基于成像物理模型与流形结构的自监督磁共振指纹参数量化方法
收稿日期: 2025-06-27
网络出版日期: 2025-08-18
基金资助
国家自然科学基金资助项目(62371167);黑龙江省自然科学基金资助项目(YQ2021F005)
Self-supervised Magnetic Resonance Fingerprint Parameter Quantization Method Based on Imaging Physical Model and Manifold Structure
Received date: 2025-06-27
Online published: 2025-08-18
磁共振指纹成像是一种高效的多参数定量成像技术，但传统方法依赖信号字典进行参数量化，存在离散化误差大与匹配效率低下等问题．针对现有监督学习方法依赖伪标签、缺乏物理可解释性的局限性，本文提出了一种融合成像物理模型与流形结构建模的自监督磁共振指纹参数量化方法．该方法通过布洛赫方程驱动的自监督物理一致性学习获得可靠的无标签约束，并结合流形结构驱动的知识蒸馏，将长帧的特征迁移至短帧模型，实现物理约束与结构先验的联合优化，从而同时提升无标签条件下的精度与效率．实验验证了本方法在准确性与鲁棒性方面的优势，为实现高效可靠的磁共振指纹参数估计提供了新思路．
李晓迪 , 纪雨萍 , 胡悦 . 基于成像物理模型与流形结构的自监督磁共振指纹参数量化方法[J]. 波谱学杂志, 2026 , 43(1) : 46 -60 . DOI: 10.11938/cjmr20253173
Magnetic resonance fingerprint (MRF) is an efficient multi-parameter quantitative imaging technology. However, traditional methods relying on signal dictionaries for parameter quantization are plagued by significant discretization errors and low matching efficiency. To overcome the limitations of existing supervised learning approaches that depend on pseudo-labels and lack physical interpretability, this study proposes a self-supervised parameter quantization method that integrates imaging physical models and manifold structure modeling. This method establishes reliable unlabeled constraints through Bloch equation-driven self-supervised physical consistency learning. By incorporating manifold structure-driven knowledge distillation, it transfers features of long frames to short frame models, realizing joint optimization of physical constraints and structural priors, thereby improving both accuracy and efficiency under unlabeled conditions. Experiments have verified this method’s superior accuracy and robustness, providing a novel approach for efficient and reliable MRF parameter estimation.
| [1] | MA D, GULANI V, SEIBERLICH N, et al. Magnetic resonance fingerprinting[J]. Nature, 2013, 495(7440): 187-192. |
| [2] | HUANG M, LI S Y, CHEN J B, et al. Progress of magnetic resonance fingerprinting technology and its clinical application[J]. Chinese J Magn Reson, 2023, 40(2): 207-219. |
| 黄敏, 李思怡, 陈军波, 等. 磁共振指纹成像技术及临床应用的进展[J]. 波谱学杂志, 2023, 40(2): 207-219. | |
| [3] | CAO X, LIAO C, ZHOU Z, et al. DTI-MR fingerprinting for rapid high-resolution whole-brain T1, T2, proton density, ADC, and fractional anisotropy mapping[J]. Magn Reson Med, 2024, 91(3): 987-1001. |
| [4] | LENG Y J, WU Q W, LI Y X, et al. Quantitative detection of brain tissue parameters based on magnetic resonance fingerprinting and its preliminary application in brain diseases[J]. Chinese Journal of Clinical Neurosciences, 2020, 28(5): 579-583. |
| 冷一峻, 吴秋雯, 李郁欣, 等. 基于磁共振指纹技术的脑组织参数定量检测及在脑疾病中的初步应用[J]. 中国临床神经科学, 2020, 28(5): 579-583. | |
| [5] | KRETZLER M E, HUANG S S, SUN J E P, et al. Free-breathing qRF-MRF with pilot tone respiratory motion navigator for T1, T2, T2*, and off-resonance mapping of the human body at 3 T[J]. Magn Reson Mater Phys, 2025, 38(1): 85-95. |
| [6] | RATA M, ORTON M R, TUNARIU N, et al. Repeatability of quantitative MR fingerprinting for T1 and T2 measurements of metastatic bone in prostate cancer patients[J]. Eur Radiol, 2025, 35: 2487-2498. |
| [7] | MCGIVNEY D F, PIERRE E, MA D, et al. SVD compression for magnetic resonance fingerprinting in the time domain[J]. IEEE Trans Med Imaging, 2014, 33(12): 2311-2322. |
| [8] | ASSLANDER J, CLOOS M A, KNOLL F, et al. Low rank alternating direction method of multipliers reconstruction for MR fingerprinting[J]. Magn Reson Med, 2018, 79(1): 83-96. |
| [9] | CAULEY S F, SETSOMPOP K, MA D, et al. Fast group matching for MR fingerprinting reconstruction[J]. Magn Reson Med, 2015, 74(2): 523-528. |
| [10] | YANG M, MA D, JIANG Y, et al. Low rank approximation methods for MR fingerprinting with large scale dictionaries[J]. Magn Reson Med, 2018, 79(4): 2392-2400. |
| [11] | WANG Z, ZHANG J, CUI D, et al. Magnetic resonance fingerprinting using a fast dictionary searching algorithm: MRF-ZOOM[J]. IEEE Trans Biomed Eng, 2018, 66(6): 1526-1535. |
