Multiple-input multiple-output (MIMO) signal processing approach to parallel transmit and receive MRI
Abstract
The idea of using parallel coil arrays in magnetic resonance imaging (MRI) has been the focus of much research, specifically for the case in which multiple receive coils are used. Most prior transmit coil array research aims to match the excitation pattern as closely as possible to some theoretically designed target excitation. In order to improve the signal-to-noise ratio (SNR) these arrays are often used in conjunction with a high B 0 field, resulting in adverse effects to both the patient and the image quality. The transmit coils must combat all of these effects. Past approaches attempt to optimize the physical properties of the system, despite the fact that these might lead to suboptimal reconstructed images. An additional development in recent years in MRI is that of coil selection in the receive coil array. Doing this reduces reconstruction time by choosing the coils which contribute the most to the reconstructed image. We propose taking a multiple-input multiple-output (MIMO) signal processing perspective for parallel transmit and receive signaling in which we try to optimize a performance metric given the constraints of the system. Given joint sensitivity information for the coils in the transmit and receive arrays, we can work within these constraints to maximize an objective function (e.g., SNR) given by combining techniques from sensitivity encoding (SENSE) and MIMO signal processing. The optimization of the objective function will be used to directly improve image quality. We use a process akin to beamforming to pick gain terms on the pulses generated by the transmit coils in order to optimize the chosen objective function to obtain better image quality. We propose techniques using small tip-angle assumption. We also combine this transmit optimization with receive coil selection.
Degree
M.S.
Advisors
Love, Purdue University.
Subject Area
Biomedical engineering|Electrical engineering|Medical imaging
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