Spatial and temporal motion characterization for x-ray CT.

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

Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies.

OBJECTIVES: To assess perceptual benefits provided by the improved spatial resolution and noise performance of deep silicon photon-counting CT (Si-PCCT) over conventional energy-integrating CT (ECT) using polychromatic images for various clinical tasks and anatomical regions. MATERIALS AND METHODS: Anthropomorphic, computational models were developed for lungs, liver, inner ear, and head-and-neck (H&N) anatomies. These regions included specific abnormalities such as lesions in the lungs and liver, and calcified plaques in the carotid arteries. The anatomical models were imaged using a scanner-specific CT simulation platform (DukeSim) modeling a Si-PCCT prototype and a conventional ECT system at matched dose levels. The simulated polychromatic projections were reconstructed with matched in-plane resolutions using manufacturer-specific software. The reconstructed pairs of images were scored by radiologists to gauge the task-specific perceptual benefits provided by Si-PCCT compared to ECT based on visualization of anatomical and image quality features. The scores were standardized as z-scores for minimizing inter-observer variability and compared between the systems for evidence of statistically significant improvement (one-sided Wilcoxon rank-sum test with a significance level of 0.05) in perceptual performance for Si-PCCT. RESULTS: Si-PCCT offered favorable image quality and improved visualization capabilities, leading to mean improvements in task-specific perceptual performance over ECT for most tasks. The improvements for Si-PCCT were statistically significant for the visualization of lung lesion (0.08 ± 0.89 vs. 0.90 ± 0.48), liver lesion (-0.64 ± 0.37 vs. 0.95 ± 0.55), and soft tissue structures (-0.47 ± 0.90 vs. 0.33 ± 1.24) and cochlea (-0.47 ± 0.80 vs. 0.38 ± 0.62) in inner ear. CONCLUSIONS: Si-PCCT exhibited mean improvements in task-specific perceptual performance over ECT for most clinical tasks considered in this study, with statistically significant improvement for 6/20 tasks. The perceptual performance of Si-PCCT is expected to improve further with availability of spectral information and reconstruction kernels optimized for high resolution provided by smaller pixel size of Si-PCCT. The outcomes of this study indicate the positive potential of Si-PCCT for benefiting routine clinical practice through improved image quality and visualization capabilities.

Synthetization of high-dose images using low-dose CT scans.

BACKGROUND: Radiation dose reduction has been the focus of many research activities in x-ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients. PURPOSE: Reconstruction algorithm-based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high-dose images (SHD) from the data acquired with low-dose scans. A representative example is the model-based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high-quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low-dose clinical datasets that overcomes both the noise texture issue and the data availability issue. METHODS: Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick-slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD. RESULTS: The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise-texture. CONCLUSIONS: We present a method to generate SHD datasets from regularly acquired low-dose CT scans. Images produced with the proposed approach exhibit excellent noise-reduction with the desired noise-texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined.

Can Photon-Counting CT Improve Estimation Accuracy of Morphological Radiomics Features? A Simulation Study for Assessing the Quantitative Benefits from Improved Spatial Resolution in Deep Silicon-Based Photon-Counting CT.

RATIONALE AND OBJECTIVES: Deep silicon-based photon-counting CT (Si-PCCT) is an emerging detector technology that provides improved spatial resolution by virtue of its reduced pixel sizes. This article reports the outcomes of the first simulation study evaluating the impact of this advantage over energy-integrating CT (ECT) for estimation of morphological radiomics features in lung lesions. MATERIALS AND METHODS: A dynamic nutrient-access-based stochastic model was utilized to generate three distinct morphologies for lung lesions. The lesions were inserted into the lung parenchyma of an anthropomorphic phantom (XCAT - 50th percentile BMI) at 50, 70, and 90 mm from isocenter. The phantom was virtually imaged with an imaging simulator (DukeSim) modeling a Si-PCCT and a conventional ECT system using varying imaging conditions (dose, reconstruction kernel, and pixel size). The imaged lesions were segmented using a commercial segmentation tool (AutoContour, Advantage Workstation Server 3.2, GE Healthcare) followed by extraction of morphological radiomics features using an open-source radiomics package (pyradiomics). The estimation errors for both systems were computed as percent differences from corresponding feature values estimated for the ground-truth lesions. RESULTS: Compared to ECT, the mean estimation error was lower for Si-PCCT (independent features: 35.9% vs. 54.0%, all features: 54.5% vs. 68.1%) with statistically significant reductions in errors for 8/14 features. For both systems, the estimation accuracy was minimally affected by dose and distance from the isocenter while reconstruction kernel and pixel size were observed to have a relatively stronger effect. CONCLUSION: For all lesions and imaging conditions considered, Si-PCCT exhibited improved estimation accuracy for morphological radiomics features over a conventional ECT system, demonstrating the potential of this technology for improved quantitative imaging.

