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Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms...
In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using...
In this paper, a Multi-Channel/Multi-Contrast image reconstruction algorithm is proposed. The method, which is based on the Augmented Lagrangian Method uses joint convex objective functions to utilize the mutual information in the data from multiple channels to improve reconstruction quality. For this purpose, color total variation and group sparsity are used. To evaluate the performance of the method,...
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