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.

Hybrid deterministic-stochastic modeling of x-ray beam bowtie filter scatter on a CT system.

Knowledge of scatter generated by bowtie filter (i.e. x-ray beam compensator) is crucial for providing artifact free images on the CT scanners. Our approach is to use a hybrid deterministic-stochastic simulation to estimate the scatter level generated by a bowtie filter made of a material with low atomic number. First, major components of CT systems, such as source, flat filter, bowtie filter, body phantom, are built into a 3D model. The scattered photon fluence and the primary transmitted photon fluence are simulated by MCNP - a Monte Carlo simulation toolkit. The rejection of scattered photon by the post patient collimator (anti-scatter grid) is simulated with an analytical formula. The biased sinogram is created by superimposing scatter signal generated by the simulation onto the primary x-ray beam signal. Finally, images with artifacts are reconstructed with the biased signal. The effect of anti-scatter grid height on scatter rejection are also discussed and demonstrated.

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.

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.

Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise.

OBJECTIVE: The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. MATERIALS AND METHODS: Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. RESULTS: Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). CONCLUSION: In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.

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.

Non-invasive assessment of functionally relevant coronary artery stenoses with quantitative CT perfusion: preliminary clinical experiences.

OBJECTIVES: We developed a quantitative Dynamic Contrast-Enhanced CT (DCE-CT) technique for measuring Myocardial Perfusion Reserve (MPR) and Volume Reserve (MVR) and studied their relationship with coronary stenosis. METHODS: Twenty-six patients with Coronary Artery Disease (CAD) were recruited. Degree of stenosis in each coronary artery was classified from catheter-based angiograms as Non-Stenosed (NS, angiographically normal or mildly irregular), Moderately Stenosed (MS, 50-80% reduction in luminal diameter), Severely Stenosed (SS, >80%) and SS with Collaterals (SSC). DCE-CT at rest and after dipyridamole infusion was performed using 64-slice CT. Mid-diastolic heart images were corrected for beam hardening and analyzed using proprietary software to calculate Myocardial Blood Flow (MBF, in mL∙min(-1)∙100 g(-1)) and Blood Volume (MBV, in mL∙100 g(-1)) parametric maps. MPR and MVR in each coronary territory were calculated by dividing MBF and MBV after pharmacological stress by their respective baseline values. RESULTS: MPR and MVR in MS and SS territories were significantly lower than those of NS territories (p < 0.05 for all). Logistic regression analysis identified MPR∙MVR as the best predictor of ≥50% coronary lesion than MPR or MVR alone. CONCLUSIONS: DCE-CT imaging with quantitative CT perfusion analysis could be useful for detecting coronary stenoses that are functionally significant.

An enhanced reconstruction algorithm to extend CT scan field-of-view with z-axis consistency constraint.

PURPOSE: To further improve the image quality, in particularly, to suppress the boundary artifacts, in the extended scan field-of-view (SFOV) reconstruction. METHODS: To combat projection truncation artifacts and to restore truncated objects outside the SFOV, an algorithm has previously been proposed based on fitting a partial water cylinder at the site of the truncation. Previous studies have shown this algorithm can simultaneously eliminate the truncation artifacts inside the SFOV and preserve the total amount of attenuation, owing to its emphasis on consistency conditions of the total attenuation in the parallel sampling geometry. Unfortunately, the water cylinder fitting parameters of this 2D algorithm are inclined to high noise fluctuation in the projection samples from image to image, causing anatomy boundaries artifacts, especially during helical scans with higher pitch (≥1.0). To suppress the boundary artifacts and further improve the image quality, the authors propose to use a roughness penalty function, based on the Huber regularization function, to reinforce the z-dimensional boundary consistency. Extensive phantom and clinical tests have been conducted to test the accuracy and robustness of the enhanced algorithm. RESULTS: Significant reduction in the boundary artifacts is observed in both phantom and clinical cases with the enhanced algorithm. The proposed algorithm also reduces the percent difference error between the horizontal and vertical diameters to well below 1%. It is also noticeable that the algorithm has improved CT number uniformity outside the SFOV compared to the original algorithm. CONCLUSIONS: The proposed algorithm is capable of suppressing boundary artifacts and improving the CT number uniformity outside the SFOV.

Simulation and analysis of image quality impacts from single source, ultra-wide coverage CT scanner.

