Abstract #2541
TOF-MRA reconstruction from undersampled data: Comparison of three different regularization methods
Akira Yamamoto 1 , Koji Fujimoto 1 , Yasutaka Fushimi 1 , Tomohisa Okada 1 , Kei Sano 2 , Toshiyuki Tanaka 2 , and Kaori Togashi 1
1
Department of Diagnostic Imaging and Nuclear
Medicine, Graduate School of Medicine, Kyoto University,
Kyoto, Kyoto, Japan,
2
Department
of Systems Science, Graduate School of Informatics,
Kyoto University, Kyoto, Kyoto, Japan
Three different regularization methods, L1-norm,
wavelet, and total variation in NESTA method for
undersampled TOF-MRA image reconstruction were
evaluated. In qualitative visual analysis, subtle but
distinct difference was noted among them. In
quantitative analysis, L1-norm showed the largest
vessel-brain-ratio and more than 30 % undersampled data
seemed sufficient for TOF-MRA reconstruction.
Undersampled data less than 30 % showed visible image
degradation. In conclusion, NESTA method can be used for
TOF-MRA undersampled data reconstruction and L1-norm
should be a choice for regularization method.
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