In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.

中文翻译：

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改变欠采样方向以加速多次采集磁共振成像

在这项研究中，我们提出了一种新的采样策略来有效地加速多次采集 MRI。新的采样策略是在多个采集中沿不同的相位编码方向获取数据。通过使用具有中心和随机采样模式的卷积神经网络，在多对比 MR 成像（T1、T2、质子密度）和多相位循环 (PC) 平衡稳态自由进动 (bSSFP) 成像中评估了所提出的采样策略。体内 MRI 采集以及公共数据库用于测试该概念。基于视觉检查和定量分析，无论采样模式如何（中央，随机）或数据集（公开的、回顾性的和前瞻性的体内数据）。对于预期的体内应用，通过在采集时沿不同的相位编码方向采样来执行加速，采用传统的矩形视场，这证明了所提出的采样策略在真实环境中的优势。压缩感知 (CS) 的初步试验也证明了所提出的想法对 CS 的改进。跨多个采集沿不同相位编码方向进行采样有利于加速多采集 MRI，无论采样模式或数据集如何，并通过迁移学习进一步改进。加速度是通过在采集时沿不同相位编码方向采样来执行的，具有传统的矩形视场，这证明了所提出的采样策略在真实环境中的优势。压缩感知 (CS) 的初步试验也证明了所提出的想法对 CS 的改进。跨多个采集沿不同相位编码方向进行采样有利于加速多采集 MRI，无论采样模式或数据集如何，并通过迁移学习进一步改进。加速度是通过在采集时沿不同相位编码方向采样来执行的，具有传统的矩形视场，这证明了所提出的采样策略在真实环境中的优势。压缩感知 (CS) 的初步试验也证明了所提出的想法对 CS 的改进。跨多个采集沿不同相位编码方向进行采样有利于加速多采集 MRI，无论采样模式或数据集如何，并通过迁移学习进一步改进。压缩感知 (CS) 的初步试验也证明了所提出的想法对 CS 的改进。跨多个采集沿不同相位编码方向进行采样有利于加速多采集 MRI，无论采样模式或数据集如何，并通过迁移学习进一步改进。压缩感知 (CS) 的初步试验也证明了所提出的想法对 CS 的改进。跨多个采集沿不同相位编码方向进行采样有利于加速多采集 MRI，无论采样模式或数据集如何，并通过迁移学习进一步改进。