Future generations of CT systems would need a mean to cover an entire organ in a single rotation. A way to accomplish this is to physically increase detector size to provide, e.g., 120∼160 mm z (head-foot) coverage at iso-center. The x-ray cone angle of such a system is usually 3∼4 times of that of a 64-slice (40 mm) system, which leads to more severe cone beam artifacts in cardiac scans. In addition, the extreme x-ray take-off angles for such a system cause severe heel effect, which would require an increase in anode target angle to compensate for it. One shortcoming of larger target angle is that tube output likely decreases because of shorter thermal length. This would result in an increase of image noise. Our goal is to understand from a physics and math point of view, what is the theoretical entitlement of artifacts, resolution, and noise impact of such a system. The image artifacts are assessed through computer simulation of a helical body phantom and visual comparison of reconstructed images between a 140 mm system and a 64-slice system. The IQ impact from target angle increase is studied analytically and experimentally by first finding the proper range of target angles that give the acceptable heel effect, then estimating the impact on peak power (flux) and z resolution using an empirical model of heel effect for given target angle and analytical models of z resolution and tube current loading factor for given target thermal length. The results show that, for a 140 mm system, 24.5% of imaging volume exhibits more severe cone beam artifacts than a 64-slice system, which also poses a patient dose concern. In addition, this system may suffer from a 36% peak power (flux) loss, which is equivalent to about 20% image noise increase. Therefore, a wide coverage CT system using a single x-ray source is likely to face some severe challenges in IQ and clinical accuracy.

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.

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.

Adaptive statistical iterative reconstruction technique for radiation dose reduction in chest CT: a pilot study.

PURPOSE: To compare lesion detection and image quality of chest computed tomographic (CT) images acquired at various tube current-time products (40-150 mAs) and reconstructed with adaptive statistical iterative reconstruction (ASIR) or filtered back projection (FBP). MATERIALS AND METHODS: In this Institutional Review Board-approved HIPAA-compliant study, CT data from 23 patients (mean age, 63 years ± 7.3 [standard deviation]; 10 men, 13 women) were acquired at varying tube current-time products (40, 75, 110, and 150 mAs) on a 64-row multidetector CT scanner with 10-cm scan length. All patients gave informed consent. Data sets were reconstructed at 30%, 50%, and 70% ASIR-FBP blending. Two thoracic radiologists assessed image noise, visibility of small structures, lesion conspicuity, and diagnostic confidence. Objective noise and CT number were measured in the thoracic aorta. CT dose index volume, dose-length product, weight, and transverse diameter were recorded. Data were analyzed by using analysis of variance and the Wilcoxon signed rank test. RESULTS: FBP had unacceptable noise at 40 and 75 mAs in 17 and five patients, respectively, whereas ASIR had acceptable noise at 40-150 mAs. Objective noise with 30%, 50%, and 70% ASIR blending (11.8 ± 3.8, 9.6 ± 3.1, and 7.5 ± 2.6, respectively) was lower than that with FBP (15.8 ± 4.8) (P < .0001). No lesions were missed on FBP or ASIR images. Lesion conspicuity was graded as well seen on both FBP and ASIR images (P < .05). Mild pixilated blotchy texture was noticed with 70% blended ASIR images. CONCLUSION: Acceptable image quality can be obtained for chest CT images acquired at 40 mAs by using ASIR without any substantial artifacts affecting diagnostic confidence. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101450/-/DC1.

Quantification of head and body CTDI(VOL) of dual-energy x-ray CT with fast-kVp switching.

PURPOSE: Recently, a fast-kVp switching (FKS) dual-energy method has been presented with clinical and phantom results to demonstrate its efficacy. Patient dose concern has been raised on FKS dual-energy since it involves higher energy acquisition at 140 kVp and slower gantry rotation time (e.g., 0.9-1 s) as opposed to 0.5 s as used in routine single-energy exams. The purpose of our study was to quantitatively compare the CTDI(VOL) of FKS and routine CT exams under the body and head conditions. METHODS: For a fair comparison, we have to overcome the difficulty of unmatched protocols between FKS and routine CT exams. In this paper, we propose to match the low contrast detectability (LCD), a critical image quality metric impacting diagnostic quality, before measuring CTDI(VOL). The kVp pair, flux ratio, and optimal monochromatic energy have been carefully optimized for FKS protocols prior to the comparison. Our baseline single-energy protocols were per IEC-61223-3-5 under head and body conditions except for mA, which was iteratively adjusted to match the LCD of FKS. CTDI(VOL) was measured using either a 16 cm (for head scanning) or a 32 cm (for body scanning) PMMA phantom of at least 14 cm in length. The LCD was measured using the uniform section of Catphan 600. To make the study repeatable, the automated statistical LCD measurement tool available on GE Discovery CT750 scanner was used in this work. A visual LCD phantom and a Gammex tissue characterization phantom were also employed to verify the statistical LCD measurements and to introduce various patient sizes and contrast levels. RESULTS: The mean CTDI(VOL) for the head and body single-energy acquisitions was 57.5 and 29.2 mGy, respectively. The LCD was measured at 0.45% and 0.42%, respectively. The average CTDI(VOL) for FKS head and body scans was 70.4 and 33.4 mGy, respectively. The corresponding LCD was measured at 0.45% and 0.43%, respectively. The results from the visual LCD phantom and Gammex phantom supported the statistical LCD measurements. CONCLUSIONS: For equal image quality as measured by low contrast detectability, the CTDI(VOL) of a FKS head and body exam is roughly 22% and 14% higher than that of a routine single-energy head and body exam, respectively, for the phantom measured.

