scanning electron source was used to acquire planar-tomographic images of a 12.5 x 12.5 cm field of view at a frame rate of 15 frames/sec during dynamic contrast injection. A beagle animal model was used to evaluate the tomosynthetic perfusion measurements. A manual bolus injection of iodinated contrast solution was used in order to resolve the parameters of the contrast pass curve. The acquired planar tomosynthetic dataset was reconstructed with a simple back-projection algorithm. Digital subtraction techniques were used to visualize the change in contrast agent intensity in each reconstructed plane. Given the TDSA images, region of interest based analysis was used in the selection of the image pixels corresponding to the artery and tissue bed. The mean transit time (MTT), regional cerebral blood volume (rCBV) and regional cerebral blood flow (rCBF) were extracted from the tomosynthetic data for selected regions in each of the desired reconstructed planes. For the purpose of this study, the arterial contrast enhancement curve was fit with a combination of gamma variate terms, and the MTT was calculated using a deconvolution based on the singular value decomposition (SVD). The results of the contrast pass curves derived with TDSA were consistent with the results from perfusion measurements as implemented with CT acquisition.

Purpose: Truncation artifacts in CT occur if the object to be imaged extends past the scanner field of view (SFOV). These artifacts impede diagnosis and could possibly introduce errors in dose plans for radiation therapy. Several approaches exist for correcting truncation artifacts, but existing correction algorithms do not accurately recover the skin line (or support) of the patient, which is important in some dose planning methods. The purpose of this paper was to develop an iterative algorithm that recovers the support of the object. Methods: The authors assume that the truncated portion of the image is made up of soft tissue of uniform CT number and attempt to find a shape consistent with the measured data. Each known measurement in the sinogram is interpreted as an estimate of missing mass along a line. An initial estimate of the object support is generated by thresholding a reconstruction made using a previous truncation artifact correction algorithm (e.g., water cylinder extrapolation). This object support is iteratively deformed to reduce the inconsistency with the measured data. The missing data are estimated using this object support to complete the dataset. The method was tested on simulated and experimentally truncated CT data. Results: The proposed algorithm produces a better defined skin line than water cylinder extrapolation. On the experimental data, the RMS error of the skin line is reduced by about 60%. For moderately truncated images, some soft tissue contrast is retained near the SFOV. As the extent of truncation increases, the soft tissue contrast outside the SFOV becomes unusable although the skin line remains clearly defined, and in reformatted images it varies smoothly from slice to slice as expected. Conclusions: The support recovery algorithm provides a more accurate estimate of the patient outline than thresholded, basic water cylinder extrapolation, and may be preferred in some radiation therapy applications.

Purpose: Recently an extension to compressed sensing theory has been proposed in which a prior image is used to constrain the reconstruction process. This new algorithm may be applied to gated CT acquisitions where a fully sampled ‘blurred’ image is used as the prior image to constrain imagesreconstructed at each phase. Our hypothesis is that the PICCS algorithm can accurately track moving objects throughout a given cyclic motion profile. The purpose of this study is to experimentally verify this hypothesis. Method and Materials: The PICCS algorithm was implemented on a clinical C‐arm system (GE Innova 4100). A phantom was constructed using human bones to simulate a realistic background of ribs and vertebrae. A 3 mm plastic rod was scanned along a one dimensional motion profile. The motion profile had a period of 0.8sec with a resting phase of 0.2 sec, and an amplitude of 8 mm. 420 cone‐beam projections were acquired using a 14 second data acquisition time over 210 degrees. PICCS reconstructed was applied to the gated data such that 25 phases were reconstructed.Results: The center of mass of the moving rod was calculated based on the reconstructed images and agrees well with the programmed motion profile for all points in the simulated motion profile. Intensity plots are given comparing the gated PICCS reconstruction to the standard filtered backprojection algorithm. The PICCS reconstruction results also faithfully depict the width of the moving objects. When the width of the gating window in the PICCS algorithm is increased the width of the reconstructed moving object increases. Conclusion: This study demonstrates that for moving objects PICCS has the ability to reconstructimages with simultaneous high spatial resolution and temporal resolution via gated reconstruction. PICCS has the potential to improve reconstruction for gated acquisitions such as cardiac and lungimaging.

Purpose: Radiation dose becomes more of an issue for contrast enhanced breast Cone Beam CT than regular breast CBCT, due to the multiple scans required in order to trace the contrast uptake curve. In this paper, we report a novel scheme to reduce noise variance for each individual breast cone beam CT scan by a factor of about 20. As a result, radiation dose reduction can be achieved by lowering the x‐ray tube current. Method and Materials: The enabling technology is Prior Image Constrained Compressed Sensing (PICCS) method. A prior image with significantly low noise is generated by: (1) first low pass filtering the projection data along the z‐direction which is perpendicular to the patient table top, and: (2) then reconstructing the prior image volume with the standard FDK algorithm. Then the entire cone‐beam CTimage volume is reconstructed by applying the above described PICCS algorithm to the natively measured cone‐beam projection data. The method was validated using CBCT datasets acquired with a dedicated Breast CT scanner. Phantom experiments as well as a contrast enhanced breast cone beam CT exam were performed to demonstrate the dramatic noise reduction using the new scheme. Results: For both phantom experiments and the human subject exam, PICCS image considerably reduces the noise and enhances the low contrast visibility.Noise variance measurements shows that a noise reduction factor of about 20 was achieved. Conclusion: The proposed method demonstrates a noise reduction ratio of more than 20 and the potential of significant dose reduction for breast CBCT by an order of ten. For an accepted noise variance level, the new scheme can be applied to enable more than 10 cone beam CT scans in contrast enhance breast CTimaging while the total radiation dose level is maintained at the two‐view mammography level.

Purpose: To describe a method of low dose CT myocardial perfusion imaging based on an angularly under‐sampled projection acquisition and PICCS image reconstruction, and evaluate its performance in a preliminary porcine study. Method and Materials: Recently an extension to compressed sensing has been proposed in which a prior image is utilized as a constraint in the image reconstruction (i.e. Prior Image Constrained Compressed Sensing — PICCS). In this case the prior image is reconstructed with a short scan about 3 seconds before contrast arrives. Using the PICCS algorithm significant under‐sampling of the subsequent time frames is feasible, which would yield a direct dose savings if a pulsed x‐ray tube is used. In order to demonstrate the potential dose savings, a contrast enhanced porcine scan was performed on a 64 slice MDCT system. Results: Three regions‐of‐interest were identified in the myocardium adjacent to the left ventricle. The ability of the under‐sampled PICCS acquisitions to portray the temporal enhancement dynamics seen in full‐dose, fully‐sampled dynamic CTimaging was assessed by comparing quantitative perfusion parameters derived from time‐density curves. For dose reduction factors from 4.3 to 12.8, the average error in the time to peak ranged from −3% to 2.5%, and the average error in upslope parameter ranged from −14% to 7.5%. Conclusion: The results of a single porcine study indicate the potential for significant dose reduction (e.g. an order of magnitude) using a gated pulsed acquisition with respect to myocardial perfusion measurements.