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Objective. To address the zero-count problem in low-dose, high-spatial-resolution photon counting detector CT (PCD-CT) without introducing statistical biases or degrading spatial resolution. Approach. The classical approach to generate the sinogram projection data for estimating the line integrals of the linear attenuation coefficients of the image object is to take a log transform of detector counts, which requires zero counts to be replaced by positive numbers. Both the log transform and the zero-count replacement introduce biases. After analyzing the statistical properties of the zero-count replaced pre-log and post-log data, a formula for the statistical sinogram bias was derived, based on which a new sinogram estimator was empirically constructed to cancel the statistical biases. Dose- and object-independent free parameters in the proposed estimator were learned from simulated data, and then the estimator was applied to experimental low-dose PCD-CT data of physical phantoms for validation and generalizability testing. Both bias and noise performances of the proposed method were evaluated and compared with those of previous zero-count correction methods, including zero-weighting, zero-replacement, and adaptive filtration-based methods. The impact of these correction methods on spatial resolution was also quantified using line-pair patterns. Main Results. For all objects and reduced-dose levels, the proposed method reduces the statistical CT number biases to be within ± 10 HU, which is significantly lower than the biases given by the classical zero-count correction methods. The Bland-Altman analysis demonstrated that the proposed correction led to negligible sinogram biases at all attenuation levels, whereas the other correction methods did not. Additionally, the proposed method was found to have no discernible impact on image noise and spatial resolution. Significance. The proposed zero-count correction scheme allows the CT numbers of low-dose, high-spatial-resolution PCD-CT images to match those of standard-dose and standard-resolution PCD-CT images., (Creative Commons Attribution license.)

Non-contrast CT (NCCT) is widely employed as the first-line imaging test to evaluate intracranial hemorrhage (ICH). Advances in mutidetector CT (MDCT) technology have greatly improved the image quality of NCCT for the detection of established, relatively large, and acute ICHs. Meanwhile, the reliability of MDCT in detecting microbleeds and chronic hemorrhage, and in predicting hemorrhagic transformation needs to be further improved. The purpose of this work was to investigate the potential use of non-spectral photon counting CT (PCCT) to address these challenges in ICH imaging. Towards this goal, the NCCT protocol of an experimental PCCT system that simulates the geometry of a general-purpose MDCT was optimized. The optimization was driven by three imaging tasks: detection of a 4.0 mm intraparenchymal hemorrhage, detection of a 1.5 mm subarachnoid hemorrhage, and discrimination of a sulcus in the insular cortex from the parenchymal background. These imaging tasks were custom-built into an anthropomorphic head phantom. Under the guidance of the frequency-dependent noise equivalent quanta and the ideal observer model detectability index [Formula: see text], the optimal PCD detection mode, energy threshold, and reconstruction kernel were found to be the anti-charge sharing mode, 15 keV, and an apodized ramp kernel, respectively. Compared with a clinical MDCT operated with an ICH protocol and at a matched dose level, the PCCT system provided at least 20% improvements in [Formula: see text] for all three ICH imaging tasks. These results demonstrated the potential benefits of non-spectral PCCT in ICH assessment.

While CTA is an established clinical gold standard for imaging large cerebral arteries and veins, an important challenge that currently remains for CTA is its limited performance in imaging small perforating arteries with diameters below 0.5 mm. The purpose of this work was to theoretically and experimentally study the potential benefits of using photon counting detector (PCD)-based CT (PCCT) to improve the performance of CTA in imaging these small arteries. In particular, the study focused on an important component of the CTA image package known as the maximum intensity projection (MIP) image. To help understand how the physical properties of a detector quantitatively influence the MIP image quality, a theoretical model on the statistical properties of MIP images was developed. After validating this model, it was used to explore the individual and joint contribution of the following detector properties to the MIP signal-to-noise ratio (SNR): inter-slice noise covariance, spatial resolution along the z direction, and native pixel pitch along z. The model demonstrated that superior slice sensitivity, reduced inter-slice noise correlation, and smaller native pixel pitch along z provided by PCDs lead to improved vessel SNR in MIP images. Finally, experiments were performed by scanning an anthropomorphic cerebral angiographic phantom using a benchtop PCCT system and a commercial MDCT system. The experimental MIP results consistently demonstrated that compared with MDCT, PCCT provides superior vessel conspicuity and reduced artifactual stenosis.

The zero-frequency detective quantum efficiency (DQE), viz., DQE 0 , is defined as the ratio between output and input squared signal-to-noise ratio of an imaging system. In 1963, R. Shaw applied Fourier analysis to generalize DQE 0 to the frequency-dependent DQE, i.e. DQE[Formula: see text]. Under conditions specified by Shaw, DQE[Formula: see text] is the same as DQE 0 at k  =  0. The experimental measurement of DQE[Formula: see text] involves the measurement of system modulation transfer function (MTF) and noise power spectrum (NPS). Although the measurement of MTF is straightforward, the experimental measurements of NPS[Formula: see text] encountered several challenges. As a result, some experimental methods may yield a nonphysical NPS value at k  =  0, which makes the measured DQE(k)| k=0 deviate from the true zero-frequency DQE. This work presents new results from three aspects: 1) system drift is a significant error source when a large number of independent image acquisitions are involved in measuring NPS and DQE; 2) a cascaded systems analysis shows that the drift induces a global positive offset to the measured autocovariance function, and the offset is quantitatively related to the NPS error at k  =  0; 3) based on the measured autocovariance data, drift-induced offset can be estimated, so that errors in the measured NPS(k)| k=0 and DQE(k)| k=0 can be corrected. Both numerical simulations with known ground truth for DQE 0 and experimental studies were performed to validate the proposed measurement method. The results demonstrated that the method mitigates the undesirable influence of system drift in DQE(k)| k=0 and DQE 0 , allowing the measured values consistent with the classical definition of zero-frequency DQE.

In a recently published paper by Li et al (2018 Phys. Med. Biol. 63 075001), reconstruction parameters were optimized for the so-called synchronized multiArtifact reduction with tomographic reconstruction (SMART-RECON) method to enable time-resolved Cone beam computed tomography angiography (TR-CBCTA) from a single short-scan CBCT data set. However, the paper did not quantitatively address how much temporal resolution can be improved by using the SMART-RECON algorithm in TR-CBCTA. The purpose of this note was to present a method to quantify this temporal resolution improvement factor and to use that method to quantify the improvement in temporal resolution using SMART-RECON and optimized reconstruction parameters. In the method proposed in this note, the potential temporal blurring caused by SMART-RECON was modeled as a convolution between a temporal Gaussian blurring kernel and the ground truth temporal enhancement curve. The width parameter of the resulting Gaussian blurring kernel was used as a surrogate metric for temporal resolution to quantify the achievable temporal resolution for the conventional filtered backprojection (FBP) and SMART-RECON methods. The ratio of the surrogate temporal resolutions for the two reconstruction methods was calculated to quantify the factor of temporal resolution improvement. The quantitative results show that the temporal resolution is improved by a factor of 4.5 using SMART-RECON compared with FBP.

Given that the computed tomography (CT) reconstruction algorithm based on compressed sensing (CS) results in blurred edges, we propose a modified Canny operator that assists the CS algorithm to accurately capture an object's edge, to preserve and further enhance the contrasts in the reconstructed image, thereby improving image quality. We modified two procedures of the traditional Canny operator, namely non-maximum suppression and edge tracking by hysteresis according to the characteristics of low-dose CT reconstruction, and proposed two major modifications: double-response edge detection and directional edge tracking. The newly modified Canny operator was combined with the CS reconstruction algorithm to become an edge-enhanced CS (EECS). Both a 2D Shepp-Logan phantom and a 3D dental phantom were used to conduct reconstruction testing. Root-mean-square error, peak signal-to-noise ratio, and universal quality index were employed to verify the reconstruction results. Qualitative and quantitative results of EECS reconstruction showed its superiority over conventional CS or CS combined with different edge detection techniques, such as Laplacian, Prewitt, Sobel operators, etc. The experiments verified that the proposed modified Canny operator is able to effectively detect the edge location of an object during low-dose reconstruction, enabling EECS to reconstruct images with better quality than those produced by other algorithms.

