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
Department of Diagnostic Imaging and Nuclear
Medicine, Graduate School of Medicine, Kyoto University,
Kyoto, Kyoto, Japan,
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.
This abstract and the presentation materials are available to members only;
a login is required.