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Abstract #4232

Improved Compressed Sensing Reconstruction with Overcomplete Wavelet Transforms

Alicia W. Yang1, 2, Li Feng1, 2, Jian Xu3, 4, Ivan Selesnick4, Daniel K. Sodickson1, Ricardo Otazo1

1Department of Radiology, New York University School of Medicine, New York, NY, United States; 2Sackler Institute for Biomedical Sciences, New York University, New York, NY, United States; 3Siemens Medical Solution USA Inc, NY, United States; 4Polytechnic Institute of New York University, NY, United States

An adaptive decreasing thresholding method is developed to take into consideration the structure of overcomplete transforms by (1) using local thresholds that adapt to the signal power in each band and (2) decreasing the threshold for each step of the iterative reconstruction algorithm. The performance of this method in dual-tree wavelet transforms and curvelets was tested on compressed sensing reconstructions of retrospectively undersampled 3D coronary MRA and brain image datasets, and compared to that of standard Haar wavelet transforms.