Inter-pixel communication and anti-charge sharing (ACS) technologies have been introduced to photon counting detector (PCD) systems to address the undesirable charge sharing problem. In addition to improving the energy resolution of PCD, ACS may also influence other aspects of PCD performance such as detector multiplicity (i.e. the number of pixels triggered by each interacted photon) and detective quantum efficiency (DQE). In this work, a theoretical model was developed to address how ACS impacts the multiplicity and zero-frequency DQE [DQE(0)] of PCD systems. The work focused on cadmium telluride (CdTe)-based PCD that often involves the generation and transport of K-fluorescence photons. Under the parallel cascaded systems analysis framework, the theory takes both photoelectric and scattering effects into account, and it also considers both the reabsorption and escape of photons. In a new theoretical treatment of ACS, it was considered as a modified version of the conventional single pixel (i.e. non-ACS) mode, but with reduced charge spreading distance and K-fluorescence travel distance. The proposed theoretical model does not require prior knowledge of the detailed ACS implementation method for each specific PCD, and its parameters can be experimentally determined using a radioisotope without invoking any Monte-Carlo simulation. After determining the model parameters, independent validation experiments were performed using a diagnostic x-ray tube and four different polychromatic beams (from 50 to 120 kVp). Both the theoretical and experimental results demonstrate that ACS increased the first and second moments of multiplicity for a majority of the x-ray energy and threshold levels tested, except when the threshold level was much lower than the x-ray energy level. However, ACS always improved DQE(0) at all energy and threshold levels tested.

Time-resolved C-arm cone-beam CT (CBCT) angiography (TR-CBCTA) images can be generated from a series of CBCT acquisitions that satisfy data sufficiency condition in analytical image reconstruction theory. In this work, a new technique was developed to generate TR-CBCTA images from a single short-scan CBCT data acquisition with contrast media injection. The reconstruction technique enabling this application is a previously developed image reconstruction technique, synchronized multi-artifact reduction with tomographic reconstruction (SMART-RECON). In this new application, the acquired short-scan CBCT projection data were sorted into a union of several sub-sectors of view angles and each sub-sector of view angles corresponds to an individual image volume to be reconstructed. The SMART-RECON method was then used to jointly reconstruct all of these individual image volumes under two constraints: (1) each individual image volume is maximally consistent with the measured cone-beam projection data within the corresponding view angle sector and (2) the nuclear norm of the image matrix is minimized. The difference between these reconstructed individual image volumes is used to generated the desired subtracted angiograms. To validate the technique, numerical simulation data generated from a fractal tree angiogram phantom were used to quantitatively study the accuracy of the proposed method and retrospective in vivo human subject studies were used to demonstrate the feasibility of generating TR-CBCTA in clinical practice.

In recent years, potential applications of energy-resolved photon counting detectors (PCDs) in the x-ray medical imaging field have been actively investigated. Unlike conventional x-ray energy integration detectors, PCDs count the number of incident x-ray photons within certain energy windows. For PCDs, the interactions between x-ray photons and photoconductor generate electronic voltage pulse signals. The pulse height of each signal is proportional to the energy of the incident photons. By comparing the pulse height with the preset energy threshold values, x-ray photons with specific energies are recorded and sorted into different energy bins. To quantitatively understand the meaning of the energy threshold values, and thus to assign an absolute energy value to each energy bin, energy calibration is needed to establish the quantitative relationship between the threshold values and the corresponding effective photon energies. In practice, the energy calibration is not always easy, due to the lack of well-calibrated energy references for the working energy range of the PCDs. In this paper, a new method was developed to use the precise knowledge of the characteristic K-edge energy of materials to perform energy calibration. The proposed method was demonstrated using experimental data acquired from three K-edge materials (viz., iodine, gadolinium, and gold) on two different PCDs (Hydra and Flite, XCounter, Sweden). Finally, the proposed energy calibration method was further validated using a radioactive isotope (Am-241) with a known decay energy spectrum.

