标题： 通过切片交错扩散编码（SIDE）实现扩散MRI的多倍加速 显示英文标题

标题： Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

摘要： 扩散磁共振成像（dMRI）是一种独特的成像技术，用于活体组织微结构和白质通路的特征描述。然而，其相对较长的采集时间意味着在成像婴儿或帕金森病患者等对象时会产生更大的运动伪影。 为了加速dMRI采集，本文提出（i）一种称为切片交错扩散编码（SIDE）的扩散编码方案，该方案通过将每个扩散加权（DW）图像体积与使用不同扩散梯度编码的切片交错，本质上允许减少与每个扩散梯度相关的图像体积的切片欠采样，从而显著减少采集时间；以及（ii）一种基于深度学习的方法，用于从高度切片欠采样的数据中有效重建DW图像。 基于人类连接组项目（HCP）数据集的评估表明，我们的方法可以实现高达6倍的高加速因子，并且信息损失最小。 使用SIDE采集获得的dMRI数据进行的评估表明，结合多带成像时，采集速度最多可以提高50倍。 显示英文摘要

摘要： Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways. However, its relatively long acquisition time implies greater motion artifacts when imaging, for example, infants and Parkinson's disease patients. To accelerate dMRI acquisition, we propose in this paper (i) a diffusion encoding scheme, called Slice-Interleaved Diffusion Encoding (SIDE), that interleaves each diffusion-weighted (DW) image volume with slices that are encoded with different diffusion gradients, essentially allowing the slice-undersampling of image volume associated with each diffusion gradient to significantly reduce acquisition time, and (ii) a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data. Evaluation based on the Human Connectome Project (HCP) dataset indicates that our method can achieve a high acceleration factor of up to 6 with minimal information loss. Evaluation using dMRI data acquired with SIDE acquisition demonstrates that it is possible to accelerate the acquisition by as much as 50 folds when combined with multi-band imaging.