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Fast recovery of compressed multi-contrast magnetic resonance images

Styner, Martin A., Angelini, Elsa D., Güngör, Alper, Kopanoglu, Emre, Çukur, Tolga and Güven, H. Emre 2017. Fast recovery of compressed multi-contrast magnetic resonance images. Presented at: Medical Imaging 2017: Image Processing, Orlando, FL, USA, 11 February 2017. Proc. SPIE 10133, Medical Imaging 2017: Image Processing. Society of Photo-optical Instrumentation Engineers, 101331R. 10.1117/12.2252101

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Abstract

In many settings, multiple Magnetic Resonance Imaging (MRI) scans are performed with different contrast characteristics at a single patient visit. Unfortunately, MRI data-acquisition is inherently slow creating a persistent need to accelerate scans. Multi-contrast reconstruction deals with the joint reconstruction of different contrasts simultaneously. Previous approaches suggest solving a regularized optimization problem using group sparsity and/or color total variation, using composite-splitting denoising and FISTA. Yet, there is significant room for improvement in existing methods regarding computation time, ease of parameter selection, and robustness in reconstructed image quality. Selection of sparsifying transformations is critical in applications of compressed sensing. Here we propose using non-convex p-norm group sparsity (with p < 1), and apply color total variation (CTV). Our method is readily applicable to magnitude images rather than each of the real and imaginary parts separately. We use the constrained form of the problem, which allows an easier choice of data-fidelity error-bound (based on noise power determined from a noise-only scan without any RF excitation). We solve the problem using an adaptation of Alternating Direction Method of Multipliers (ADMM), which provides faster convergence in terms of CPU-time. We demonstrated the effectiveness of the method on two MR image sets (numerical brain phantom images and SRI24 atlas data) in terms of CPU-time and image quality. We show that a non-convex group sparsity function that uses the p-norm instead of the convex counterpart accelerates convergence and improves the peak-Signal-to-Noise-Ratio (pSNR), especially for highly undersampled data. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Cardiff University Brain Research Imaging Centre (CUBRIC)
Psychology
Publisher: Society of Photo-optical Instrumentation Engineers
ISSN: 0277-786X
Last Modified: 09 Aug 2019 14:00
URI: http://orca.cf.ac.uk/id/eprint/101051

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