arXiv AI
4d ago

Generalizable field of view extension for diffusion MRI via conditional flow matching

arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractogra...

By 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
arXiv Machine Learning
Aug 27

FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

FlowMoDL is an unrolled neural network designed for highly accelerated 4D flow MRI reconstruction, optimizing both anatomical magnitude and phase-derived velocity accuracy. It alternates a learned (3+1)D spatiotemporal denoiser with conjugate‑gradient data‑consistency updates, using a dual‑pathway conditioning scheme to handle acceleration factors from 10× to 50×. Trained with a deep‑supervision composite loss that penalizes velocity magnitude and angular errors, FlowMoDL outperforms classical and deep‑learning baselines on the multi‑center CMRx4DFlow dataset, achieving superior gradient‑step efficiency and robust convergence across all acceleration factors.

By Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter
arXiv Machine Learning
Sep 18

Fast Cross-Strength Multi-Contrast Brain MRI Translation using Latent Bridge Matching

The paper presents a unified conditional model for translating brain MRI scans across different field strengths and modalities using conditional latent bridge matching. The single model achieves competitive results on all three tasks of the MRIxFields2026 challenge without task‑specific architectures, and it generates 30 axial slices in under 90 seconds or a full volume in under 70 seconds on a single NVIDIA A5000 GPU. Extensive ablations of the model’s components are also provided.

By Siddharth Srivastava, Till Bretschneider
arXiv Computer Vision
Aug 28

FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction

FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction proposes a new training method for unrolled neural networks used in MRI reconstruction. By interpreting unrolled networks as discretizations of conditional probability flows, the authors derive cascade parameters from Flow Matching and align intermediate reconstructions with the ideal Flow Matching trajectory. Experiments on three MRI datasets demonstrate that FLAT stabilizes the reconstruction trajectory across sub-networks and improves the final reconstruction quality.

By Kehan Qi, Saumya Gupta, Xiaoling Hu, Qingqiao Hu, Weimin Lyu, Yicun Wang, Chao Chen