Yoshiharu Ohno

OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies. MATERIALS AND METHODS: For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test. RESULTS: The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001). CONCLUSION: HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA. KEY POINTS: Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.

OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CADv) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR), mode-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR) at a phantom study. METHODS: A commercially available anthropomorphic abdominal phantom was scanned five times with a 320-detector row CT at 600 mA, 400 mA, 200 mA, and 100 mA and reconstructed by four methods. Signal-to-noise ratios (SNRs) of all lesions within the arterial and portal-venous phase inserts were calculated, and SNR of the lesion phantom was compared with that of all reconstruction methods by means of Tukey's honestly significant difference (HSD) test. Then, tumor volume (V) of each nodule was automatically measured using commercially available CADv software. To compare dose reduction capability for each reconstruction method at both phases, mean differences between measured V and standard references were compared by Tukey's honestly significant difference test among the four different reconstruction methods on CT obtained at each of the four tube currents. RESULTS: With each of the tube currents, SNRs for MBIR and DLR were significantly higher than those for FBP and hybrid-type IR (p < 0.05). At the arterial phase, the mean difference in V for the CT protocol obtained at 600 or 100 mA and reconstructed with DLR was significantly smaller than that for others (p < 0.05). At the portal-venous phase, the mean differences in V for the CT protocol obtained at 100 mA and reconstructed with hybrid-type IR, MBIR, and DLR were significantly smaller than that for FBP (p < 0.05). CONCLUSIONS: Findings of our phantom study show that reconstruction method had influence on CADv merits for abdominal CT with not only standard but also reduced dose examinations and that DLR can potentially yield better image quality and CADv measurements than FBP, hybrid-type IR, or MBIR in this setting.

PURPOSE: The purpose of this study was to determine the capability of proton density with fat fraction (PD-FFQ) imaging to help assess hematopoietic ability and diagnose aplastic anemia in adults. METHODS: Between January 2021 and March 2023, patients diagnosed with aplastic anemia (AA: n = 14) or myelodysplastic syndrome (MDS: n = 14) were examined by whole-body PD-FFQ imaging, and 14 of 126 age and gender matched patients who had undergone the same PD-FFQ imaging were selected as control group. All proton density fat fraction (PDFF) index evaluations were then performed by using regions of interest (ROIs). Pearson's correlation was used to determine the relationship between blood test results and each quantitative index, and ROC-based positive test and discrimination analyses to compare capability to differentiate the AA from the non-AA group. Finally, sensitivity, specificity and accuracy of all quantitative indexes were compared by means of McNemar's test. RESULTS: Mean PDFF, standard deviation (SD) and percentage of coefficient of variation (%CV) for vertebrae showed significant correlation with blood test results (-0.52 ≤ r ≤ -0.34, p < 0.05). Specificity (SP) and accuracy (AC) of %CV of PDFF in vertebrae were significantly higher than those of mean PDFF in vertebrae and the posterior superior iliac spine (SP: p = 0.0002, AC: p = 0.0001) and SD of PDFF in vertebrae (SP: p = 0.008, AC: p = 0.008). Moreover, AC of SD of PDFF in vertebrae was significantly higher than that of mean PDFF in vertebrae and the posterior superior iliac spine (p = 0.03). CONCLUSION: Whole-body PD-FFQ imaging is useful for hematopoietic ability assessment and diagnosis of aplastic anemia in adults.