Deep learning based correction of low performing pixel in computed tomography.

Low Performing Pixel (LPP)/bad pixel in CT detectors cause ring and streaks artifacts, structured non-uniformities and deterioration of the image quality. These artifacts make the image unusable for diagnostic purposes. A missing/defective detector pixel translates to a channel missing across all views in sinogram domain and its effect gets spill over entire image in reconstruction domain as artifacts. Most of the existing ring and streak removal algorithms perform correction only in the reconstructed image domain. In this work, we propose a supervised deep learning algorithm that operates in sinogram domain to remove distortions cause by the LPP. This method leverages CT scan geometry, including conjugate ray information to learn the interpolation in sinogram domain. While the experiments are designed to cover the entire detector space, we emphasize on LPPs near detector iso-center as these have most adverse impact on image quality specially if the LPPs fall on the high frequency region (bone-tissue interface). We demonstrated efficacy of the proposed method using data acquired on GE RevACT multi-slice CT system with flat-panel detector. Experimental results on head scans show significant reduction in ring artifacts regardless of LPP location in the detector geometry. We have simulated isolated LPPs accounting for 5% and 10% of total channels. Detailed statistical analysis illustrates approximately 5dB improvement in SNR in both sinogram and reconstruction domain as compared to classical bicubic and Lagrange interpolation methods. Also, with reduction in ring and streak artifacts, the perceptual image quality is improved across all the test images.

A novel simulation-driven reconstruction approach for x-ray computed tomography.

PURPOSE: Radiation dose reduction is critical to the success of x-ray computed tomography (CT). Many advanced reconstruction techniques have been developed over the years to combat noise resulting from the low-dose CT scans. These algorithms rely on accurate local estimation of the image noise to determine reconstruction parameters or to select inferencing models. Because of the difficulties in the noise estimation for heterogeneous objects, the performance of many algorithms is inconsistent and suboptimal. Here, we propose a novel approach to overcome such shortcoming. METHOD: By injecting appropriate amount of noise in the CT raw data, a computer simulation approach is capable of accurately estimating the local statistics of the raw data and the local noise in the reconstructed images. This information is then used to guide the noise-reduction process during the reconstruction. As an initial implementation, a scaling map is generated based on the noise predicted from the simulation and the noise estimated from existing reconstruction algorithms. Images generated with existing algorithms are subsequently modified based on the scaling map. In this study, both iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms are evaluated. RESULTS: Phantom experiments were conducted to evaluate the performance of the simulation-based noise estimation in terms of the standard deviation and noise power spectrum. Quantitative results have demonstrated that the noise measured from the original image matches well with the noise estimated from the simulation. Clinical datasets were utilized to further confirm the accuracy of the proposed approach under more challenging conditions. To validate the performance of the proposed reconstruction approach, clinical scans were used. Performance comparison was carried out qualitatively and quantitatively. Two existing advanced reconstruction techniques, IR and DLIR, were evaluated against the proposed approach. Results have shown that the proposed approach outperforms existing IR and DLIR algorithms in terms of noise suppression and, equally importantly, noise uniformity across the entire imaging volume. Visual assessment of the images also reveals that the proposed approach does not endure noise texture issues faced by some of the existing reconstruction algorithms today. CONCLUSION: Phantom and clinical results have demonstrated superior performance of the proposed approach with regard to noise reduction as well as noise homogeneity. Visual inspection of the noise texture further confirms the clinical utility of the proposed approach. Future enhancements on the current implementation are explored regarding image quality and computational efficiency. Because of the limited scope of this paper, detailed investigation on these enhancement features will be covered in a separate report.

Computed tomography recent history and future perspectives.

