Application of endoscopic and MRI image registration and fusion algorithm in gastrointestinal diseases(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第7期
- Page:
- 910-915
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Application of endoscopic and MRI image registration and fusion algorithm in gastrointestinal diseases
- Author(s):
- HAN Bing; DING Xiuting
- Department of Gastroenterology, Qinhuangdao Hospital, Peking University Third Hospital, Qinhuangdao 066000, China
- Keywords:
- Keywords: medical image registration multimodal fusion endoscopy magnetic resonance imaging gastrointestinal disease deep learning computer-aided diagnosis
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.07.011
- Abstract:
- Abstract: Objective To develop a robust and precise registration and fusion algorithm for endoscopic and MRI images, and systematically validate its application potential in clinical scenarios including precise localization and boundary definition of gastrointestinal tumors and the evaluation of inflammatory bowel diseases, thereby promoting the practical application of multimodal image fusion in digestive endoscopy. Methods A non-rigid registration framework (Hybrid-DRL) was established based on hybrid features and deep reinforcement learning. A paired dataset consisting of endoscopic images (white light, narrow band imaging, and blue light imaging modes) and three-dimensional T2-weighted MR images from 120 patients was used for model training and testing. The algorithm first extracted dense feature maps based on deep learning and improved speeded up robust features key points to construct hybrid features, and then optimized non-rigid deformation field parameters through the proximal policy optimization-based deep reinforcement learning agent. Results The average target registration error of the proposed algorithm was significantly lower than that of the traditional mutual information-based algorithm and the deep learning-based VoxelMorph algorithm (P<0.01). In clinical applications, the fusion images improved the inter-observer consistency of boundary observation for early gastric cancer from 0.72 to 0.91, and assisted in detecting 12% of cases with submucosal infiltration signs that were difficult to identify by traditional endoscopy. Conclusion The proposed Hybrid-DRL algorithm can effectively achieve high-precision registration of endoscopic and MRI images, and possesses promising value in improving the diagnostic and therapeutic accuracy of gastrointestinal diseases. These findings provide a reliable technical framework and empirical basis for the precise diagnosis and treatment of gastrointestinal diseases, as well as key support for the transformation of multimodal intelligent fusion from algorithm innovation to clinical application.
Last Update: 2026-07-22