波谱学杂志2026，Vol.43Issue(1)：46-60,15.DOI:10.11938/cjmr20253173
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
Self-supervised Magnetic Resonance Fingerprint Parameter Quantization Method Based on Imaging Physical Model and Manifold Structure
摘要
Abstract
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.关键词
磁共振指纹成像/自监督学习/参数量化/成像物理模型/流形结构Key words
magnetic resonance fingerprint imaging/self-supervised learning/parameter quantization/imaging physical model/manifold structure分类
医药卫生引用本文复制引用
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):46-60,15.基金项目
国家自然科学基金资助项目(62371167) （62371167）
黑龙江省自然科学基金资助项目(YQ2021F005). （YQ2021F005）
