A new redundancy weighting scheme for nonstationary data for computed tomography

Half-scan artifact correction using generative adversarial network for dental CT

Half-scan image reconstruction with Parker weighting can correct motion artifacts in dental CT images taken with a slow scan-based dental CT. Since the residual half-scan artifacts in the dental CT images appear much stronger than those in medical CT images, the artifacts often persist to the extent that they compromise the surface-rendered bone and tooth images computed from the dental CT images. We used a variation of generative adversarial network (GAN), so-called U-WGAN, to correct half-scan artifacts in dental CT images. For the generative network of GAN, we used a U-net structure of five stages to take advantage of its high computational efficiency. We trained the network using the Wasserstein loss function on the dental CT images of 40 patients. We tested the network with comparing its output images to the half-scan images corrected with other methods; Parker weighting and the other two popular GANs, that is, SRGAN and m-WGAN. For the quantitative comparison, we used the image quality metrics measuring the similarity of the corrected images to the full-scan images (reference images) and the noise level on the corrected images. We also compared the visual quality of the surface-rendered bone and tooth images. We observed that the proposed network outperformed Parker weighting and other GANs in all the image quality metrics. The computation time for the proposed network to process 336×336×336 3D images on a GPU-equipped personal computer was about 3 s, which was much shorter than those of SRGAN and m-WGAN, 50 s and 54 s, respectively.

Quantitative low-dose rest and stress CT myocardial perfusion imaging with a whole-heart coverage scanner improves functional assessment of coronary artery disease

Objective

We evaluated the diagnostic accuracy of myocardial blood flow (MBF) and perfusion reserve (MPR) measured from low-dose dynamic contrast-enhanced (DCE) imaging with a whole-heart coverage CT scanner for detecting functionally significant coronary artery disease (CAD).

Methods

Twenty one patients with suspected or known CAD had rest and dipyridamole stress MBF measurements with CT and SPECT myocardial perfusion imaging (MPI), and lumen narrowing assessment with coronary angiography (catheter and/or CT based) within 6 weeks. SPECT MBF measurements and coronary angiography were used together as reference to determine the functional significance of coronary artery stenosis. In each CT MPI study, DCE images of the whole heart were acquired with breath-hold using a low-dose acquisition protocol to generate MBF maps. Binomial logistic regression analysis was used to determine the diagnostic accuracy of CT-measured MBF and MPR (ratio of stress to rest MBF) for assessing functionally significant coronary stenosis.

Results

Mean stress MBF and MPR in ischemic segments were lower than those in non-ischemic segments (1.37 ± 0.34 vs. 2.14 ± 0.64 ml/min/g; 1.56 ± 0.41 vs. 2.53 ± 0.70; p < 0.05 for all). The receiver operating characteristic curve analysis revealed that MPR (AUC 0.916, 95%CI: 0.885–0.947) had a superior power than stress MBF (AUC 0.869, 95%CI: 0.830–0.909) for differentiating non-ischemic and ischemic myocardial segments (p = 0.045). On a per-vessel and per-segment analysis, concomitant use of MPR and stress MBF thresholds further improved the diagnostic accuracy compared to MPR or stress MBF alone for detecting obstructive coronary lesions (per-vessel: 93.4% vs. 83.6% and 88.5%, respectively; per-segment: 90.0% vs. 83.7% and 83.1%, respectively). The estimated effective dose of a rest and stress CT MPI study was 3.04 and 3.19 mSv respectively.

Conclusion

Quantitative rest and stress myocardial perfusion measurement with a large-coverage CT scanner improves the diagnostic accuracy for detecting functionally significant coronary stenosis.

Three-Dimensional Weighting in Cone Beam FBP Reconstruction and Its Transformation Over Geometries

GOALS

With substantially increased number of detector rows in multidetector CT (MDCT), axial scan with projection data acquired along a circular source trajectory has become the method-of-choice in increasing clinical applications. Recognizing the practical relevance of image reconstruction directly from the projection data acquired in the native cone beam (CB) geometry, especially in scenarios wherein the most achievable in-plane resolution is desirable, we present a three-dimensional (3-D) weighted CB-FBP algorithm in such geometry in this paper.

