基于虚拟线圈和GRAPPA增强网络的PMRI方法
收稿日期: 2025-02-24
网络出版日期: 2025-04-24
基金资助
合肥综合性国家科学中心能源研究院（安徽省能源实验室）项目(24JYCD01);安徽高校协同创新项目(GXXT-2021-030);安徽高校协同创新项目(GXXT-2022-011)
PMRI Image Reconstruction Method Based on Virtual Coils and GRAPPA-enhanced Network
Received date: 2025-02-24
Online published: 2025-04-24
并行磁共振成像（PMRI）是一种通过多个接收线圈进行欠采样的成像技术，它利用空间信息补充梯度相位编码不足，通过特定算法重建无混叠图像，从而加速成像过程. 针对基于特定扫描的PMRI算法在有限数量的自动校准信号（ACS）下使用较高的加速因子会出现过拟合或泛化能力差的问题，提出一种基于虚拟线圈和GRAPPA增强网络的重建方法. 该方法通过使用虚拟共轭线圈扩充样本，并利用GRAPPA算法获得增强的ACS进行非线性的深度学习网络训练. 实验结果表明，提出的PMRI方法在较少ACS数量与较高加速因子的情况下，能够有效减少由于参考数据不足引起的混叠伪影，从而显著提高图像重建质量.
高照耀 , 张展 , 胡亮亮 , 许光宇 , 周胜 , 胡雨欣 , 林子捷 , 周超 . 基于虚拟线圈和GRAPPA增强网络的PMRI方法[J]. 波谱学杂志, 2025 , 42(4) : 390 -401 . DOI: 10.11938/cjmr20253147
Parallel magnetic resonance imaging (PMRI) is an imaging technique that uses multiple receiver coils for undersampling. It utilizes spatial information to supplement the insufficient gradient phase encoding and reconstructs aliasing-free images with specific algorithms to accelerate the imaging process. To address the issue of overfitting or poor generalization when using high acceleration factors with a limited number of auto calibration signals (ACS) in PMRI algorithms based on specific scans, a reconstruction method based on virtual coils and GRAPPA-enhanced networks is proposed. This method expands the sample by using virtual conjugate coils and enhances the ACS using the GRAPPA algorithm for training a nonlinear deep learning network. Experimental results show that the proposed PMRI method can effectively reduce aliasing artifacts caused by insufficient reference data, significantly improving image reconstruction quality with fewer ACS and higher acceleration factors.
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