A GPU-accelerated MRI sequence simulator for differentiable optimization and learning

dc.contributor.advisorTamir, Jon (Jonathan I.)
dc.creatorRakshit, Somnath
dc.creator.orcid0000-0002-2017-5997
dc.date.accessioned2021-09-01T02:57:42Z
dc.date.available2021-09-01T02:57:42Z
dc.date.created2021-05
dc.date.issued2021-05-10
dc.date.submittedMay 2021
dc.date.updated2021-09-01T02:57:43Z
dc.description.abstractThe Extended Phase Graph (EPG) Algorithm is a powerful tool for magnetic resonance imaging (MRI) sequence simulation and quantitative fitting, but simulators based on EPG are mostly written to run on CPU only and (with some exception) are poorly parallelized. A parallelized simulator compatible with other learning-based frameworks would be a useful tool to optimize scan parameters, estimate tissue parameter maps, and combine with other learning frameworks. In this thesis, I present our work on an open source, GPU-accelerated EPG simulator written in PyTorch. Since the simulator is fully differentiable by means of automatic differentiation, it can be used to take derivatives with respect to sequence parameters, e.g. flip angles, as well as tissue parameters, e.g. T₁ and T₂. Here, I describe the simulator package and demonstrate its use for a number of MRI tasks including tissue parameter estimation and sequence optimization.
dc.description.departmentInformation
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/87329
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/14279
dc.language.isoen
dc.subjectEPG
dc.subjectMRI
dc.subjectSimulator
dc.subjectParallelization
dc.subjectAuto-differentiable
dc.titleA GPU-accelerated MRI sequence simulator for differentiable optimization and learning
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentInformation
thesis.degree.disciplineInformation Studies
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Information Studies

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