Literature DB >> 28684333

Improving temporal resolution in fMRI using a 3D spiral acquisition and low rank plus sparse (L+S) reconstruction.

Andrii Y Petrov1, Michael Herbst2, V Andrew Stenger3.   

Abstract

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 kz-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.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain functional imaging; Low rank plus sparse modeling; Spiral sampling

Mesh:

Year:  2017        PMID: 28684333     DOI: 10.1016/j.neuroimage.2017.06.004

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  4 in total

1.  High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.

Authors:  Shouchang Guo; Jeffrey A Fessler; Douglas C Noll
Journal:  IEEE Trans Med Imaging       Date:  2020-11-30       Impact factor: 10.048

2.  Compressed sensing based dynamic MR image reconstruction by using 3D-total generalized variation and tensor decomposition: k-t TGV-TD.

Authors:  Jucheng Zhang; Lulu Han; Jianzhong Sun; Zhikang Wang; Wenlong Xu; Yonghua Chu; Ling Xia; Mingfeng Jiang
Journal:  BMC Med Imaging       Date:  2022-05-27       Impact factor: 2.795

3.  Highly accelerated submillimeter resolution 3D GRASE with controlled T 2 blurring in T 2 -weighted functional MRI at 7 Tesla: A feasibility study.

Authors:  Suhyung Park; Salvatore Torrisi; Jennifer D Townsend; Alexander Beckett; David A Feinberg
Journal:  Magn Reson Med       Date:  2020-11-24       Impact factor: 4.668

4.  Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints.

Authors:  Mark Chiew; Nadine N Graedel; Karla L Miller
Journal:  Neuroimage       Date:  2018-03-20       Impact factor: 6.556

  4 in total

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