B-mode ultrasound provides the highest intraobserver and interscan reproducibility for measuring carotid atherosclerosis compared with 3D ultrasound and MRI, according to recent research from the University of Western Ontario in London, Ontario. However, 3D ultrasound and MRI did yield strong results in quantifying vessel wall volume (VWV).
"Although the measurement of [intima media thickness] provided the lowest intraobserver and interscan variability, VWV measurements derived from 3DUS and MR images provided similar intraobserver and interscan reliability with the advantage that these 3-dimensional measurements also provided the increased sensitivity required for longitudinal studies of carotid atherosclerosis," wrote a research team led by Micaela Egger.
The researchers sought to compare the intraobserver and interscan variability of carotid atherosclerosis measured using B-mode ultrasound for quantifying intima media thickness (IMT), 3D ultrasound for quantifying VWV and total plaque volume (TPV), and MRI for measuring VWV. They also evaluated the association of these measurements and sample sizes required to detect specific changes in patients with moderate atherosclerosis (Journal of Ultrasound in Medicine, September 2008, Vol. 27: 9, pp. 1321-1334).
Ten volunteer patients from the Premature Atherosclerosis Clinic and Stroke Prevention Clinic at University Hospital, London Health Sciences Centre in London, Ontario, were evaluated with B-mode ultrasound, MRI, and 3D ultrasound twice within 14 ± 2 days. Single observers then performed measurements using manual (VWV and TPV) and semiautomated (IMT) segmentation.
Intraobserver coefficients of variation were 3.4% (IMT), 4.7% (3DUS VWV), 6.5% (MRI VWV), and 23.9% (3DUS TPV), according to the researchers. Interscan coefficients of variation were 8.1% (MRI VWV), 8.9% (IMT), 13.5% (3DUS VWV), and 46.6% (3DUS TPV). Scan-rescan linear regressions were significant for 3DUS TPV (R2 = 0.57), 3DUS VWV (R2 = 0.59), and IMT (R2 = 0.75) and significantly different (p < 0.05) for MRI VWV (R2 = 0.87), the authors wrote.
The researchers acknowledged several limitations of the study, including its small sample size and the limitation of the study to patients with moderate atherosclerosis and without hemodynamically severe carotid stenosis.
"In our experience, these study patients are typical (in terms of age, risk factors, and the extent of atherosclerotic disease) of those who would be included in a clinical study of atherosclerosis disease progression and treatment," the authors wrote. "Therefore, although this initial study was limited to patients with moderate atherosclerosis and without carotid stenosis, the results can be viewed as a guide for planning clinical imaging studies of carotid atherosclerosis."
By Erik L. Ridley
AuntMinnie.com staff writer
September 19, 2008
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)