The use of grating based Talbot-Lau interferometry permits the acquisition of differential phase contrast (DPC) imaging with a conventional medical x-ray source and detector. However, due to the limited area of the gratings, limited area of the detector, or both, data truncation image artifacts are often observed in tomographic DPC acquisitions and reconstructions, such as tomosynthesis (limited-angle tomography). When data are truncated in the conventional x-ray absorption tomosynthesis imaging, a variety of methods have been developed to mitigate the truncation artifacts. However, the same strategies used to mitigate absorption truncation artifacts do not yield satisfactory reconstruction results in DPC tomosynthesis reconstruction. In this work,several new methods have been proposed to mitigate data truncation artifacts in a DPC tomosynthesis system. The proposed methods have been validated using experimental data of a mammography accreditation phantom, a bovine udder, as well as several human cadaver breast specimens using a bench-top DPC imaging system at our facility.

A patient-specific scatter correction algorithm is proposed to mitigate scatter artefacts in cone-beam CT (CBCT). The approach belongs to the category of convolution-based methods in which a scatter potential function is convolved with a convolution kernel to estimate the scatter profile. A key step in this method is to determine the free parameters introduced in both scatter potential and convolution kernel using a so-called calibration process, which is to seek for the optimal parameters such that the models for both scatter potential and convolution kernel is able to optimally fit the previously known coarse estimates of scatter profiles of the image object. Both direct measurements and Monte Carlo (MC) simulations have been proposed by other investigators to achieve the aforementioned rough estimates. In the present paper, a novel method has been proposed and validated to generate the needed coarse scatter profile for parameter calibration in the convolution method. The method is based upon an image segmentation of the scatter contaminated CBCT image volume, followed by a reprojection of the segmented image volume using a given x-ray spectrum. The reprojected data is subtracted from the scatter contaminated projection data to generate a coarse estimate of the needed scatter profile used in parameter calibration. The method was qualitatively and quantitatively evaluated using numerical simulations and experimental CBCT data acquired on a clinical CBCT imaging system. Results show that the proposed algorithm can significantly reduce scatter artefacts and recover the correct CT number. Numerical simulation results show the method is patient specific, can accurately estimate the scatter, and is robust with respect to segmentation procedure. For experimental and in vivo human data, the results show the CT number can be successfully recovered and anatomical structure visibility can be significantly improved.

By adding a Talbot-Lau interferometer to a conventional x-ray absorption computed tomography (CT) imaging system, both differential phase contrast (DPC) signal and absorption contrast signal can be simultaneously measured from the same set of CT measurements. The imaging performance of such multi-contrast x-ray CT imaging systems can be characterized with standard metrics such as noise variance, noise power spectrum, contrast-to-noise ratio, modulation transfer function (MTF), and task-based detectability index. Among these metrics, the measurement of the MTF can be challenging in DPC-CT systems due to several confounding factors such as phase wrapping and the difficulty of using fine wires as probes. To address these technical challenges, this paper discusses a viable and reliable method to experimentally measure the MTF of DPC-CT. It has been found that the spatial resolution of DPC-CT is degraded, when compared to that of the corresponding absorption CT, due to the presence of a source grating G0 in the Talbot-Lau interferometer. An effective MTF was introduced and experimentally estimated to describe the impact of the Talbot-Lau interferometer on the system MTF.