Purpose: We provide a review of the key computed tomography (CT) technologies developed since the late 1980s and offer an overview of one of the future technologies under development. The focus of this review is mainly on the hardware and system development. The topics on the historical event linked to the early days of CT development and other innovations that contributed to the CT development, such as advanced image reconstruction techniques, are covered by companion papers in this special issue. Approach: The review is divided into five major sections, each linked to a key innovation in CT: helical spiral data acquisition, multi-slice CT, wide-cone CT, dual-source CT, and spectral CT. Given the limited scope of this review, only one of the future technologies, photon-counting CT, is discussed in detail. Whenever possible, both theory of operation and clinical examples are provided. Results: Theoretical analyses, phantom results, and clinical examples clearly demonstrate the efficacy and clinical relevancy of five historical technology developments and one future technology in CT. These technologies have improved and will continue to improve CT performance in terms of isotropic volume coverage, improved temporal resolution, and material differentiation and characterization capabilities. Conclusions: Over the past 30 years, technological developments of CT have contributed to the success of CT in many clinical applications such as trauma, oncology, cardiac imaging, and stroke. Advanced clinical applications have and will continue to demand more advanced technology development.

High Pitch Helical CT Reconstruction.

To avoid severe limited-view artifacts in reconstructed CT images, current multi-row detector CT (MDCT) scanners with a single x-ray source-detector assembly need to limit table translation speeds such that the pitch p (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When , it remains an open question whether one can reconstruct clinically useful helical CT images without severe artifacts. In this work, we show that a synergistic use of advanced techniques in conventional helical filtered backprojection, compressed sensing, and more recent deep learning methods can be properly integrated to enable accurate reconstruction up to p=4 without significant artifacts for single source MDCT scans.

Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction.

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.

Principles and applications of multienergy CT: Report of AAPM Task Group 291.

In x-ray computed tomography (CT), materials with different elemental compositions can have identical CT number values, depending on the mass density of each material and the energy of the detected x-ray beam. Differentiating and classifying different tissue types and contrast agents can thus be extremely challenging. In multienergy CT, one or more additional attenuation measurements are obtained at a second, third or more energy. This allows the differentiation of at least two materials. Commercial dual-energy CT systems (only two energy measurements) are now available either using sequential acquisitions of low- and high-tube potential scans, fast tube-potential switching, beam filtration combined with spiral scanning, dual-source, or dual-layer detector approaches. The use of energy-resolving, photon-counting detectors is now being evaluated on research systems. Irrespective of the technological approach to data acquisition, all commercial multienergy CT systems circa 2020 provide dual-energy data. Material decomposition algorithms are then used to identify specific materials according to their effective atomic number and/or to quantitate mass density. These algorithms are applied to either projection or image data. Since 2006, a number of clinical applications have been developed for commercial release, including those that automatically (a) remove the calcium signal from bony anatomy and/or calcified plaque; (b) create iodine concentration maps from contrast-enhanced CT data and/or quantify absolute iodine concentration; (c) create virtual non-contrast-enhanced images from contrast-enhanced scans; (d) identify perfused blood volume in lung parenchyma or the myocardium; and (e) characterize materials according to their elemental compositions, which can allow in vivo differentiation between uric acid and non-uric acid urinary stones or uric acid (gout) or non-uric acid (calcium pyrophosphate) deposits in articulating joints and surrounding tissues. In this report, the underlying physical principles of multienergy CT are reviewed and each of the current technical approaches are described. In addition, current and evolving clinical applications are introduced. Finally, the impact of multienergy CT technology on patient radiation dose is summarized.

Effect of a calcium deblooming algorithm on accuracy of coronary computed tomography angiography.

BACKGROUND: Coronary artery calcification is a significant contributor to reduced accuracy of coronary computed tomographic angiography (CTA) in the assessment of coronary artery disease severity. The aim of the current study is to assess the impact of a prototype calcium deblooming algorithm on the diagnostic accuracy of CTA. METHODS: 40 patients referred for invasive catheter angiography underwent CTA and invasive catheter angiography. The CTA were reconstructed using a standard soft tissue kernel (CTASTAND) and a deblooming algorithm (CTADEBLOOM). CTA studies were read with and without the deblooming algorithm blinded to the invasive coronary angiogram findings. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value for the detection of stenosis ≥50% or ≥70% were evaluated using quantitative coronary angiography as the reference standard. Image quality was assessed using a 5-point scale, and the presence of image artifact recorded. RESULTS: All studies were diagnostic with 548 segments available for evaluation. Image score was 3.64 ±â€¯0.72 with CTADEBLOOM, versus 3.56 ±â€¯0.72 with CTASTAND (p = 0.38). CTADEBLOOM had significantly less calcium blooming artifact than CTASTAND (12.5% vs. 47.5%, p = 0.001). Based on a 50% stenosis threshold for defining significant disease, the Sensitivity/Specificity/PPV/NPV/Accuracy were 65.9/84.9/27.6/96.6/83.4 for CTADEBLOOM and 75.0/81.9/26.6/97.4/81.4 for CTASTAND using a ≥50% threshold. CTADEBLOOM specificity was significantly higher than CTASTAND (84.9% vs. 81.5%, p = 0.03), with no difference between the algorithms in sensitivity (p = 0.22), or accuracy (p = 0.15). These results remained unchanged when a stenosis threshold of ≥70% was used. Interobserver agreement was fair with both techniques (CTADEBLOOM k = 0.38, CTASTAND k = 0.37). CONCLUSION: In this proof of concept study, coronary calcification deblooming using a prototype post-processing algorithm is feasible and reduces calcium blooming with an improvement of the specificity of the CTA exam.

Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging.

We investigated a projection interpolation method for reconstructing dynamic contrast-enhanced (DCE) heart images from undersampled x-ray projections with filtered backprojecton (FBP). This method may facilitate the application of sparse-view dynamic acquisition for ultralow-dose quantitative computed tomography (CT) myocardial perfusion (MP) imaging. We conducted CT perfusion studies on 5 pigs with a standard full-view acquisition protocol (984 projections). We reconstructed DCE heart images with FBP from all and a quarter of the measured projections evenly distributed over 360°. We interpolated the sparse-view (quarter) projections to a full-view setting using a cubic-spline interpolation method before applying FBP to reconstruct the DCE heart images (synthesized full-view). To generate MP maps, we used 3 sets of DCE heart images, and compared mean MP values and biases among the 3 protocols. Compared with synthesized full-view DCE images, sparse-view DCE images were more affected by streak artifacts arising from projection undersampling. Relative to the full-view protocol, mean bias in MP measurement associated with the sparse-view protocol was 10.0 mL/min/100 g (95%CI: -8.9 to 28.9), which was >3 times higher than that associated with the synthesized full-view protocol (3.3 mL/min/100 g, 95% CI: -6.7 to 13.2). The cubic-spline-view interpolation method improved MP measurement from DCE heart images reconstructed from only a quarter of the full projection set. This method can be used with the industry-standard FBP algorithm to reconstruct DCE images of the heart, and it can reduce the radiation dose of a whole-heart quantitative CT MP study to <2 mSv (at 8-cm coverage).

CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD.

The patch manifold of a natural image has a low dimensional structure and accommodates rich structural information. Inspired by the recent work of the low-dimensional manifold model (LDMM), we apply the LDMM for regularizing X-ray computed tomography (CT) image reconstruction. This proposed method recovers detailed structural information of images, significantly enhancing spatial and contrast resolution of CT images. Both numerically simulated data and clinically experimental data are used to evaluate the proposed method. The comparative studies are also performed over the simultaneous algebraic reconstruction technique (SART) incorporated the total variation (TV) regularization to demonstrate the merits of the proposed method. Results indicate that the LDMM-based method enables a more accurate image reconstruction with high fidelity and contrast resolution.

Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study.

The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis.

Functional CT assessment of extravascular contrast distribution volume and myocardial perfusion in acute myocardial infarction.

PURPOSE: In a pig model of acute myocardial infarction (AMI), we validated a functional computed tomography (CT) technique for concomitant assessment of myocardial edema and ischemia through extravscualar contrast distribution volume (ECDV) and myocardial perfusion (MP) measurements from a single dynamic imaging session using a single contrast bolus injection. METHODS: In seven pigs, balloon catheter was used to occlude the distal left anterior descending artery for one hour followed by reperfusion. CT and cardiac magnetic resonance (CMR) imaging studies were acquired on 3 days and 12 ±â€¯3 day post ischemic insult. In each CT study, 0.7 ml/kg of iodinated contrast was intravenously injected at 3-4 ml/s before dynamic contrast-enhanced (DCE) cardiac images were acquired with breath-hold using a 64-row CT scanner. DCE cardiac images were analyzed with a model-based deconvolution to generate ECDV and MP maps. ECDV as an imaging marker of edema was validated against CMR T2 weighted imaging in normal and infarcted myocardium delineated from ex-vivo histological staining. RESULTS: ECDV in infarcted myocardium was significantly higher (p < 0.05) than that in normal myocardium on both days post AMI and was in agreement with the findings of CMR T2 weighted imaging. MP was significantly lower (p < 0.05) in the infarcted region compared to normal on both days post AMI. CONCLUSION: This imaging technique can rapidly and simultaneously assess myocardial edema and ischemia through ECDV and MP measurements, and may be useful for delineation of salvageable tissue within at-risk myocardium to guide reperfusion therapy.

