PittsburghV1, Main, Exploration, bibRecord, 003731

Compressed Sensing fMRI using Gradient-recalled Echo and EPI Sequences

Identifieur interne : 003731 ( Main/Exploration ); précédent : 003730; suivant : 003732

Compressed Sensing fMRI using Gradient-recalled Echo and EPI Sequences

Auteurs : Xiaopeng Zong [États-Unis] ; Juyoung Lee [Corée du Sud] ; Alexander Poplawsky [États-Unis] ; Seong-Gi Kim [États-Unis, Corée du Sud] ; Jong Chul Ye [Corée du Sud]

Source :

NeuroImage [ 1053-8119 ] ; 2014.

RBID : PMC:4021580

Descripteurs français

English descriptors

KwdEn :
- Algorithms, Animals, Brain Mapping (methods), Data Compression (methods), Echo-Planar Imaging (methods), Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Magnetic Resonance Imaging (methods), Male, Multimodal Imaging (methods), Rats, Rats, Sprague-Dawley, Reproducibility of Results, Sensation (physiology), Sensitivity and Specificity, Somatosensory Cortex (physiology).
MESH :
- methods : Brain Mapping, Data Compression, Echo-Planar Imaging, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Multimodal Imaging.
- physiology : Sensation, Somatosensory Cortex.
- Algorithms, Animals, Male, Rats, Rats, Sprague-Dawley, Reproducibility of Results, Sensitivity and Specificity.

Abstract

Compressed sensing (CS) may be useful for accelerating data acquisitions in high-resolution fMRI. However, due to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power loss, the CS approach for fMRI (CS-fMRI) has not been extensively investigated. To evaluate the utility of CS in fMRI application, we systematically investigated the properties of CS-fMRI using computer simulations and in vivo experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding direction were studied and k-t FOCUSS was used as the CS reconstruction algorithm, which exploits the temporal redundancy of images. Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS-fMRI with reduction factors of 2 and 4. CS-fMRI with GRE, but not with EPI, improves the statistical sensitivity for activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves the temporal resolution and temporal noise correlations. While CS reduces the functional response amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive. Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021580

DOI: 10.1016/j.neuroimage.2014.01.045
PubMed: 24495813
PubMed Central: 4021580

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><p id="P2">Compressed sensing (CS) may be useful for accelerating data acquisitions in high-resolution fMRI. However, due to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power loss, the CS approach for fMRI (CS-fMRI) has not been extensively investigated. To evaluate the utility of CS in fMRI application, we systematically investigated the properties of CS-fMRI using computer simulations and <italic>in vivo</italic>
 experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding direction were studied and k-t FOCUSS was used as the CS reconstruction algorithm, which exploits the temporal redundancy of images. Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS-fMRI with reduction factors of 2 and 4. CS-fMRI with GRE, but not with EPI, improves the statistical sensitivity for activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves the temporal resolution and temporal noise correlations. While CS reduces the functional response amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive. Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies.</p>
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Compressed Sensing fMRI using Gradient-recalled Echo and EPI Sequences

Compressed Sensing fMRI using Gradient-recalled Echo and EPI Sequences

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki

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