Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

Distinguishing true from pseudo hematoma in the cervical spinal canal using postmortem computed tomography.

Spinal cord injury is difficult to detect directly on postmortem computed tomography (PMCT) and it is usually diagnosed by indirect findings such as a hematoma in the spinal canal. However, we have encountered cases where the hematoma-like high-attenuation area in the cervical spinal canal was visible on PMCT, while no hematoma was observed at autopsy; we called it a "pseudo hematoma in the cervical spinal canal (pseudo-HCSC)." In this retrospective study, we performed statistical analysis to distinguish true from pseudo-HCSC. The cervical spinal canal was dissected in 35 autopsy cases with a hematoma-like high-attenuation area (CT values 60-100 Hounsfield Unit (HU)) in the spinal canal from the first to the fourth cervical vertebrae in axial slices of PMCT images. Of these 22 had a hematoma and 13 did not (pseudo-HCSC). The location and length of the hematoma-like high-attenuation and spinal cord areas were assessed on reconstructed PMCT images, true HCSC cases had longer the posterior hematoma-like area and shorter the spinal cord area in the midline of the spinal canal (P < 0.05). Furthermore, we found that true HCSC cases were more likely to have fractures and gases on PMCT while pseudo-HCSC cases were more likely to have significant facial congestion (P < 0.05). We suggest that pseudo-HCSC on PMCT is related to congestion of the internal vertebral venous plexus. This study raises awareness about the importance of distinguishing true HCSC from pseudo-HCSC in PMCT diagnosis, and it also presents methods for differentiation between these two groups.

Common and uncommon vascular injuries and endovascular treatment associated with pelvic blunt trauma: a real-world experience.

Pelvic fractures are common in cases of blunt trauma, which is strongly associated with mortality. Transcatheter arterial embolization is a fundamental treatment strategy for fatal arterial injuries caused by blunt pelvic trauma. However, vascular injuries due to blunt pelvic trauma can show various imaging findings other than arterial hemorrhage. We present a pictorial review of common and uncommon vascular injuries, including active arterial bleeding, pseudoaneurysm, arteriovenous fistula, arterial occlusion, vasospasm, and active venous bleeding. Knowledge of these vascular injuries can help clinicians select the appropriate therapeutic strategy and thus save lives.

The use of viabahn VBX stent-grafts for the treatment of extrahepatic portal vein hemorrhage.

Percutaneous transhepatic stent-graft placement usually requires more than a 12 F sheath, which has a higher potential risk of peritoneal hemorrhage. A case of postoperative portal vein hemorrhage after pancreaticoduodenectomy treated using a Viabahn VBX balloon expanding stent-graft is described in this report. The stent-graft was delivered using an 8 F sheath through a transhepatic approach and deployed from the superior mesenteric vein to the main portal vein. Hemostasis was achieved and graft patency was confirmed one year after the procedure.

Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area.

In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow.