Evaluation of carotid artery wall volume measurement using novel semiautomated analysis software

Abstract

Purpose:To evaluate semiautomated analysis software for measuring the total carotid arterial wall volume (TWV) as a measure of atheroma burden. Materials and Methods:Semiautomated-software and manual analyses of TWV measured by cardiovascular magnetic resonance (CMR) were compared in two phantom models, 10 subjects with no known carotid artery disease, and eight subjects with known carotid disease. The subjects were scanned twice for reproducibility. Results:In subjects with no known carotid disease, semiautomated analysis of 98% of slices showed an improved interstudy coefficient of variation (COV) compared to manual analysis of 50% of slices (4.0% vs. 6.2%, P ϭ 0.02). The proportion of matched cross-sectional slices usable for TWV measurement was superior (99% vs. 49%, P ϭ 0.005) and the median analysis time was shorter (31 minutes vs. 90 minutes, P Ͻ 0.001) using the semiautomated software. In subjects with known carotid disease, semiautomated (99% of slices) and manual (56% of slices) analyses had comparable interstudy COVs (4.1% vs. 3.9%, P ϭ 0.01). However, the proportion of matched cross-sectional slices usable for TWV measurement was greater using semiautomated contouring (96% vs. 56%, P ϭ 0.01). Conclusion:Carotid CMR measurement of TWV using novel semiautomated analysis software shows good reproducibility, enables greater coverage of arterial vessel wall length, and is considerably faster compared to manual contouring.

Fluid–structure interaction analysis of a patient‐specific right coronary artery with physiological velocity and pressure waveforms

“…The planes perpendicular to the centerline were computed at 1 mm intervals and used to re-slice the data set with cubic B-spline resampling [26]. The RCA was then semi-automatically segmented from the resulting stack of cross-sectional images [27]. The lumenal surface was constructed by interpolation of the extracted cross-sectional outlines using cubic B-splines.…”

Carotid intima-media thickness and distensibility measured by MRI at 3 T versus high-resolution ultrasound

“…high-grade ICA stenosis, by high-resolution 2D dark blood T2-weighted MRI at 3 T. In comparison to previous MRI studies investigating the wall properties of the CCA [10,13,[15][16][17] we additionally provide data on carotid artery compliance. In particular, we took the potential influence of haemodynamic conditions such as pulse pressure on both ultrasound and MRI measurements into account as these affect the calculation of the distensibility coefficient.…”

“…While one 3D T1-weighted fast spin echo (FSE) black blood MR technique resulted in improved visualization of small plaque components of ICA stenosis but reduced reliability for image quality [13], excellent reliability was demonstrated in another 3D study using black blood balanced steady state free precession (SSFP) for the evaluation of wall thickness [14]. Furthermore, semiautomated or automated data analysis tools improved reproducibility and accelerated vessel wall analysis [15,16]. Such MRI studies showed similar repeatability for the assessment of carotid wall thickness and small differences compared with ultrasound, most probably due to the inclusion of the adventitia by MRI [17].…”

Loss of fine structure and edge sharpness in fast‐spin‐echo carotid wall imaging: Measurements and comparison with multiple‐spin‐echo in normal and atherosclerotic subjects

“…The algorithm was implemented in MATLAB V R . Quantitative measurements of vascular parameters such as cross-sectional wall and lumen areas are obtained by semi-automatic detection of the vessel wall boundaries (6)(7)(8)(9)(10). The performances of these methods depend on objective image properties, such as resolution and CNR, but also on the subjective perception of edge sharpness observed by the human visual system.…”

“…In vivo multi-contrast MRI can discriminate the major plaque components (1,4,5): lipid-rich necrotic core, intraplaque hemorrhage, fibrous tissue, and calcification. Detection of vessel wall boundaries is performed by semi-automated methods (6)(7)(8)(9)(10) and can be challenging due to anatomical and signal features in the region of interest (ROI) of carotid arteries. The most common problem is blurring along the phase-encoding (PE) direction caused by random patient movements and physiological motion such as breathing, swallowing, and arterial pulsation.…”