Compressive sensing-based interior tomography: preliminary clinical application.

Compressive sensing (CS)-based interior tomography is a state-of-the-art method for accurate image reconstruction from only locally truncated projections. Here, we report our preliminary interior tomography results reconstructed from raw projections of a patient acquired on a GE Discovery CT750 HD scanner. This is the first clinical application of the CS-based interior reconstruction techniques, and the results show an excellent match with those reconstructed from global projections.

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.

Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques.

PURPOSE: To compare image quality and lesion conspicuity on abdominal computed tomographic (CT) images acquired with different x-ray tube current-time products (50-200 mAs) and reconstructed with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) techniques. MATERIALS AND METHODS: Twenty-two patients (mean age, 60.1 years ± 7.3 [standard deviation]; age range, 52.8-67.4 years; mean weight, 78.9 kg ± 18.3; 12 men, 10 women) gave informed consent for this prospective institutional review board-approved and HIPAA-compliant study, which involved the acquisition of four additional image series at multidetector CT. Images were acquired at different tube current-time products (200, 150, 100, and 50 mAs) and encompassed an abdominal lesion over a 10-cm scan length. Images were reconstructed separately with FBP and with three levels of ASIR-FBP blending. Two radiologists reviewed FBP and ASIR images for image quality in a blinded and randomized manner. Volume CT dose index (CTDI(vol)), dose-length product, patient weight, objective noise, and CT numbers were recorded. Data were analyzed by using analysis of variance and the Wilcoxon signed rank test. RESULTS: CTDI(vol) values were 16.8, 12.6, 8.4, and 4.2 mGy for 200, 150, 100, and 50 mAs, respectively (P < .001). Subjective noise was graded as below average at 150 mAs and average at 100 and 50 mAs for ASIR images, as compared with FBP images, on which noise was graded as average at 150 mAs, above average at 100 mAs, and unacceptable at 50 mAs. A substantial blotchy image appearance was noted in four of 22 image series acquired at 4.2 mGy with 70% ASIR. Lesion conspicuity was significantly better at 4.2 mGy on ASIR than on FBP images (observed P < .044), and overall diagnostic confidence changed from unacceptable on FBP to acceptable on ASIR images. CONCLUSION: ASIR lowers noise and improves diagnostic confidence in and conspicuity of subtle abdominal lesions at 8.4 mGy when images are reconstructed with 30% ASIR blending and at 4.2 mGy in patients weighing 90 kg or less when images are reconstructed with 50% or 70% ASIR blending.

Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique.

PURPOSE: To compare visualization of subtle normal and abnormal findings at computed tomography (CT) of the chest for diffuse lung disease with images reconstructed with filtered back projection and adaptive statistical iterative reconstruction (ASIR) techniques. MATERIALS AND METHODS: In this HIPAA-compliant, institutional review board-approved study, 24 patients underwent 64-section multi-detector row CT of the chest for evaluation of diffuse lung disease. Scanning parameters included a pitch of 0.984:1 and 120 kVp in thin-section mode, with 2496 views per rotation compared with 984 views acquired for normal mode. The 0.625-mm-thick images were reconstructed with filtered back projection, ASIR, and ASIR high-definition (ASIR-HD) kernels. Two thoracic radiologists independently assessed the filtered back projection, ASIR, and ASIR-HD images for small anatomic details (interlobular septa, centrilobular region, and small bronchi and bronchioles), abnormal findings (reticulation, tiny nodules, altered attenuation, bronchiectasis), image quality (graded by using a six-point scale, where 1 = excellent image quality, and 5 = interpretation impossible), image noise, and artifacts. Data were tabulated for statistical testing. RESULTS: For visualization of normal and pathologic structures, CT image series reconstructed with ASIR-HD were rated substantially better than those reconstructed with filtered back projection and ASIR (P < .001). ASIR-HD images were superior to filtered back projection images in 15 of 24 (62%) patients for visualization of normal structures and in 24 of 24 (100%) patients for pathologic findings. ASIR-HD was superior to ASIR in three of 24 (12%) images for normal anatomic findings and in seven of 24 (29%) images for pathologic evaluation. None of the images in the three groups were rated as unacceptable for noise (P < .001). CONCLUSION: ASIR-HD reconstruction results in superior visualization of subtle and tiny anatomic structures and lesions in diffuse lung disease compared with ASIR and filtered back projection reconstructions.

Temporal resolution improvement in cardiac CT using PICCS (TRI-PICCS): performance studies.

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