Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China.
Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361004, People's Republic of China.
Phys Med Biol. 2023 Aug 18;68(17). doi: 10.1088/1361-6560/aced77.
. The acquisition of diffusion-weighted images for intravoxel incoherent motion (IVIM) imaging is time consuming. This work aims to accelerate the scan through a highly under-sampling diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) scheme and to develop a reconstruction method for accurate IVIM parameter mapping from the under-sampled data.The proposed under-sampling DW-TSE-PROPELLER scheme for IVIM imaging is that a few blades per-value are acquired and rotated along the-value dimension to cover high-frequency information. A physics-informed residual feedback unrolled network (PIRFU-Net) is proposed to directly estimate distortion-free and artifact-free IVIM parametric maps (i.e., the perfusion-free diffusion coefficientand the perfusion fraction) from highly under-sampled DW-TSE-PROPELLER data. PIRFU-Net used an unrolled convolution network to explore data redundancy in the k-q space to remove under-sampling artifacts. An empirical IVIM physical constraint was incorporated into the network to ensure that the signal evolution curves along the-value follow a bi-exponential decay. The residual between the realistic and estimated measurements was fed into the network to refine the parametric maps. Meanwhile, the use of synthetic training data eliminated the need for genuine DW-TSE-PROPELLER data.The experimental results show that the DW-TSE-PROPELLER acquisition was six times faster than full k-space coverage PROPELLER acquisition and within a clinically acceptable time. Compared with the state-of-the-art methods, the distortion-freeandmaps estimated by PIRFU-Net were more accurate and had better-preserved tissue boundaries on a simulated human brain and realistic phantom/rat brain/human brain data.Our proposed method greatly accelerates IVIM imaging. It is capable of directly and simultaneously reconstructing distortion-free, artifact-free, and accurateandmaps from six-fold under-sampled DW-TSE-PROPELLER data.
. 采集体素内不相干运动（IVIM）成像的扩散加权图像需要花费大量时间。本研究旨在通过高度欠采样扩散加权涡轮自旋回波螺旋桨（DW-TSE-PROPELLER）方案加速扫描，并开发一种从欠采样数据中准确映射 IVIM 参数的重建方法。用于 IVIM 成像的提出的欠采样 DW-TSE-PROPELLER 方案是，每个值采集几个叶片，并沿值维度旋转以覆盖高频信息。提出了一种基于物理信息的残差反馈展开网络（PIRFU-Net），可直接从高度欠采样 DW-TSE-PROPELLER 数据中估计无失真和无伪影的 IVIM 参数图（即无灌注扩散系数和灌注分数）。PIRFU-Net 使用展开卷积网络在 k-q 空间中探索数据冗余，以去除欠采样伪影。网络中纳入了经验性 IVIM 物理约束，以确保沿值的信号演化曲线遵循双指数衰减。将真实和估计测量之间的残差反馈到网络中，以细化参数图。同时，使用合成训练数据消除了对真实 DW-TSE-PROPELLER 数据的需求。实验结果表明，DW-TSE-PROPELLER 采集速度比全 k 空间覆盖 PROPELLER 采集快六倍，且在可接受的临床时间范围内。与最先进的方法相比，PIRFU-Net 估计的无失真和图更准确，在模拟人脑和真实体模/大鼠脑/人脑数据上具有更好的组织边界保留。我们提出的方法大大加速了 IVIM 成像。它能够直接且同时从六倍欠采样 DW-TSE-PROPELLER 数据中重建无失真、无伪影和准确的和图。
