German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Department of Computer Science, University of Bonn, Germany.
NMR Biomed. 2019 Mar;32(3):e4055. doi: 10.1002/nbm.4055. Epub 2019 Jan 14.
Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.
时间限制对磁共振成像的应用有很大的影响，通常会限制高级扩散磁共振成像（dMRI）协议在临床实践和高通量研究中的应用。因此，人们研究了加速 dMRI 的采集策略，以便即使在严格的扫描时间限制下，也能采集到多功能和高质量的成像数据。扩散谱成像（DSI）是一种先进的采集策略，它可以实现更高的体素内微观结构分辨率，可以通过压缩感知（CS）理论得到充分的加速。CS 理论描述了一种从数据集采集比传统要求更少的样本的有效方法，然后进行稳健的重建，从稀疏测量中恢复完整的数据集。为了准确恢复 DSI 数据，需要选择合适的稀疏 q 空间采样方案和 CS 重建的传感和稀疏基。在这项工作中，我们探索了三种不同类型的 q 空间欠采样方案和两种基于傅里叶或 SHORE 基函数的 CS 重建框架。CS 恢复后，通过用于 dMRI 分析的最先进的处理技术，从重建数据中估计扩散和微观结构参数以及各向异性信息。通过模拟、扩散体模和体内 DSI 数据，发现 q 空间样本的各向同性分布最适合稀疏 DSI。CS 重建结果表明，基于傅里叶的 CS-DSI 方法比基于 SHORE 的方法具有更好的性能。基于这些发现，我们概述了一种适用于限时研究的加速 DSI 和稀疏测量稳健 CS 重建的实验设计。