| [12] | NALLAPAREDDY N, RAY S. Inferring multiple tissue properties from magnetic resonance fingerprinting images[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Palo Atto, CA: AAAI Press, 2022, 36(11): 12587-12593. |
| [13] | VIRTUE P, STELLA X Y, LUSTIG M. Better than real: Complex-valued neural nets for MRI fingerprinting[C]// IEEE International Conference on Image Processing (ICIP). Beijing, China: IEEE, 2017: 3953-3957. |
| [14] | OKSUZ I, CRUZ G, CLOUGH J, et al. Magnetic resonance fingerprinting using recurrent neural networks[C]// IEEE International Symposium on Biomedical Imaging (ISBI). Venice, Italy: IEEE, 2019: 1537-1540. |
| [15] | BALSIGER F, JUNGO A, SCHEIDEGGER O, et al. Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting[J]. Med Image Anal, 2020, 64: 101741. |
| [16] | 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[J]. IEEE Trans Med Imaging, 2019, 38(10): 2364-2374. |
| [17] | FANG Z, CHEN Y, NIE D, et al. Rca-u-net: Residual channel attention u-net for fast tissue quantification in magnetic resonance fingerprinting[C]// Medical Image Computing and Computer Assisted Intervention-MICCAI International Conference. Shenzhen, China. Cham: Springer, 2019: 101-109. |
| [18] | LI P, HU Y. Deep magnetic resonance fingerprinting based on local and global vision transformer[J]. Med Image Anal, 2024, 95: 103198. |
| [19] | FATANIA K, CHAU K Y, PIRKL C M, et al. Nonlinear equivariant imaging: Learning multi-parametric tissue mapping without ground truth for compressive quantitative mri[C]// International Symposium on Biomedical Imaging (ISBI). Cartagena, Colombia: IEEE, 2023: 1-4. |
| [20] | HAMILTON J I. A self-supervised deep learning reconstruction for shortening the breathhold and acquisition window in cardiac magnetic resonance fingerprinting[J]. Front Cardiovasc Med, 2022, 9: 928546. |
| [21] | MAYO P, CENCINI M, FATANIA K, et al. Deep image priors for magnetic resonance fingerprinting with pretrained Bloch-consistent denoising autoencoders[C]// IEEE International Symposium on Biomedical Imaging (ISBI). Athens, Greece: IEEE, 2024: 1-4. |
| [22] | ULYANOV D, VEDALDI A, LEMPITSKY V. Deep image prior[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 9446-9454. |
| [23] | YUAN W, GU X, DAI Z, et al. Neural window fully-connected crfs for monocular depth estimation[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, LA, USA: IEEE, 2022: 3916-3925. |
| [24] | RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]// Medical Image Computing and Computer-Assisted Intervention-MICCAI International Conference. Munich, Germany. Cham: Springer, 2015: 234-241. |
| [25] | HU Y, LI P, CHEN H, et al. High-quality MR fingerprinting reconstruction using structured low-rank matrix completion and subspace projection[J]. IEEE Trans Med Imaging, 2021, 41(5): 1150-1164. |
| [26] | COLLINS D L, ZIJDENBOS A P, KOLLOKIAN V, et al. Design and construction of a realistic digital brain phantom[J]. IEEE Trans Med Imaging, 2002, 17(3): 463-468. |
| [27] | AUBERT-BROCHE B, EVANS A C, COLLINS L. A new improved version of the realistic digital brain phantom[J]. NeuroImage, 2006, 32(1): 138-145. |
| [28] | WEIGEL M. Extended phase graphs: dephasing, RF pulses, and echoes-pure and simple[J]. Magn Reson Imaging, 2015, 41(2): 266-295. |
| [29] | SOYAK R, NAVRUZ E, ERSOY E O, et al. Channel attention networks for robust MR fingerprint matching[J]. IEEE Trans Biomed Eng, 2021, 69(4): 1398-1405. |
| [30] | CHEN D, TACHELLA J, DAVIES M E. Equivariant imaging: Learning beyond the range space[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal, QC, Canada: IEEE, 2021: 4379-4388. |
| [31] | BIPIN MEHTA B, COPPO S, FRANCES MCGIVNEY D, et al. Magnetic resonance fingerprinting: a technical review[J]. Magn Reson Med, 2019, 81(1): 25-46. |
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