基于条件流匹配的扩散 MRI 通用视野扩展方法
Generalizable field of view extension for diffusion MRI via conditional flow matching
研究者提出一种流匹配生成模型，通过预测六个方向的表观扩散系数（ADC）来扩展扩散 MRI 的上下视野，并以最近的两层已采集切片和全视野 T1w 图像为条件，兼顾局部扩散连续性与解剖一致性。
Authors:Gaurav Rudravaram, Lianrui Zuo, Ema Topolnjak, Michael E. Kim, Adam M. Saunders, Elyssa M. McMaster, Paula Trujillo, Ely E. Wesley, Colin D. McKnight, Pratik P. Pandharipande, Mayur B. Patel, Cayce Strength, Brenda T. Pund, Amelia W Maiga, Robert S. Dittus, David A. Bennett, Julie A. Schneider, Konstantinos Arfanakis, The HABS-HD Study Team, Derek B. Archer, Timothy J. Hohman, Yelena Bodien, Daniel C. Moyer, Kurt G. Schilling, Bennett A. Landman
Abstract:Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractography and connectivity studies. While deep generative models for dMRI FOV extension have been proposed, their generalizability remains limited by the acquisition-dependent nature of dMRI, where signal intensity varies with b-value, gradient direction, and scanner protocol, and the inherent 4D complexity of the data, making it difficult to train models that transfer across sites or acquisition schemes without retraining. We propose a flow-matching generative model that extends the dMRI FOV in both the superior and inferior directions by predicting apparent diffusion coefficients (ADC) in six directions, conditioned on the two nearest acquired slices and the full-FOV T1w image to enforce both local diffusion continuity and anatomical consistency. The ADC representation decouples the model from acquisition-specific b-values and gradient directions, providing a middle-ground across protocols. The model was trained on 3,103 subjects (5,105 sessions) across five different datasets spanning diverse acquisition schemes (b = 500 and 1000 s/mm$^2$; 21--192 gradient directions), four single-shell (b = 1000) and one multi-shell (b = 500/1000). Evaluated on 299 subjects across five independent datasets spanning b = 750--1500 s/mm$^2$ and 32--90 gradient directions, with single- and multi-shell schemes unseen during training, the proposed method achieves significantly improved bundle Dice score on downstream tractography (p < 0.01).
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17401 [cs.AI] |
| (or arXiv:2610.17401v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17401 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gaurav Rudravaram [view email]
[v1]
Mon, 12 Oct 2026 21:10:25 UTC (3,966 KB)
来源：arXiv：cs.AI · arxiv.org