Abstract #3787
Reconstruction Strategies for Pure 2D Spatiotemporal MRI
Albert Jang 1,2 , Alexander Gutierrez 3 , Di Xiao 2 , Curtis A. Corum 1 , Vuk Mandic 4 , Jarvis Haupt 2 , and Michael Garwood 1
1
Center for Magnetic Resonance Research and
Department of Radiology, University of Minnesota,
Minneapolis, MN, United States,
2
Department
of Electrical and Computer Engineering, University of
Minnesota, Minneapolis, MN, United States,
3
Department
of Mathematics, University of Minnesota, Minneapolis,
MN, United States,
4
School
of Physics and Astronomy, Department of Physics,
University of Minnesota, Minneapolis, Minneapolis, MN,
United States
Spatiotemporal-based encoding offers certain advantages
over traditional Fourier-based encoding, enabling an
alternative way of doing MRI. Two new reconstruction
approaches, maximum-likelihood estimation (MLE) and
total variation regularization (TVR), are evaluated for
spatiotemporal encoding and compared with conventional
methods (Cartesian gridded Fourier Transform and
pseudo-inverse). It is demonstrated that MLE and TVR
generate better images in terms of resolution and can
compensate for non-uniform excitation profiles as well.
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