Optimizing the number of valid measurements for the attenuation coefficient to assess hepatic steatosis in MAFLD patients: A study of 139 patients who underwent liver biopsy.

PURPOSE: We investigated the optimal number of valid measurements (VMs) for the attenuation coefficient (AC) to assess liver steatosis using attenuation imaging (ATI) and explored factors that may affect AC measurement in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). MATERIALS AND METHODS: A total of 139 patients with MAFLD who underwent ATI and liver biopsy were enrolled. Hepatic steatosis was graded as S0-3 according to the SAF scoring system. The AC values from 1, 2, 3, 5, and 7 VMs were compared with the degree of liver steatosis. The correlation between AC values from different VMs was analyzed. The diagnostic performance of AC from different VMs at each steatosis grade was compared. The factors related to AC were identified using linear regression analysis. RESULTS: The mean AC values from 1, 2, 3, 5, and 7 VMs were not significantly different between grades S0-3 (p=n.s. for all). Bland-Altman analysis showed the mean difference in AC values of 3 VMs and 7 VMs was 0.003 dB/cm/MHz, which was smaller compared with 2 VMs, and close to 5 VMs. The intraclass correlation coefficients of AC were all > 0.90 among different VM groups. AC values from different VMs all significantly predicted steatosis grade ≥S1, ≥S2, and S3 without significant statistical differences (p=n.s. for all). The multivariate analysis showed that the hepatic steatosis grade and triglyceride level were factors independently associated with AC. CONCLUSION: Three valid measurements of AC may be adequate to ensure the accuracy and reproducibility of hepatic steatosis assessment. The degree of liver steatosis and the triglyceride level significantly affected AC values.

3D carotid artery segmentation using shape-constrained active contours.

Reconstruction of the carotid artery is demanded in the detection and characterization of atherosclerosis. This study proposes a shape-constrained active contour model for segmenting the carotid artery from MR images, which embeds the output of the deep learning network into the active contour. First the centerline of the carotid artery is localized and then modified active contour initialized from the centerline is used to extract the vessel lumen, finally the probability atlas generated by the deep learning network in polar representation domain is integrated into the active contour as a prior information to detect the outer wall. The results showed that the proposed active contour model was efficient and comparable to manual segmentation.

The Need to Shift from Morphological to Structural Assessment for Carotid Plaque Vulnerability.

Degree of luminal stenosis is generally considered to be an important indicator for judging the risk of atherosclerosis burden. However, patients with the same or similar degree of stenosis may have significant differences in plaque morphology and biomechanical factors. This study investigated three patients with carotid atherosclerosis within a similar range of stenosis. Using our developed fluid-structure interaction (FSI) modelling method, this study analyzed and compared the morphological and biomechanical parameters of the three patients. Although their degrees of carotid stenosis were similar, the plaque components showed a significant difference. The distribution range of time-averaged wall shear stress (TAWSS) of patient 2 was wider than that of patient 1 and patient 3. Patient 2 also had a much smaller plaque stress compared to the other two patients. There were significant differences in TAWSS and plaque stresses among three patients. This study suggests that plaque vulnerability is not determined by a single morphological factor, but rather by the combined structure. It is necessary to transform the morphological assessment into a structural assessment of the risk of plaque rupture.