Differential phase contrast computed tomography (DPC-CT) is an x-ray imaging method that uses the wave properties of imaging photons as the contrast mechanism. It has been demonstrated that DPC images can be obtained using a conventional x-ray tube and a Talbot-Lau-type interferometer. Due to the limited size of the gratings, current data acquisition systems only offer a limited field of view, and thus are prone to data truncation. As a result, the reconstructed DPC-CT image may suffer from image artifacts and increased inaccuracy in the reconstructed image values. In this paper, we demonstrate that a small region of interest (ROI) within a large object can be accurately and stably reconstructed using fully truncated projection datasets provided that a priori information on electron density is known for a small region inside the ROI. The method reconstructs an image iteratively to satisfy a group of physical conditions by using a projection onto convex set (POCS) approach. In this work, this POCS algorithm is validated using both numerical simulations and physical phantom experimental data. In both cases, the root mean square error is reduced by an order of magnitude with respect to the truncated analytic reconstructions. Truncation artifacts observed in the latter reconstructions are eliminated using the POCS algorithm.

C-arm cone-beam CT could replace preoperative multi-detector CT scans in the cardiac interventional setting. However, cardiac gating results in view angle undersampling and the small size of the detector results in projection data truncation. These problems are incompatible with conventional tomographic reconstruction algorithms. In this paper, the prior image constrained compressed sensing (PICCS) reconstruction method was adapted to solve these issues. The performance of the proposed method was compared to that of FDK, FDK with extrapolated projection data (E-FDK), and total variation-based compressed sensing. A canine projection dataset acquired using a clinical C-arm imaging system supplied realistic cardiac motion and anatomy for this evaluation. Three different levels of truncation were simulated. The relative root mean squared error and the universal image quality index were used to quantify the reconstruction accuracy. Three main conclusions were reached. (1) The adapted version of the PICCS algorithm offered the highest image quality and reconstruction accuracy. (2) No meaningful variation in performance was observed when the amount of truncation was changed. (3) This study showed evidence that accurate interior tomography with an undersampled acquisition is possible for realistic objects if a prior image with minimal artifacts is available.

Four-dimensional cone beam computed tomography (4DCBCT) has been proposed to characterize the breathing motion of tumors before radiotherapy treatment. However, when the acquired cone beam projection data are retrospectively gated into several respiratory phases, the available data to reconstruct each phase is under-sampled and thus causes streaking artifacts in the reconstructed images. To solve the under-sampling problem and improve image quality in 4DCBCT, various methods have been developed. This paper presents performance studies of three different 4DCBCT methods based on different reconstruction algorithms. The aims of this paper are to study (1) the relationship between the accuracy of the extracted motion trajectories and the data acquisition time of a 4DCBCT scan and (2) the relationship between the accuracy of the extracted motion trajectories and the number of phase bins used to sort projection data. These aims will be applied to three different 4DCBCT methods: conventional filtered backprojection reconstruction (FBP), FBP with McKinnon-Bates correction (MB) and prior image constrained compressed sensing (PICCS) reconstruction. A hybrid phantom consisting of realistic chest anatomy and a moving elliptical object with known 3D motion trajectories was constructed by superimposing the analytical projection data of the moving object to the simulated projection data from a chest CT volume dataset. CBCT scans with gantry rotation times from 1 to 4 min were simulated, and the generated projection data were sorted into 5, 10 and 20 phase bins before different methods were used to reconstruct 4D images. The motion trajectories of the moving object were extracted using a fast free-form deformable registration algorithm. The root mean square errors (RMSE) of the extracted motion trajectories were evaluated for all simulated cases to quantitatively study the performance. The results demonstrate (1) longer acquisition times result in more accurate motion delineation for each method; (2) ten or more phase bins are necessary in 4DCBCT to ensure sufficient temporal resolution in tumor motion and (3) to achieve the same performance as FBP-4DCBCT with a 4 min data acquisition time, MB-4DCBCT and PICCS-4DCBCT need about 2- and 1 min data acquisition times, respectively.

