Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning

doi:10.1007/s12204-023-2677-9

Abstract

Abstract: Sensitivity encoding (SENSE) is a parallel magnetic resonance imaging (MRI) reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction. The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms, resulting in a limited reconstruction accuracy. Therefore, we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning (SOUPDIL) with the SENSE model, called SOUPDIL-SENSE. The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques. The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity, eliminates the noise and artifacts of the reconstructed images, and improves the reconstruction accuracy.

Key words: parallel magnetic resonance imaging (MRI), sensitivity encoding (SENSE), efficient sum of outer products dictionary learning (SOUPDIL), alternating direction method of multipliers

摘要： 灵敏度编码(Sensitivity Encoding, SENSE)是一种利用接收器线圈灵敏度的固有空间编码信息来实现图像重建的并行磁共振成像(Magnetic Resonance Imaging, MRI)技术。现存基于SENSE模型的MRI重建算法的自适应能力不足,导致重建图像精度欠缺。基于上述情况，将包含L0范数的外积有效和字典学习(Efficient Sum of Outer Products Dictionary Learning, SOUPDIL)引入SENSE模型中，提出一种基于SOUPDIL的改进灵敏度编码重建算法，即SOUPDIL-SENSE。提出的算法主要基于交替方向乘子法求解，通过字典学习和图像更新两步实现并行MRI重建。仿真实验结果表明：该算法能够促进图像稀疏性，有效消除图像重建噪声和伪影，显著提升图像重建精度。

关键词: 并行磁共振成像，灵敏度编码，外积有效和字典学习，交替方向乘子法

CLC Number:

TP391
R445.2

Duan Jizhong, Su Yan. Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 555-565.

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