METHODS

We start the algorithm's derivation in the cone-parallel geometry. Via changing of variables, taking the Jacobian into account and making heuristic and empirical assumptions, we arrive at the formulas for 3-D weighted image reconstruction in the native CB geometry.

RESULTS

Using the projection data simulated by computer and acquired by an MDCT scanner, we evaluate and verify performance of the proposed algorithm for image reconstruction directly from projection data acquired in the native CB geometry.

CONCLUSION

The preliminary data show that the proposed algorithm performs as well as the 3-D weighted CB-FBP algorithm in the cone-parallel geometry.

SIGNIFICANCE

The proposed algorithm is anticipated to find its utility in extensive clinical and preclinical applications wherein the reconstruction of images in the native CB geometry, i.e., the geometry for data acquisition, is of relevance.

Technical Note: Evaluation of a 160-mm/256-row CT scanner for whole-heart quantitative myocardial perfusion imaging

PURPOSE

The authors investigated the performance of a recently introduced 160-mm/256-row CT system for low dose quantitative myocardial perfusion (MP) imaging of the whole heart. This platform is equipped with a gantry capable of rotating at 280 ms per full cycle, a second generation of adaptive statistical iterative reconstruction (ASiR-V) to correct for image noise arising from low tube voltage potential/tube current dynamic scanning, and image reconstruction algorithms to tackle beam-hardening, cone-beam, and partial-scan effects.

METHODS

Phantom studies were performed to investigate the effectiveness of image noise and artifact reduction with a GE Healthcare Revolution CT system for three acquisition protocols used in quantitative CT MP imaging: 100, 120, and 140 kVp/25 mAs. The heart chambers of an anthropomorphic chest phantom were filled with iodinated contrast solution at different concentrations (contrast levels) to simulate the circulation of contrast through the heart in quantitative CT MP imaging. To evaluate beam-hardening correction, the phantom was scanned at each contrast level to measure the changes in CT number (in Hounsfield unit or HU) in the water-filled region surrounding the heart chambers with respect to baseline. To evaluate cone-beam artifact correction, differences in mean water HU between the central and peripheral slices were compared. Partial-scan artifact correction was evaluated from the fluctuation of mean water HU in successive partial scans. To evaluate image noise reduction, a small hollow region adjacent to the heart chambers was filled with diluted contrast, and contrast-to-noise ratio in the region before and after noise correction with ASiR-V was compared. The quality of MP maps acquired with the CT system was also evaluated in porcine CT MP studies. Myocardial infarct was induced in a farm pig from a transient occlusion of the distal left anterior descending (LAD) artery with a catheter-based interventional procedure. MP maps were generated from the dynamic contrast-enhanced (DCE) heart images taken at baseline and three weeks after the ischemic insult.

RESULTS

Their results showed that the phantom and animal images acquired with the CT platform were minimally affected by image noise and artifacts. For the beam-hardening phantom study, changes in water HU in the wall surrounding the heart chambers greatly reduced from >±30 to ≤ ± 5 HU at all kVp settings except one region at 100 kVp (7 HU). For the cone-beam phantom study, differences in mean water HU from the central slice were less than 5 HU at two peripheral slices with each 4 cm away from the central slice. These findings were reproducible in the pig DCE images at two peripheral slices that were 6 cm away from the central slice. For the partial-scan phantom study, standard deviations of the mean water HU in 10 successive partial scans were less than 5 HU at the central slice. Similar observations were made in the pig DCE images at two peripheral slices with each 6 cm away from the central slice. For the image noise phantom study, CNRs in the ASiR-V images were statistically higher (p < 0.05) than the non-ASiR-V images at all kVp settings. MP maps generated from the porcine DCE images were in excellent quality, with the ischemia in the LAD territory clearly seen in the three orthogonal views.

CONCLUSIONS

The study demonstrates that this CT system can provide accurate and reproducible CT numbers during cardiac gated acquisitions across a wide axial field of view. This CT number fidelity will enable this imaging tool to assess contrast enhancement, potentially providing valuable added information beyond anatomic evaluation of coronary stenoses. Furthermore, their results collectively suggested that the 100 kVp/25 mAs protocol run on this CT system provides sufficient image accuracy at a low radiation dose (<3 mSv) for whole-heart quantitative CT MP imaging.

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm  ×  25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8  ×  higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.