Acquisition and reconstruction techniques for improving rapid magnetic resonance imaging with applications in fetal imaging
Author(s)Arefeen, Yamin I.(Yamin Ishraq)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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Magnetic Resonance Imaging (MRI) serves as a powerful and noninvasive medical imaging modality. It gives clinicians the ability to image a wide variety of physical parameters, control image content, and acquire images at arbitrary spatial orientations and resolutions. Recently, MRI has gained traction as a method to supplement the diagnostic capability of Ultrasound for pregnant mothers and fetuses. In particular, MR images dominated by T2 contrast, an intrinsic tissue parameter, give informative visualization for diagnosis. Traditional T2 weighted imaging techniques take on the order of seconds to minutes to acquire an image. However, due to the frequency and unpredictability of fetal motion, imaging techniques in fetal MRI must acquire a 2D image in under a second. T2-weighted fetal MRI techniques, called single shot acquisitions, achieve rapid imaging speed by acquiring all desired data after a single radio frequency excitation, but constraining all measurements to be taken after a single excitation produces images with spatially dependent blurring and poor contrast. As a result, many single shot techniques produce images with reduced diagnostic capability, in comparison to methods with longer acquisition times. This thesis aims to improve the diagnostic quality of T2-weighted single shot images. We incorporate the physical effects of rapid imaging into the image acquisition model and solve the resulting underdetermined system of equations using prior knowledge. We verify the efficacy of the techniques both in simulation and invivo adult brain experiments. Finally, we discuss how our proof of concent technique and results can be translated to the fetal imaging context.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 65-68).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.