物理先验引导的多对比度磁共振重建扩散模型

doi:10.11938/cjmr20263215

波谱学杂志 ›› 2026, Vol. 43 ›› Issue (3): 321-338.doi: 10.11938/cjmr20263215cstr: 32225.14.cjmr20263215

物理先验引导的多对比度磁共振重建扩散模型

苏奕霖¹, 刘元元², 崔卓须², 梁栋¹^,²^,³^,⁴^,^*()

¹ 南方医科大学生物医学工程学院，广东广州 510515
² 医学人工智能研究中心，广东深圳 518055
³ 中国科学院深圳先进技术研究院医学成像科学与技术系统全国重点实验室，广东深圳 518055
⁴ 中国科学院深圳先进技术研究院，广东省多模态无创脑机接口理论与技术重点实验室，广东深圳 518055

收稿日期:2026-03-24 出版日期:2026-09-05 在线发表日期:2026-08-11
通讯作者: 梁栋 *Tel: 0755- 86392243, E-mail: dong.liang@siat.ac.cn.
基金资助:
国家自然科学基金(62125111);国家自然科学基金(62476268);国家自然科学基金(62206273);广东省多模态无创脑机接口理论与技术重点实验室(2024B1212010010);深圳市科技计划项目(JCYJ20240813155840052)

Physics Guided Multi-contrast Magnetic Resonance Reconstruction Diffusion Model

SU Yilin¹, LIU Yuanyuan², CUI Zhuoxu², LIANG Dong¹^,²^,³^,⁴^,^*()

¹ School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
² Research Center for Medical AI, Shenzhen 518055, China
³ State Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
⁴ Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received:2026-03-24 Published:2026-09-05 Online:2026-08-11
Contact: LIANG Dong *Tel: 0755- 86392243, E-mail: dong.liang@siat.ac.cn.

摘要/Abstract

摘要：

多对比度磁共振成像技术通过一次扫描同步获取多对比度加权图像，在提升成像效率的同时也为后续参数定量提供了丰富信息．然而，高度欠采样条件下同时重建多个对比度图像并保证其解剖一致性与物理合理性，仍是该领域面临的关键挑战．现有方法或依赖难以获取的全采样标签数据，或面临训练域与目标域分布不一致时性能下降的域偏移问题．为此，本文提出一种物理先验引导的多对比度重建扩散模型，该方法将布洛赫动力学方程所描述的磁共振信号演化规律编码为字典匹配约束，直接耦合至扩散模型的反向采样过程中，在无需重新训练的前提下，实现对数据驱动先验的物理校正．实验表明，本方法较现有监督及自监督学习方法泛化能力更强，估算的参数图进一步验证了其参数定量准确性，有望推动多对比度定量成像在临床中的广泛应用．

关键词: 磁共振成像, 多对比度成像, 扩散模型, 无监督学习, 物理先验

Abstract:

Multi-contrast MRI allows for the simultaneous acquisition of multiple weighted images, which improves imaging efficiency and provides rich quantitative information. However, reconstructing these images under high undersampling while ensuring anatomical consistency and physical plausibility remains a significant challenge. Existing methods often rely on scarce fully-sampled data or suffer from performance degradation due to domain shift. To address this, we propose a physics-prior-guided diffusion model that encodes MR signal evolution via Bloch equations into a dictionary-matching constraint. This constraint is directly coupled into the reverse sampling process, enabling physical correction of data-driven priors without retraining. Experimental results demonstrate superior generalization performance over supervised and self-supervised approaches, while the estimated parameter maps validate its high quantitative accuracy, highlighting its potential for clinical multi-contrast imaging.

Key words: magnetic resonance imaging (MRI), multi-contrast imaging, diffusion models, unsupervised learning, physics priors

中图分类号:

O482.53

苏奕霖, 刘元元, 崔卓须, 梁栋. 物理先验引导的多对比度磁共振重建扩散模型[J]. 波谱学杂志, 2026, 43(3): 321-338.

SU Yilin, LIU Yuanyuan, CUI Zhuoxu, LIANG Dong. Physics Guided Multi-contrast Magnetic Resonance Reconstruction Diffusion Model[J]. Chinese Journal of Magnetic Resonance, 2026, 43(3): 321-338.

