Eur Radiol. 2026 Jan 31. doi: 10.1007/s00330-026-12347-4. Online ahead of print.
ABSTRACT
OBJECTIVES: This study developed an automated AI-based method for accurate image reconstruction, stenosis detection and plaque calculation in high-resolution magnetic resonance vessel wall imaging (HR-MRVWI) and compared its performance with radiologists.
MATERIALS AND METHODS: A deep learning algorithm trained on HR-MRVWI was collected retrospectively from three tertiary hospitals. An independent test set was collected prospectively at another hospital. Model performance was evaluated via the Dice similarity coefficient, average centerline distance and average surface distance in centerline extraction and vessel wall segmentation. Two radiologists reviewed the reconstructed images in randomized order to determine whether the quality matched the clinical diagnosis. The stenosis diagnosis and plaque calculation of the algorithm were compared with the ground truth of the consensus by two radiologists. The relationships of the calculated parameters with plaque vulnerability were also analyzed.
RESULTS: 476 patients (mean age 61 years ± 15 [SD], 286 men) were evaluated. The accuracy of image reconstruction in the independent test set was 92.3%. The consistency between the radiologists and the deep learning-assisted algorithm for stenosis detection was 0.89 (95% CI: 85.4, 90.2) in ≥ 50% stenosis. The accuracies of algorithm in normalized wall index, eccentricity and remodeling indices were 0.94, 0.83 and 0.87. The normalized wall index was highly related to plaque vulnerability. The AI-assisted in diagnosis and vessel wall analysis, which reduced the time from 32.0 ± 11.8 to 12.9 ± 4.3 min (p < 0.001).
CONCLUSION: A deep learning algorithm for HR-MRVWI interpretation could achieve image reconstruction, vessel stenosis and plaque calculation, which has satisfactory diagnostic performance.
KEY POINTS: Question Can a deep learning system achieve image reconstruction, stenosis diagnosis and plaque calculation in high-resolution MR vessel wall imaging (HR-MRVWI)? Findings The overall time reduced from 32.0 ± 11.8 to 12.9 ± 4.3 min (p < 0.001) with the aid of the system. Clinical relevance This effective deep learning system has great potential for processing head and neck HR-MRVWI images; it assists radiologists’ workloads and saves considerable time in hospitals. Additionally, it provides plaque-related parameters automatically for the evaluation of atherosclerosis patients.
PMID:41619006 | DOI:10.1007/s00330-026-12347-4
