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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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