Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-03-187 ISMRM Abstract

Neural Networks for Dictionary-Free MR Fingerprint Matching in the Brain: A Systematic Review

Primary: Acquisition & Reconstruction - AI methods
Secondary: Analysis Methods - Data Processing
570-03-187 · AI Methods · Wednesday, 13 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: Brain Machine Learning/Artificial Intelligence Systematic Review Magnetic resonance fingerprinting Quantitative Parameter Mapping
Accepted
Ela Kanani 1,2, Geoff J Parker1,2,3, Elizabeth Powell1,2
1UCL Hawkes Institute, University College London, London, United Kingdom
2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
3Bioxydyn Limited, Manchester, United Kingdom
Presenting Author: Ela Kanani

Synopsis

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References

1. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192. doi:10.1038/nature11971 [doi]
2. Wang Z, Zhang Q, Yuan J, Wang X. MRF denoising with compressed sensing and adaptive filtering. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE; 2014:870-873. doi:10.1109/isbi.2014.6868009 [doi]
3. Su P, Mao D, Liu P, et al. Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL. Magnetic Resonance in Med. 2017;78(5):1812-1823. doi:10.1002/mrm.26587 [doi]
4. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Published online March 29, 2021:n71. doi:10.1136/bmj.n71 [doi]
5. Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence. 2020;2(2):e200029. doi:10.1148/ryai.2020200029 [doi]
6. Chen D, Davies ME, Golbabaee M. Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations. In: Martel AL, Abolmaesumi P, Stoyanov D, et al., eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Vol 12262. Lecture Notes in Computer Science. Springer International Publishing; 2020:13-22. doi:10.1007/978-3-030-59713-9_2 [doi]
7. Cohen O, Yu VY, Tringale KR, et al. CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magnetic Resonance in Medicine. 2023;89(1):233-249. doi:10.1002/mrm.29448 [doi]
8. Zhang Q, Su P, Chen Z, et al. Deep learning-based MR fingerprinting ASL ReconStruction (DeepMARS). Magn Reson Med. 2020;84(2):1024-1034. doi:10.1002/mrm.28166 [doi]
9. Fan H, Su P, Huang J, Liu P, Lu H. Multi-band MR fingerprinting (MRF) ASL imaging using artificial-neural-network trained with high-fidelity experimental data. Magnetic Resonance in Medicine. 2021;85(4):1974-1985. doi:10.1002/mrm.28560 [doi]
10. Golbabaee M, Buonincontri G, Pirkl CM, et al. Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks. Medical Image Analysis. 2021;69. doi:10.1016/j.media.2020.101945 [doi]
11. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539 [doi]
12. Oğuz A, Ertuğrul ÖF. Introduction to deep learning and diagnosis in medicine. In: Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods. Elsevier; 2023:1-40. doi:10.1016/B978-0-323-96129-5.00003-2 [doi]
13. Balsiger F, Shridhar Konar A, Chikop S, et al. Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks. In: Knoll F, Maier A, Rueckert D, eds. Machine Learning for Medical Image Reconstruction. Vol 11074. Lecture Notes in Computer Science. Springer International Publishing; 2018:39-46. doi:10.1007/978-3-030-00129-2_5 [doi]
14. Rohatgi A. WebPlotDigitizer. https://automeris.io
15. Gu Y, Pan Y, Fang Z, et al. Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain. Magnetic Resonance in Medicine. 2024;91(3):1149-1164. doi:10.1002/mrm.29905 [doi]
16. Kang B, Kim B, Schär M, Park H, Heo H. Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging. Magnetic Resonance in Med. 2021;85(4):2040-2054. doi:10.1002/mrm.28573 [doi]
17. Kim B, Schar M, Park H, Heo HY. A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging. Neuroimage. 2020;221(cpp, 9215515):117165. doi:10.1016/j.neuroimage.2020.117165 [doi]
18. Chen Y, Fang Z, Hung SC, Chang WT, Shen D, Lin W. High-resolution 3D MR Fingerprinting using parallel imaging and deep learning. NeuroImage. 2020;206. doi:10.1016/j.neuroimage.2019.116329 [doi]
19. Liu Y, Chen Y, Yap PT. Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting. In: Vol 12906 LNCS. 2021:161-170. doi:10.1007/978-3-030-87231-1_16 [doi]
