Academic Journal
Deep learning for high-resolution magnetic resonance vessel wall imaging: image reconstruction, stenosis diagnosis and plaque calculation.
| Τίτλος: | Deep learning for high-resolution magnetic resonance vessel wall imaging: image reconstruction, stenosis diagnosis and plaque calculation. |
|---|---|
| Συγγραφείς: | Fu F; Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.; Institute for Medical Imaging Technology, Shanghai, China., Lin Z; United Imaging Healthcare Co., Ltd., Shanghai, China., Yang X; United Imaging Healthcare Co., Ltd., Shanghai, China., Huang X; Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.; Institute for Medical Imaging Technology, Shanghai, China., Chen X; Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.; Institute for Medical Imaging Technology, Shanghai, China., Meng H; Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.; Institute for Medical Imaging Technology, Shanghai, China., Li B; Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. lb10363@rjh.com.cn.; Institute for Medical Imaging Technology, Shanghai, China. lb10363@rjh.com.cn. |
| Πηγή: | European radiology [Eur Radiol] 2026 Jun; Vol. 36 (6), pp. 5169-5181. Date of Electronic Publication: 2026 Jan 31. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Berlin : Springer International, c1991- |
| Ιατρικοί όροι (MeSH): | Plaque, Atherosclerotic*/diagnostic imaging , Image Interpretation, Computer-Assisted*/methods , Magnetic Resonance Imaging*/methods , Image Processing, Computer-Assisted*/methods , Magnetic Resonance Angiography*/methods , Deep Learning*, Humans ; Algorithms ; Middle Aged ; Female ; Male ; Retrospective Studies ; Aged |
| Περίληψη: | 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. (© 2026. The Author(s), under exclusive licence to European Society of Radiology.) |
| Competing Interests: | Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Biao Li. Conflict of interest: Zengping Lin and Xiong Yang are employees of United Imaging Healthcare. The remaining authors declare no competing interests. Statistics and biometry: One of the authors has significant statistical expertise. Informed consent: Written informed consent was obtained from all subjects (patients) in this study. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: None. Methodology: Retrospective Diagnostic or prognostic study Multicenter study |
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| Grant Information: | No.82302129 National Natural Science Foundation of China; 23YF1424800 Shanghai Sailing Program; 23CGA19 Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission |
| Contributed Indexing: | Keywords: Deep learning; Diagnosis; Image processing; Magnetic resonance imaging |
| Entry Date(s): | Date Created: 20260131 Date Completed: 20260706 Latest Revision: 20260706 |
| Update Code: | 20260707 |
| DOI: | 10.1007/s00330-026-12347-4 |
| PMID: | 41619006 |
| Βάση Δεδομένων: | MEDLINE |
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