Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
463-01-005 ISMRM Abstract

Mamba-MRF:A Deep Mamba Network for Highly Accelerated Magnetic Resonance Fingerprinting Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Analysis Methods - Image Enhancement
463-01-005 · Data-Driven Reconstruction: From Networks to Clinical Practice · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row D
Keywords: Deep learning reconstruction Undersampling Magnetic Resoance Fingerprinting
Accepted
Tianyi Ding1, Hongli Chen1, Yang Gao2, Peng Wu 3,4, Zhuang Xiong5, Shanshan Shan6, Feng Liu7, Martijn A Cloos8, Hongfu Sun9
1School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
2School of Computer Science and Engineering, Central South University, Changsha, China
3Philips Healthcare, Guangzhou, China
4Clinical & Technical Support, Philips Healthcare, Guangzhou, China
5Image X Institute, The University of Sydney, Sydney, Australia
6School of Radiological and Interdisciplinary Sciences, Soochow University, Suzhou, China
7The University of Queensland, Brisbane, Australia
8Donders Center for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
9University of Newcastle, Newcastle, Australia
Presenting Author: Peng Wu

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Seow P, Kheok SW, Png MA, Chai PH, Yan TS, Tan EJ, Liauw L, Law YM, Anand CV, Lee W, Chen RC. Evaluation of compressed sense on image quality and reduction of mri acquisition time: A clinical validation study. Academic radiology. 2024 Mar 1;31(3):956-65.
2. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA. Magnetic resonance fingerprinting. Nature. 2013 Mar 14;495(7440):187-92.
3. Bloch F. Nuclear induction. Physical review. 1946 Oct 1;70(7-8):460.
4. Chen Y, Lu L, Zhu T, Ma D. Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy. Medical physics. 2022 Apr;49(4):2846-60.
5. Fang Z, Chen Y, Liu M, Xiang L, Zhang Q, Wang Q, Lin W, Shen D. Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting. IEEE transactions on medical imaging. 2019 Feb 13;38(10):2364-74.
6. Ding T, Gao Y, Xiong Z, Liu F, Cloos MA, Sun H. MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction. Information. 2025 Mar 11;16(3):218.
7. Guo H, Li J, Dai T, Ouyang Z, Ren X, Xia ST. Mambair: A simple baseline for image restoration with state-space model. InEuropean conference on computer vision 2024 Sep 29 (pp. 222-241). Cham: Springer Nature Switzerland.
8. Li P, Hu Y. Deep magnetic resonance fingerprinting based on local and global vision transformer. Medical Image Analysis. 2024 Jul 1;95:103198.
9. Soyak R, Navruz E, Ersoy EO, Cruz G, Prieto C, King AP, Unay D, Oksuz I. Channel attention networks for robust MR fingerprint matching. IEEE Transactions on Biomedical Engineering. 2021 Sep 30;69(4):1398-405.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/463-01-005