Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Background Parallel magnetic resonance imaging (MRI) is a fast imaging technique that helps acquiring highly resolved images in space/time. Its performance depends on the reconstruction algorithm, which can proceed either in the k -space or in the image domain. Objective and methods To improve the p...

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Bibliographic Details
Published in:Magma (New York, N.Y.) Vol. 27; no. 6; pp. 509 - 529
Main Authors: Chaari, Lotfi, Ciuciu, Philippe, Mériaux, Sébastien, Pesquet, Jean-Christophe
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2014
Springer Verlag
Subjects:
Acceleration
Algorithms
Bioengineering
Biomedical Engineering and Bioengineering
Brain - anatomy & histology
Brain - physiopathology
Brain Mapping - methods
Cognitive science
Cognitive Sciences
Computer Appl. in Life Sciences
Computer Science
Data Compression - methods
Engineering Sciences
Evoked Potentials - physiology
Health Informatics
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging
Imaging, Three-Dimensional - methods
Life Sciences
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematics
Medical Imaging
Medicine
Medicine & Public Health
Neurons and Cognition
Neuroscience
Optimization and Control
Radiology
Reconstruction
Regularization
Reproducibility of Results
Research Article
Segmentation
Sensitivity and Specificity
Signal and Image Processing
Signal Processing, Computer-Assisted
Solid State Physics
Spatio-Temporal Analysis
Statistics
Three dimensional
Wavelet
Wavelet Analysis
fMRI
Spatio-temporal regularization
Parallel MRI
Wavelet transform
Convex optimization
convex optimization
wavelet transform
ISSN:0968-5243, 1352-8661, 1352-8661
Online Access:Get full text
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Summary:Background Parallel magnetic resonance imaging (MRI) is a fast imaging technique that helps acquiring highly resolved images in space/time. Its performance depends on the reconstruction algorithm, which can proceed either in the k -space or in the image domain. Objective and methods To improve the performance of the widely used SENSE algorithm, 2D regularization in the wavelet domain has been investigated. In this paper, we first extend this approach to 3D-wavelet representations and the 3D sparsity-promoting regularization term, in order to address reconstruction artifacts that propagate across adjacent slices. The resulting optimality criterion is convex but nonsmooth, and we resort to the parallel proximal algorithm to minimize it. Second, to account for temporal correlation between successive scans in functional MRI (fMRI), we extend our first contribution to 3D +  t acquisition schemes by incorporating a prior along the time axis into the objective function. Results Our first method (3D-UWR-SENSE) is validated on T1-MRI anatomical data for gray/white matter segmentation. The second method (4D-UWR-SENSE) is validated for detecting evoked activity during a fast event-related functional MRI protocol. Conclusion We show that our algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (motor or computation tasks) and two parallel acceleration factors ( R = 2 and R = 4 ) on 2 × 2 × 3 mm 3 echo planar imaging (EPI) images.
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ISSN:0968-5243
1352-8661
1352-8661
DOI:10.1007/s10334-014-0436-5