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

[2512.03020] FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2512.03020 (cs) [Submitted on 2 Dec 2025 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction Authors: Kehan Qi , Saumya Gupta , Xiaoling Hu , Qingqiao Hu , Weimin Lyu , Yicun Wang , Chao Chen View PDF HTML (experimental) Abstract: Unrolled networks are widely used in Magnetic Resonance Imaging (MRI) reconstruction for their efficiency. Structured as a series of neural network stages (or cascades), an unrolled network takes a low-quality input and passes it sequentially through each stage to iteratively refine the reconstruction. However, unrolled networks typically exhibit unstable output quality across cascades, resulting in sub-optimal final reconstruction results. In this work, we address this inherent limitation of unrolled networks, drawing inspiration from recent Flow Matching paradigm. We first theoretically show that unrolled networks can be viewed as discretizations of approximate conditional probability flows. This connection shows that unrolled networks and Flow Matching are analogous in MRI reconstruction. Building upon this insight, we propose FLow-Aligned Training (FLAT), which (1) derives important cascade parameters from the Flow Matching discretization; and (2) aligns intermediate reconstructions with the ideal Flow Matching trajectory to improve cascade iteration stability and convergence. Experiments on three MRI datasets show that FLAT results in a stable trajectory across sub-networks, improving the quality of the final reconstruction. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2512.03020 [cs.CV] (or arXiv:2512.03020v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2512.03020 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kehan Qi [ view email ] [v1] Tue, 2 Dec 2025 18:48:10 UTC (4,835 KB) [v2] Sat, 31 Jan 2026 01:45:13 UTC (7,910 KB) [v3] Thu, 27 Aug 2026 01:43:08 UTC (7,635 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2025-12 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction#674256
• Deep learning methods for accelerated MRI reconstruction: Optimization-inspired unrolled networks and beyond#206998
• Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging#976498
• RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using autoencoders.#433827
• Enhancing compressed sensing and parallel imaging accelerated magnetic resonance angiography using a dual-domain projection generative adversarial network.#360885