Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations

[2603.22627] Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Image and Video Processing arXiv:2603.22627 (eess) [Submitted on 23 Mar 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations Authors: Heejong Kim , Abhishek Thanki , Roel van Herten , Daniel Margolis , Mert R Sabuncu View PDF HTML (experimental) Abstract: Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore acquired to provide complementary anatomical information. Conventional integration of these views relies on registration followed by interpolation, which can degrade fine structural details. Recent deep learning-based super-resolution (SR) approaches have demonstrated strong performance in enhancing single-view images. However, their clinical reliability is often limited by the need for large-scale training datasets, resulting in increased dependence on cohort-level priors. Self-supervised strategies offer an alternative by learning directly from the target scans. Prior work either neglects the existence of multi-view information or assumes that in-plane information can supervise through-plane reconstruction under the assumption of pre-alignment between images. However, this assumption is rarely satisfied in clinical settings. In this work, we introduce Single-Subject Implicit Multi-View Super-Resolution for MRI (SIMS-MRI), a framework that operates solely on anisotropic multi-view scans from a single patient without requiring pre- or post-processing. Our method combines a multi-resolution hash-encoded implicit representation with learned inter-view alignment to generate a spatially consistent isotropic reconstruction. We validate the SIMS-MRI pipeline on both simulated brain and clinical prostate MRI datasets. Code will be made publicly available for reproducibility: this https URL Comments: 11 pages, 3 figures. Accepted at MICCAI 2026 Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.22627 [eess.IV] (or arXiv:2603.22627v2 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2603.22627 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Abhishek Thanki [ view email ] [v1] Mon, 23 Mar 2026 23:00:18 UTC (2,151 KB) [v2] Tue, 25 Aug 2026 04:34:04 UTC (2,150 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2026-03 Change to browse by: cs cs.CV eess References & Citations NASA ADS 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) Major funding support from

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