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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Magnetic resonance electrical impedance tomography (MREIT) is used to produce high resolution images of true conductivitv distribution. Images are reconstructed by utilising measurements of magnetic flux density distribution and surface potentials. Surface potential measurements are needed to reconstruct true conductivity values. In this study, a novel MREIT reconstruction algorithm is developed to generate conductivity images without utilizing the surface potential measurements. The proposed algorithm and ...
Induced Current Magnetic Resonance Electrical Impedance Tomography with z-Gradient Coil
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Magnetic Resonance Electrical Impedance Tomography (MREIT) is a medical imaging method that provides images of electrical conductivity at low frequencies (0-1 kHz). In MREIT, electrical current is applied to the body via surface electrodes and corresponding magnetic flux density is measured by means of Magnetic Resonance (MR) phase imaging techniques. By utilizing the magnetic flux density measurements and surface potential measurements images of true conductivity distribution can be reconstructed. In order...
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Özdemir, Mahir Sinan; Eyüboğlu, Behçet Murat; Department of Electrical and Electronics Engineering (2003)
In this study, a direct reconstruction algorithm for Magnetic Resonance Electrical Impedance Tomography (MR-EIT) is proposed and experimentally implemented for high resolution true conductivity imaging. In MR-EIT, elec trical impedance tomography (EIT) and magnetic resonance imaging (MRI) are combined together. Current density measurements are obtained making use of Magnetic Resonance Current Density Imaging (MR-CDI) techniques and peripheral potential measurements are determined using conventional EIT tech...
Magnetic Resonance Imaging in Inhomogeneous Magnetic Fields with Noisy Signal
Arpinar, V. E.; Eyüboğlu, Behçet Murat (2008-11-27)
In this study, an image reconstruction algorithm for a Magnetic Resonance Imaging (MRI) system with inhomogeneous magnetic fields is proposed. The proposed reconstruction algorithm uses spatial distributions of main magnetic field, Radio Frequency (RF) and gradient fields as inputs, together with the pulse sequence and the noisy Magnetic Resonance (MR) signal. To calculate the noise signal, noise model for MRI with homogeneous fields is extended for inhomogeneous magnetic fields. Using this embedded noise m...
Citation Formats
A. Güngör, “Compressive sensing methods for multi-contrast magnetic resonance imaging,” M.S. - Master of Science, Middle East Technical University, 2017.