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

Neuroimage

Centre for Neuroscience and MRI Research, Department of Medicine, University of Hawaii, Honolulu, USA.

Published: August 2017

AI Article Synopsis

Article 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 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.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2017.06.004DOI Listing

Publication Analysis

Top Keywords

improving temporal
8
l+s reconstruction
8
faster acquisitions
8
physiological noise
8
dynamic mri
8
mri data
8
temporally correlated
8
transform domain
8
brain background
8
l+s method
8

Similar Publications

Minimally invasive parafascicular surgery (MIPS) with the use of tubular retractors achieve a safe resection in deep seated tumours. Diffusion changes noted on postoperative imaging; the significance and clinical correlation of this remains poorly understood. Single centre retrospective cohort study of neuro-oncology patients undergoing MIPS.

View Article and Find Full Text PDF

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

Neuroradiology

January 2025

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.

Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.

View Article and Find Full Text PDF

Identifying Key Weather Factors Influencing Human Salmonellosis: A Conditional Incidence Analysis in England, Wales, and the Netherlands.

J Infect

January 2025

School of Veterinary Medicine, University of Surrey, Daphne Jackson Rd, Guildford GU2 7AL, United Kingdom; The Surrey Institute for People-Centred Artificial Intelligence, Stag Hill University Campus, Guildford GU2 7XH, United Kingdom; Institute for Sustainability, University of Surrey, Guildford, United Kingdom; University of Exeter, Exeter, United Kingdom.

Objectives: This study aimed to improve the understanding of seasonal incidence pattern observed in salmonellosis by identifying the most influential weather factors, characterizing the nature of this association, and assessing whether it is geographically restricted or generalizable to other locations.

Methods: A novel statistical model was employed to estimate the incidence of salmonellosis conditional to various combinations of three simultaneous weather factors from 14 available. The analysis utilised daily salmonellosis cases reported from 2000 to 2016 along with detailed spatial and temporal weather data from England and Wales, and the Netherlands.

View Article and Find Full Text PDF

Mechanisms and effects of the upgrading of consumption structure on household carbon emissions -evidence from the yangtze river economic belt.

J Environ Manage

January 2025

Department of Big Data Science and Applied Statistics, School of Economics and Management, China University of Geosciences, 430078, China. Electronic address:

As the economy enters the "new normal" and the upgrading of consumption structure enters a critical period of acceleration, under the dual background of accelerating the upgrading of consumption structure and realizing carbon peaking and carbon neutrality goals, it is of great significance to explore the impacts of the upgrading of consumption structure on the carbon emissions of households and the internal mechanism, so as to clarify how to promote the carbon emission reduction of households from the consumption side. This study calculates the household carbon emissions of 10 provinces and 1 municipality in the Yangtze River Economic Belt from 2012 to 2021 from direct and indirect perspectives. Using panel data, a fixed effect and mediating effect model is constructed to explore the mechanism of consumption structure upgrading affecting household carbon emissions.

View Article and Find Full Text PDF

Rehabilitation is the process of helping people regain or improve lost or impaired function due to injury, illness, or disease. To assist in tracking the progress of patients undergoing rehabilitation, this paper proposes a lightweight graph-based deep-learning model for the automatic assessment of physical rehabilitation exercises. The model takes as input the 3D skeleton sequence of a patient performing a movement and outputs a continuous quality score, as a means for patient supervision that could complement or even substitute the need for ordinary clinical exams.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!