图/表 15

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R	Method	PSNR/dB	SSIM	RMSE
6	PGDS	23.651±2.582	0.715±0.043	0.068±0.018
	BCNN	21.579±2.643	0.767±0.042	0.087±0.022
	SSDU	20.736±2.963	0.748±0.046	0.097±0.028
	DLESPIRIT	17.990±2.608	0.617±0.063	0.131±0.033
	D3IP	17.936±2.902	0.606±0.049	0.133±0.039
14	PGDS	21.214±2.714	0.651±0.047	0.091±0.024
	BCNN	19.603±2.651	0.689±0.047	0.109±0.028
	SSDU	19.398±2.945	0.685±0.053	0.113±0.032
	DLESPIRIT	16.924±2.656	0.579±0.060	0.148±0.037
	D3IP	16.963±2.980	0.582±0.053	0.149±0.044

R	Method	PSNR/dB	SSIM	RMSE
4	PGDS	37.656±1.125	0.694±0.012	0.013±0.002
	BCNN	29.582±4.947	0.620±0.096	0.037±0.017
	SSDU	28.863±2.528	0.560±0.079	0.037±0.011
	DLESPIRIT	26.394±2.304	0.449±0.048	0.049±0.014
	D3IP	33.191±2.038	0.614±0.037	0.022±0.006
6	PGDS	34.688±2.094	0.615±0.035	0.019±0.005
	BCNN	27.944±2.824	0.511±0.083	0.041±0.012
	SSDU	27.629±2.878	0.475±0.078	0.043±0.015
	DLESPIRIT	24.076±0.263	0.384±0.020	0.063±0.002
	D3IP	29.613±2.136	0.521±0.036	0.034±0.009
8	PGDS	32.143±2.355	0.556±0.041	0.025±0.007
	BCNN	26.354±2.023	0.446±0.081	0.049±0.011
	SSDU	26.385±2.874	0.426±0.077	0.050±0.016
	DLESPIRIT	22.234±0.785	0.338±0.020	0.078±0.007
	D3IP	27.411±2.279	0.477±0.042	0.044±0.012

R	Method	PSNR/dB	SSIM	RMSE
8	PGDS	37.537±1.174	0.963±0.003	0.013±0.001
	DDS	36.573±1.278	0.926±0.010	0.015±0.002
	FRSGM	33.021±0.733	0.873±0.008	0.022±0.002
12	PGDS	37.007±1.132	0.951±0.003	0.014±0.002
	DDS	35.874±1.163	0.910±0.010	0.016±0.002
	FRSGM	30.351±0.605	0.834±0.016	0.030±0.002

Reference	Method	R=6	R=14
T₁ (1476.2±599.7)	PGDS	1473.2±38.4	1475.7±40.2
	BCNN	1415.0±28.0	1396.4±26.2
	SSDU	1484.9±27.6	1637.0±41.8
	DLESPIRIT	1230.9±28.7	1353.2±29.6
	D3IP	1459.7±29.9	1494.1±22.9
T_1ρ (58.5±22.5)	PGDS	57.4±1.6	58.1±1.7
	BCNN	54.5±12.7	50.4±7.9
	SSDU	58.3±9.2	52.3±12.1
	DLESPIRIT	58.9±10.1	63.7±7.3
	D3IP	66.5±10.1	65.4±13.5

Reference	Method	R=4	R=6	R=8
	PGDS	37.1±2.4	37.7±2.0	40.1±3.4
T₂	BCNN	39.2±3.6	38.9±3.4	40.4±3.2
(36.0±2.7)	SSDU	38.9±2.2	39.3±2.3	32.0±3.9
	DLESPIRIT	49.5±17.3	91.7±6.0	99.8±7.1
	D3IP	39.5±2.0	43.8±1.9	42.1±4.3

物理先验引导的多对比度磁共振重建扩散模型

Physics Guided Multi-contrast Magnetic Resonance Reconstruction Diffusion Model

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参考文献 34

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方法	心脏					头部
方法	PGDS	BCNN	SSDU	DLESPIRIT	D3IP	PGDS	DDS	FRSGM
时间/s	145.5	0.46	7.77	1.18	31.42	47.55	37.94	66.39

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