Compressive sensing methods for multi-contrast magnetic resonance imaging

Güngör, Alper
Compressive sensing (CS) is a signal processing tool that allows reconstruction of sparse signals from highly undersampled data. This study investigates application of CS to magnetic resonance imaging (MRI). In this study, first, an optimization framework for single contrast CS MRI is presented. The method relies on an augmented Lagrangian based method, specifically alternating direction method of multipliers (ADMM). The ADMM framework is used to solve a constrained optimization problem with an objective function consisting of a linear combination of the total variation on the magnitude image and the l1-norm. Then, a fast implementation is derived for MRI, which requires only two FFT operations per iteration. Second, for better exploitation of sparsity, a joint reconstruction method for multi-contrast CS MRI is presented. This method uses non-convex group-lp-sparsity as well as joint total variation as objective functions. Finally, a joint dictionary learning based method for finding the sparsifying transformation along with the image is presented. The sparsifying transformation reconstructed by the method enforces group sparse representation on all contrast images. All the proposed methods are compared quantitatively and qualitatively with previous methods that exist in the literature using both experimental in-vivo and simulated datasets. The effectiveness of the ADMM for single contrast reconstruction is demonstrated over other single contrast methods. Then, the advantages of using joint reconstruction is discussed and demonstrated. Although dictionary learning based method require high computational cost, it presents benefits in terms of image quality is shown.


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Due to lack of widespread array imaging techniques in the THz range, point detector applications coupled with spatial modulation schemes are being investigated using compressive sensing (CS) techniques. CS algorithms coupled with innovative spatial modulation schemes which allow the control of pixels on the image plane from which the light is focused onto single pixel THz detector has been shown to rapidly generate images of objects. Using a CS algorithm, the image of an object can be reconstructed rapidly....
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Coil spatial sensitivity map is considered as one of the most valuable data used in parallel magnetic resonance imaging (MRI) reconstruction. In this study, a novel sensitivity map extraction method is introduced for phased-array coils. Proposed technique uses Biot-Savart law with coil shape information and low-resolution phase image data to form sensitivity maps. The performance of this method has been tested in the parallel image reconstruction task using sensitivity encoding technique. In MRI, coil sensi...
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Citation Formats
A. Güngör, “Compressive sensing methods for multi-contrast magnetic resonance imaging,” M.S. - Master of Science, Middle East Technical University, 2017.