MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction
MRF-Mixer is a novel deep learning method for magnetic resonance fingerprinting (MRF) reconstruction, offering 200× faster processing (0.35 s on CPU and 0.3 ms on GPU) and 40% higher accuracy (lower MAE) than dictionary matching. It develops a simulation-driven approach using complex-valued multi-la...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/3/218 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850280194592473088 |
|---|---|
| author | Tianyi Ding Yang Gao Zhuang Xiong Feng Liu Martijn A. Cloos Hongfu Sun |
| author_facet | Tianyi Ding Yang Gao Zhuang Xiong Feng Liu Martijn A. Cloos Hongfu Sun |
| author_sort | Tianyi Ding |
| collection | DOAJ |
| description | MRF-Mixer is a novel deep learning method for magnetic resonance fingerprinting (MRF) reconstruction, offering 200× faster processing (0.35 s on CPU and 0.3 ms on GPU) and 40% higher accuracy (lower MAE) than dictionary matching. It develops a simulation-driven approach using complex-valued multi-layer perceptrons and convolutional neural networks to efficiently process MRF data, enabling generalization across sequence and acquisition parameters and eliminating the need for extensive in vivo training data. Evaluation on simulated and in vivo data showed that MRF-Mixer outperforms dictionary matching and existing deep learning methods for T1 and T2 mapping. In six-shot simulations, it achieved the highest PSNR (T1: 33.48, T2: 35.9) and SSIM (T1: 0.98, T2: 0.98) and the lowest MAE (T1: 28.8, T2: 4.97) and RMSE (T1: 72.9, T2: 13.67). In vivo results further demonstrate that single-shot reconstructions using MRF-Mixer matched the quality of multi-shot acquisitions, highlighting its potential to reduce scan times. These findings suggest that MRF-Mixer enables faster, more accurate multiparametric tissue mapping, substantially improving quantitative MRI for clinical applications by reducing acquisition time while maintaining imaging quality. |
| format | Article |
| id | doaj-art-49b6aaa66b9c46f6b8c5254457fa187b |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-49b6aaa66b9c46f6b8c5254457fa187b2025-08-20T01:48:50ZengMDPI AGInformation2078-24892025-03-0116321810.3390/info16030218MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting ReconstructionTianyi Ding0Yang Gao1Zhuang Xiong2Feng Liu3Martijn A. Cloos4Hongfu Sun5School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, AustraliaDonders Centre for Cognitive Neuroimaging, Radboud University, 6525 Nijmegen, The NetherlandsSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, AustraliaMRF-Mixer is a novel deep learning method for magnetic resonance fingerprinting (MRF) reconstruction, offering 200× faster processing (0.35 s on CPU and 0.3 ms on GPU) and 40% higher accuracy (lower MAE) than dictionary matching. It develops a simulation-driven approach using complex-valued multi-layer perceptrons and convolutional neural networks to efficiently process MRF data, enabling generalization across sequence and acquisition parameters and eliminating the need for extensive in vivo training data. Evaluation on simulated and in vivo data showed that MRF-Mixer outperforms dictionary matching and existing deep learning methods for T1 and T2 mapping. In six-shot simulations, it achieved the highest PSNR (T1: 33.48, T2: 35.9) and SSIM (T1: 0.98, T2: 0.98) and the lowest MAE (T1: 28.8, T2: 4.97) and RMSE (T1: 72.9, T2: 13.67). In vivo results further demonstrate that single-shot reconstructions using MRF-Mixer matched the quality of multi-shot acquisitions, highlighting its potential to reduce scan times. These findings suggest that MRF-Mixer enables faster, more accurate multiparametric tissue mapping, substantially improving quantitative MRI for clinical applications by reducing acquisition time while maintaining imaging quality.https://www.mdpi.com/2078-2489/16/3/218deep learningMR fingerprintingself-supervisedMRF-Mixer |
| spellingShingle | Tianyi Ding Yang Gao Zhuang Xiong Feng Liu Martijn A. Cloos Hongfu Sun MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction Information deep learning MR fingerprinting self-supervised MRF-Mixer |
| title | MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction |
| title_full | MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction |
| title_fullStr | MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction |
| title_full_unstemmed | MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction |
| title_short | MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction |
| title_sort | mrf mixer a simulation based deep learning framework for accelerated and accurate magnetic resonance fingerprinting reconstruction |
| topic | deep learning MR fingerprinting self-supervised MRF-Mixer |
| url | https://www.mdpi.com/2078-2489/16/3/218 |
| work_keys_str_mv | AT tianyiding mrfmixerasimulationbaseddeeplearningframeworkforacceleratedandaccuratemagneticresonancefingerprintingreconstruction AT yanggao mrfmixerasimulationbaseddeeplearningframeworkforacceleratedandaccuratemagneticresonancefingerprintingreconstruction AT zhuangxiong mrfmixerasimulationbaseddeeplearningframeworkforacceleratedandaccuratemagneticresonancefingerprintingreconstruction AT fengliu mrfmixerasimulationbaseddeeplearningframeworkforacceleratedandaccuratemagneticresonancefingerprintingreconstruction AT martijnacloos mrfmixerasimulationbaseddeeplearningframeworkforacceleratedandaccuratemagneticresonancefingerprintingreconstruction AT hongfusun mrfmixerasimulationbaseddeeplearningframeworkforacceleratedandaccuratemagneticresonancefingerprintingreconstruction |