Multi-contrast reconstruction with Bayesian compressed sensing
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Adalsteinsson_Multi-contrast.pdf
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Author(s)
Bilgic, Berkin
Adalsteinsson, Elfar
Goyal, Vivek K.
Date Issued
June 2011
Journal
Magnetic Resonance in Medicine
Publisher
Wiley Blackwell
Citation
Bilgic, Berkin, Vivek K Goyal, and Elfar Adalsteinsson. “Multi-Contrast Reconstruction with Bayesian Compressed Sensing.” Magnetic Resonance in Medicine 66, no. 6 (December 2011): 1601–1615.
Version
Author's final manuscript
Abstract
Clinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k-space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M-FOCUSS. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein solving each of the inverse problems corresponds to finding the parameters (here, image gradient coefficients) associated with each of the images. The variance of image gradients across contrasts for a single volumetric spatial position is a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the k-space data belonging to each image are used independently to infer the image gradients. Thus, commonality of image spatial structure across contrasts is exploited without the problematic assumption of correlation across contrasts. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root-mean-square error) compared with previous compressed sensing algorithms and show the benefit of joint inversion under a hierarchical Bayesian model.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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DOI of Published Version
https://doi.org/10.1002/mrm.22956
