Union of Learned Sparsifying Transforms Based Low-Dose 3D CT Image Reconstruction

Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler


Published in:Fully3D 2017 Proceedings


We propose a new penalized weighted-least squares (PWLS) reconstruction method that exploits regularization based on an efficient Union of Learned TRAnsforms (PWLS-ULTRA). The union of square transforms is pre-learned from numerous 3D patches extracted from a dataset of CT volumes. The proposed PWLS-based cost function is optimized by alternating between a CT image reconstruction step, and a sparse coding and clustering step. The CT image reconstruction step is accelerated by a relaxed linearized augmented Lagrangian method with orderedsubsets that reduces the number of forward and backward projections. Simulations with 3D axial CT scans of the XCAT phantom show that for low-dose levels, the proposed method significantly improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP). PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.
Xuehang Zheng
University of Michigan- Shanghai Jiao Tong University Joint Institute
Saiprasad Ravishankar
University of Michigan- Shanghai Jiao Tong University Joint Institute
Yong Long
University of Michigan- Shanghai Jiao Tong University Joint Institute
Jeffrey A. Fessler
University of Michigan
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