基于物理模型的ISTAVS-Net多线圈MRI图像重建

doi:10.11938/cjmr20243109

摘要/Abstract

摘要：

提高磁共振成像速度是磁共振领域待解决的问题，采用多线圈扫描是一种常用加速方式．但在加速因子大于4时，传统压缩感知磁共振（CS-MRI）重建算法得到的图像质量不高．为此，本文提出一种基于物理模型的ISTAVS-Net多线圈MRI图像重建方法．将ISTA算法与VS-Net网络拆分思想相结合，提出ISTAVS算法，并展开成ISTAVS-Net网络．将每步迭代与网络模块结合，比黑盒U-Net网络的可解释性更强．网络中引入残差机制，增加了网络的非线性表达能力和稳定性. 稀疏变换、收缩阈值以及正则化参数在训练中自动学习，提高了重建的灵活性．采用Globus膝关节数据集的测试结果表明不同加速因子下ISTAVS-Net网络效果均优于传统的 L1-ESPIRiT和ISTA迭代算法，图像质量和性能指标比U-Net、ISTA-Net+和VS-Net网络提升明显，在高加速因子下对组织细节恢复能力更强．该网络鲁棒性强，更适合对临床扫描数据进行快速高质量重建，可拓宽MRI应用范围．

关键词: 磁共振成像, 物理模型, 图像重建, 多线圈欠采样, 变量拆分网络, 迭代收缩阈值算法

Abstract:

How to improve the speed of MRI is a standing problem in the field of magnetic resonance. A commonly used approach for acceleration is multi-coil scan. However, when the acceleration factor exceeds 4, the image quality obtained by traditional compressed sensing magnetic resonance imaging (CS-MRI) reconstruction algorithms becomes unsatisfactory. In this study, we propose a multi-coil MRI image reconstruction method named as ISTAVS algorithm based on physical model. It combines the ISTA algorithm with the splitting idea of VS-Net, and is expanded into an ISTAVS-Net. Each iteration step is combined with the network module, which has higher interpretability than black box U-Net. The residual mechanism is introduced into the ISTAVS-Net to increase the non-linear expression ability and accuracy. Sparse transformation, shrinkage threshold and regularization parameter are automatically learned during training, which increases the flexibility of reconstruction. The test results of the Globus knee dataset show that the ISTAVS-Net outperforms traditional L1-ESPIRiT and ISTA algorithm by multiple indicators, including the improvement of image quality and performance metrics over U-Net, ISTA-Net+ and VS-Net at different acceleration factors, and the ability to recover tissue details at high acceleration factors. The proposed network demonstrates good robustness and is more suitable for fast and high-quality reconstruction of data acquired on clinical MR scanners, thereby can expand the application range of MRI.

Key words: magnetic resonance imaging, physical model, image reconstruction, multi-coil under-sampling, VS-Net, ISTA algorithm

中图分类号:

黄敏, 朱俊琳, 考宇辰, 周到, 唐奇伶. 基于物理模型的ISTAVS-Net多线圈MRI图像重建[J]. 波谱学杂志, 2024, 41(4): 418-429.

HUANG Min, ZHU Junlin, KAO Yuchen, ZHOU Dao, TANG Qiling. Multi-Coil MRI Image Reconstruction Based on ISTAVS-Net of Physical Model[J]. Chinese Journal of Magnetic Resonance, 2024, 41(4): 418-429.

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评价指标	AF	L1-ESPIRiT	ISTA	U-Net	ISTA-Net+	VS-Net	ISTAVS-Net
PSNR/dB	2	32.671	34.051	40.926	40.879	40.675	42.021
	4	28.868	30.390	36.911	36.281	36.478	37.724
	6	27.234	29.567	34.052	33.808	33.932	34.609
	8	26.976	28.446	33.287	32.713	33.184	33.341
SSIM	2	0.8802	0.9301	0.9573	0.9470	0.9547	0.9604
	4	0.8275	0.8680	0.9257	0.9128	0.9208	0.9311
	6	0.7608	0.8424	0.8950	0.8807	0.8936	0.9002
	8	0.7020	0.8205	0.8825	0.8679	0.8833	0.8839
NMSE	2	0.0205	0.0160	0.0107	0.0113	0.0109	0.0105
	4	0.0384	0.0362	0.0189	0.0193	0.0190	0.0183
	6	0.0478	0.0438	0.0277	0.0280	0.0275	0.0271
	8	0.0578	0.0567	0.0347	0.0350	0.0316	0.0304

	轴位			冠状位
	L1-ESPIRiT	ISTA	ISTAVS-Net	L1-ESPIRiT	ISTA	ISATVS-Net
PSNR/dB	31.743	34.051	38.952		27.234	29.567	34.609
SSIM	0.8180	0.9089	0.9382	0.7608	0.8424	0.9002
NMSE	0.0467	0.0395	0.0227	0.0478	0.0438	0.0271
Times/s	196.12	71.66	2.11	153.95	80.13	1.04