Neurovascular Imaging with Ultra-High-Resolution Photon-Counting CT: Preliminary Findings on Image Quality Evaluation.

BACKGROUND AND PURPOSE: The first-generation photon-counting detector (PCD) CT was recently introduced into clinical practice and represents a promising innovation in high-resolution CT imaging. The purpose of this study was to assess the image quality of ultra-high-resolution (UHR) PCD-CT compared with energy-integrating detector (EID)-CT, and to explore different reconstruction kernel sharpness levels for the evaluation of intracranial aneurysms. MATERIALS AND METHODS: Ten patients with intracranial saccular aneurysms, who had previously undergone conventional EID-CT, were prospectively enrolled. CT angiograms were acquired on a clinical dual-source PCD-CT in UHR mode, and reconstructed with four vascular kernels (Bv36, Bv40, Bv44, Bv48). Quantitative and qualitative image quality parameters of the intracranial arteries were evaluated. For the quantitative analysis (image noise, SNR, CNR), regions of interest were manually placed at standard anatomical intracranial and extracranial locations by one author. In addition, vessel border sharpness was evaluated quantitatively. For the qualitative analysis, three blinded neuroradiologists rated PCD-CT and EID-CT image quality for the evaluation of the intracranial vessels (i.e., the aneurysms and nine standard vascular branching locations) on a 5-point Likert-type scale. Additionally, readers independently selected their preferred kernel among the four kernels evaluated on PCD-CT. RESULTS: In terms of quantitative image quality, Bv48, the sharpest kernel, yielded increased image noise, and decreased SNR and CNR parameters compared to Bv36, the smoothest kernel. Compared to EID-CT, the Bv48 kernel offered better quantitative image quality for the evaluation of small intracranial vessels (p < .001). Image quality ratings of the Bv48 were superior to those of the EIDCT, and not significantly different from ratings of the B44 reconstruction kernel. When comparing side-by-side all four PCD-CT reconstruction kernels, readers selected the B48 kernel as the best to visualize the aneurysms in 80% of cases. CONCLUSIONS: UHR PCD-CT provides improved image quality for neurovascular imaging. Although the less sharp kernels provided superior SNR and CNR, the sharpest kernels delivered the best subjective image quality on PCD-CT for the evaluation of intracranial aneurysms.CNR ＝ Contrast-to-Noise Ratio; EID-CT ＝ Energy-Integrating Detector CT; PCD-CT ＝ Photon-Counting Detector CT; QIR ＝ Quantum Iterative Reconstruction; UHR ＝ Ultra-High-Resolution.

AI-based, automated chamber volumetry from gated, non-contrast CT.

BACKGROUND: Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT) scans can be useful for potential screening of heart failure. OBJECTIVES: To validate a new, fully automated, AI-based method for cardiac volume and myocardial mass quantification from NCCT scans compared to contrasted CT Angiography (CCTA). METHODS: Of a retrospectively collected cohort of 1051 consecutive patients, 420 patients had both NCCT and CCTA scans at mid-diastolic phase, excluding patients with cardiac devices. Ground truth values were obtained from the CCTA scans. RESULTS: The NCCT volume computation shows good agreement with ground truth values. Volume differences [95% CI ] and correlation coefficients were: -9.6 [-45; 26] mL, r â€‹= â€‹0.98 for LV Total, -5.4 [-24; 13] mL, r â€‹= â€‹0.95 for LA, -8.7 [-45; 28] mL, r â€‹= â€‹0.94 for RV, -5.2 [-27; 17] mL, r â€‹= â€‹0.92 for RA, -3.2 [-42; 36] mL, r â€‹= â€‹0.91 for LV blood pool, and -6.7 [-39; 26] g, r â€‹= â€‹0.94 for LV wall mass, respectively. Mean relative volume errors of less than 7% were obtained for all chambers. CONCLUSIONS: Fully automated assessment of chamber volumes from NCCT scans is feasible and correlates well with volumes obtained from contrast study.

Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study.

BACKGROUND. Deep learning-based convolutional neural networks have enabled major advances in development of artificial intelligence (AI) software applications. Modern AI applications offer comprehensive multiorgan evaluation. OBJECTIVE. The purpose of this article was to evaluate the impact of an automated AI platform integrated into clinical workflow for chest CT interpretation on radiologists' interpretation times when evaluated in a real-world clinical setting. METHODS. In this prospective single-center study, a commercial AI software solution was integrated into clinical workflow for chest CT interpretation. The software provided automated analysis of cardiac, pulmonary, and musculoskeletal findings, including labeling, segmenting, and measuring normal structures as well as detecting, labeling, and measuring abnormalities. AI-annotated images and autogenerated summary results were stored in the PACS and available to interpreting radiologists. A total of 390 patients (204 women, 186 men; mean age, 62.8 ± 13.3 [SD] years) who underwent out-patient chest CT between January 19, 2021, and January 28, 2021, were included. Scans were randomized using 1:1 allocation between AI-assisted and non-AI-assisted arms and were clinically interpreted by one of three cardiothoracic radiologists (65 scans per arm per radiologist; total of 195 scans per arm) who recorded interpretation times using a stopwatch. Findings were categorized according to review of report impressions. Interpretation times were compared between arms. RESULTS. Mean interpretation times were significantly shorter in the AI-assisted than in the non-AI-assisted arm for all three readers (289 ± 89 vs 344 ± 129 seconds, p < .001; 449 ± 110 vs 649 ± 82 seconds, p < .001; 281 ± 114 vs 348 ± 93 seconds, p = .01) and for readers combined (328 ± 122 vs 421 ± 175 seconds, p < .001). For readers combined, the mean difference was 93 seconds (95% CI, 63-123 seconds), corresponding with a 22.1% reduction in the AI-assisted arm. Mean interpretation time was also shorter in the AI-assisted arm compared with the non-AI-assisted arm for contrast-enhanced scans (83 seconds), noncontrast scans (104 seconds), negative scans (84 seconds), positive scans without significant new findings (117 seconds), and positive scans with significant new findings (92 seconds). CONCLUSION. Cardiothoracic radiologists exhibited a 22.1% reduction in chest CT interpretations times when they had access to results from an automated AI support platform during real-world clinical practice. CLINICAL IMPACT. Integration of the AI support platform into clinical workflow improved radiologist efficiency.