Centre for Neuroscience and MRI Research, Department of Medicine, University of Hawaii, Honolulu, USA.
Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany.
Neuroimage. 2017 Aug 15;157:660-674. doi: 10.1016/j.neuroimage.2017.06.004. Epub 2017 Jul 4.
Rapid whole-brain dynamic Magnetic Resonance Imaging (MRI) is of particular interest in Blood Oxygen Level Dependent (BOLD) functional MRI (fMRI). Faster acquisitions with higher temporal sampling of the BOLD time-course provide several advantages including increased sensitivity in detecting functional activation, the possibility of filtering out physiological noise for improving temporal SNR, and freezing out head motion. Generally, faster acquisitions require undersampling of the data which results in aliasing artifacts in the object domain. A recently developed low-rank (L) plus sparse (S) matrix decomposition model (L+S) is one of the methods that has been introduced to reconstruct images from undersampled dynamic MRI data. The L+S approach assumes that the dynamic MRI data, represented as a space-time matrix M, is a linear superposition of L and S components, where L represents highly spatially and temporally correlated elements, such as the image background, while S captures dynamic information that is sparse in an appropriate transform domain. This suggests that L+S might be suited for undersampled task or slow event-related fMRI acquisitions because the periodic nature of the BOLD signal is sparse in the temporal Fourier transform domain and slowly varying low-rank brain background signals, such as physiological noise and drift, will be predominantly low-rank. In this work, as a proof of concept, we exploit the L+S method for accelerating block-design fMRI using a 3D stack of spirals (SoS) acquisition where undersampling is performed in the k-t domain. We examined the feasibility of the L+S method to accurately separate temporally correlated brain background information in the L component while capturing periodic BOLD signals in the S component. We present results acquired in control human volunteers at 3T for both retrospective and prospectively acquired fMRI data for a visual activation block-design task. We show that a SoS fMRI acquisition with an acceleration of four and L+S reconstruction can achieve a brain coverage of 40 slices at 2mm isotropic resolution and 64 x 64 matrix size every 500ms.
快速全脑动态磁共振成像(MRI)在血氧水平依赖(BOLD)功能磁共振成像(fMRI)中特别有趣。更快的采集和更高的 BOLD 时程时间采样提供了几个优势,包括提高检测功能激活的灵敏度、过滤生理噪声以提高时间 SNR 的可能性,以及冻结头部运动。一般来说,更快的采集需要对数据进行欠采样,这会导致物体域中的混叠伪影。最近开发的低秩(L)加稀疏(S)矩阵分解模型(L+S)是从欠采样动态 MRI 数据重建图像的方法之一。L+S 方法假设动态 MRI 数据表示为时空矩阵 M,是 L 和 S 分量的线性叠加,其中 L 表示高度空间和时间相关的元素,例如图像背景,而 S 则捕获在适当的变换域中稀疏的动态信息。这表明 L+S 可能适用于欠采样任务或缓慢的事件相关 fMRI 采集,因为 BOLD 信号的周期性在时间傅里叶变换域中是稀疏的,而缓慢变化的低秩大脑背景信号,如生理噪声和漂移,主要是低秩的。在这项工作中,作为概念验证,我们利用 L+S 方法加速使用 3D 螺旋堆栈(SoS)采集的块设计 fMRI,其中在 k-t 域中进行欠采样。我们研究了 L+S 方法在准确分离 L 分量中与时间相关的大脑背景信息的同时,在 S 分量中捕获周期性 BOLD 信号的可行性。我们展示了在 3T 下,使用 SoS fMRI 采集和加速因子为四的 L+S 重建,在 500ms 时可以达到 40 张 2mm 各向同性分辨率和 64 x 64 矩阵大小的脑覆盖范围。