20. Fatania K, Chau KY, Pirkl CM, Menzel MI, Golbabaee M. Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI. In: Vol 2023-April. 2023. doi:10.1109/ISBI53787.2023.10230440 [doi]
21. Weigand-Whittier J, Sedykh M, Herz K, et al. Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network. Magnetic Resonance in Medicine. 2023;89(5):1901-1914. doi:10.1002/mrm.29574 [doi]
22. Gu Y, Pan Y, Fang Z, et al. Deep-Learning Based T1 and T2 Quantification from Undersampled Magnetic Resonance Fingerprinting Data to Track Tracer Kinetics in Small Laboratory Animals. In: Vol 13436 LNCS. 2022:432-441. doi:10.1007/978-3-031-16446-0_41 [doi]
23. Singh M, Jiang S, Li Y, van Zijl P, Zhou J, Heo HY. Bloch simulator–driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging. Magnetic Resonance in Medicine. 2023;90(4):1518-1536. doi:10.1002/mrm.29748 [doi]
24. Cabini RF, Barzaghi L, Cicolari D, et al. Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting. NMR in Biomedicine. 2024;37(1). doi:10.1002/nbm.5028 [doi]
25. Pirkl CM, Gómez PA, Lipp I, et al. Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting. In: Vol 121. 2020:638-654.
26. Cohen O, Otazo R. Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule. NMR in Biomedicine. 2023;36(10). doi:10.1002/nbm.4954 [doi]
27. Virtue P, Yu SX, Lustig M. Better than real: Complex-valued neural nets for MRI fingerprinting. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE; 2017:3953-3957. doi:10.1109/ICIP.2017.8297024 [doi]
28. Kang B, Heo HY, Park HW. Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification. In: Vol 13436 LNCS. 2022:387-396. doi:10.1007/978-3-031-16446-0_37 [doi]
29. Chen D, Davies ME, Golbabaee M. Deep Unrolling for Magnetic Resonance Fingerprinting. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE; 2022:1-4. doi:10.1109/ISBI52829.2022.9761475 [doi]
30. Cohen O, Zhu B, Rosen MS. MR fingerprinting Deep RecOnstruction NEtwork (DRONE). Magnetic Resonance in Med. 2018;80(3):885-894. doi:10.1002/mrm.27198 [doi]
31. Perlman O, Ito H, Herz K, et al. Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nature Biomedical Engineering. 2022;6(5):648-657. doi:10.1038/s41551-021-00809-7 [doi]
32. Hoppe E, Körzdörfer G, Würfl T, et al. Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series. Stud Health Technol Inform. 2017;243:202-206. doi: 10.3233/978-1-61499-808-2-202 [doi]
33. Hoppe E, Thamm F, Körzdörfer G, et al. Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks. Stud Health Technol Inform. 2019;267(ck1, 9214582):126-133. doi:10.3233/SHTI190816 [doi]
34. Hoppe E, Thamm F, Körzdörfer G, et al. RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting. In: Shen D, Liu T, Peters TM, et al., eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Vol 11766. Lecture Notes in Computer Science. Springer International Publishing; 2019:92-100. doi:10.1007/978-3-030-32248-9_11 [doi]
35. Oksuz I, Cruz G, Clough J, et al. Magnetic Resonance Fingerprinting Using Recurrent Neural Networks. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE; 2019:1537-1540. doi:10.1109/ISBI.2019.8759502 [doi]
36. Heo J, Song P, Liu W, Weller A. Physics-Based Decoding Improves Magnetic Resonance Fingerprinting. In: Vol 14221 LNCS. 2023:446-456. doi:10.1007/978-3-031-43895-0_42 [doi]
37. Fang Z, Chen Y, Liu M, et al. Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting. IEEE Trans Med Imaging. 2019;38(10):2364-2374. doi:10.1109/TMI.2019.2899328 [doi]
38. Fang Z, Chen Y, Nie D, Lin W, Shen D. RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting. In: Shen D, Liu T, Peters TM, et al., eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Vol 11766. Lecture Notes in Computer Science. Springer International Publishing; 2019:101-109. doi:10.1007/978-3-030-32248-9_12 [doi]
39. Li P, Hu Y. Learned Tensor Low-CP-Rank and Bloch Response Manifold Priors for Non-Cartesian MRF Reconstruction. IEEE Trans Med Imaging. 2023;42(12):3702-3714. doi:10.1109/TMI.2023.3302872 [doi]

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