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.

Dual energy computed tomography (DECT) is currently a subject of extensive investigation. DECT is currently implemented using either a dual source scanner with high and low kVp data acquired from separate sources or a single source scanner with both high and low kVp data acquired in an alternating manner. Both methods require dedicated hardware to enable data acquisition and image reconstruction for DECT. In this paper, we present a method to enable DECT using a single x-ray source with a slow kVp switching data acquisition. The enabling reconstruction technique allowing for the reduction in slew rate is the prior image constrained compressed sensing (PICCS) algorithm. When a slow kVp switching data acquisition method is used, the projection data with high and low kVp values are undersampled and the conventional filtered backprojection (FBP) image reconstruction does not enable streaking artifact-free images for material decomposition in DECT. In this paper, all of the acquired high and low kVp projection data were used to generate a prior image using the conventional FBP method. The PICCS algorithm was then used to reconstruct both high and low kVp images to enable material decomposition in the image domain. Both numerical simulations and physical phantom experimental studies were conducted to validate the proposed DECT scheme. The results demonstrate that a slew rate corresponding to 123 views at high and low kVp (high and low kVp values used for dual energy decomposition) is sufficient for the PICCS-based DECT method. In contrast, the slew rate should be high enough to obtain over 500 projections at each kVp for artifact-free reconstruction using an FBP-based DECT method.

Compared to single energy CT, which only provides information for x-ray linear attenuation coefficients, dual-energy CT is able to obtain both the electron density and effective atomic number for different materials in a quantitative way. In this study, as an alternative to dual-energy CT, a novel quantitative imaging method based on phase contrast CT is presented. Rather than requiring two projection data sets with different x-ray energy spectra, diffraction-grating-based phase contrast CT is capable of reconstructing images of both linear attenuation and refractive index decrement from the same projection data using a single x-ray energy spectra. From the two images, quantitative information of both the electron density and effective atomic number can be extracted. Two physical phantoms were constructed and used to validate the presented method. Experimental results demonstrate that (1) electron density can be accurately determined from refractive index decrement through a linear relationship, and (2) the effective atomic number can be explicitly derived from the ratio of the linear attenuation to refractive index decrement using a power function plus a constant. The presented method will provide insight into the technique of material separation and find its use in medical and industrial applications.

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.

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.

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.

Recently, x-ray differential phase contrast computed tomography (DPC-CT) has been experimentally implemented using a conventional tube combined with gratings. Images were reconstructed using a parallel-beam reconstruction formula. However, parallel-beam reconstruction formulae are not applicable when the parallel-beam approximation fails. In this paper, we present a new image reconstruction formula for fan-beam DPC-CT. There are several novel features of the new image reconstruction formula: (i) when the scanning angular range of data acquisition is larger than pi + gamma(m) (gamma(m) is the full fan angle), the entire field of view can be exactly reconstructed; (ii) when the scanning angular range is smaller than pi + gamma(m), a local region of interest (ROI) can be exactly reconstructed; (iii) it enables an exact reconstruction for a local ROI when the projection data are truncated at some view angles; (iv) it enlarges the imaging field of view when the detector is asymmetrically placed. In this last case, the data are truncated from every view angle. Numerical simulations have been conducted to validate the new reconstruction formula.

Recently, x-ray differential phase contrast computed tomography (DPC-CT) has been experimentally implemented using a conventional source combined with several gratings. Images were reconstructed using a parallel-beam reconstruction formula. However, parallel-beam reconstruction formulae are not directly applicable for a large image object where the parallel-beam approximation fails. In this note, we present a new image reconstruction formula for fan-beam DPC-CT. There are two major features in this algorithm: (1) it enables the reconstruction of a local region of interest (ROI) using data acquired from an angular interval shorter than 180 degrees + fan angle and (2) it still preserves the filtered backprojection structure. Numerical simulations have been conducted to validate the image reconstruction algorithm.

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.

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 fanbeam 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.