Ultra-low dose quantitative CT myocardial perfusion imaging with sparse-view dynamic acquisition and image reconstruction: A feasibility study.

PURPOSE: We implemented and validated a compressed sensing (CS) based algorithm for reconstructing dynamic contrast-enhanced (DCE) CT images of the heart from sparsely sampled X-ray projections. METHODS: DCE CT imaging of the heart was performed on five normal and ischemic pigs after contrast injection. DCE images were reconstructed with filtered backprojection (FBP) and CS from all projections (984-view) and 1/3 of all projections (328-view), and with CS from 1/4 of all projections (246-view). Myocardial perfusion (MP) measurements with each protocol were compared to those with the reference 984-view FBP protocol. RESULTS: Both the 984-view CS and 328-view CS protocols were in good agreements with the reference protocol. The Pearson correlation coefficients of 984-view CS and 328-view CS determined from linear regression analyses were 0.98 and 0.99 respectively. The corresponding mean biases of MP measurement determined from Bland-Altman analyses were 2.7 and 1.2ml/min/100g. When only 328 projections were used for image reconstruction, CS was more accurate than FBP for MP measurement with respect to 984-view FBP. However, CS failed to generate MP maps comparable to those with 984-view FBP when only 246 projections were used for image reconstruction. CONCLUSION: DCE heart images reconstructed from one-third of a full projection set with CS were minimally affected by aliasing artifacts, leading to accurate MP measurements with the effective dose reduced to just 33% of conventional full-view FBP method. The proposed CS sparse-view image reconstruction method could facilitate the implementation of sparse-view dynamic acquisition for ultra-low dose CT MP imaging.

A platform-independent method to reduce CT truncation artifacts using discriminative dictionary representations.

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

Evaluation of GPU-Based CT Reconstruction for Morbidly Obese Patients.

The obese population is increasing in the United States. There have been modest improvements in scanner hardware and image processing to address some specific challenges associated with imaging of the morbidly obese patients. However, most legacy CT systems lack capabilities to provide sufficient delivery of image-based diagnosis in this increasing subset of population. One of the most common problems is the projection data truncation in CT imaging due to the massive girths of obese patients. In the past decade, it was proved that the image can be accurately and stably reconstructed from locally truncated projections if certain prior knowledge is known, and this technique is named interior tomogrpahy. To overcome the time-consuming issue of the iterative algorithms, we apply GPU techniques to speed up the reconstruction process. In this paper, we evaluate the GPU-based CT reconstruction algorithms (one analytic algorithm and one iterative reconstruction algorithm) for obese patients with both simulated and real clinical datasets. While the approximate analytic reconstruction algorithm outperforms the iterative reconstruction (IR) algorithm in terms of computational cost, the IR algorithm outperforms the analytic one in terms of image quality especially when the projection data is suffered from patient motion, which can happen when the obese patients are not able to hold a breath during the scan.

Ultra-Low-Dose Sparse-View Quantitative CT Liver Perfusion Imaging.

Radiation dose of computed tomography liver perfusion imaging can be reduced by collecting fewer x-ray projections in each gantry rotation, but the resulting aliasing artifacts could affect the hepatic perfusion measurement. We investigated the effect of projection undersampling on the assessment of hepatic arterial blood flow (HABF) in hepatocellular carcinoma (HCC) when dynamic contrast-enhanced (DCE) liver images were reconstructed with filtered backprojection (FBP) and compressed sensing (CS). DCE liver images of a patient with HCC acquired with a 64-row CT scanner were reconstructed from all the measured projections (984-view) with the standard FBP and from one-third (328-view) and one-fourth (246-view) of all available projections with FBP and CS. Each of the 5 sets of DCE liver images was analyzed with a model-based deconvolution algorithm from which HABF maps were generated and compared. Mean HABF in the tumor and normal tissue measured by the 328-view CS and FBP protocols was within 5% differences from that assessed by the reference full-view FBP protocol. In addition, the tumor size measured by using the 328-view CS and FBP average images was identical to that determined by using the full-view FBP average image. By contrast, both the 246-view CS and FBP protocols exhibited larger differences (>20%) in anatomical and functional assessments compared with the full-view FBP protocol. The preliminary results suggested that computed tomography perfusion imaging in HCC could be performed with 3 times less projection measurement than the current full-view protocol (67% reduction in radiation dose) when either FBP or CS was used for image reconstruction.

Impact of bowtie filter and object position on the two-dimensional noise power spectrum of a clinical MDCT system.

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