Low Dose CT Image Reconstruction With Learned Sparsifying Transform
Abstract
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt an alternating algorithm to optimize the PWLS-ST cost function that alternates between a CT image update step and a sparse coding step. We adopt a relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed OS-LALM) to accelerate the CT image update step by reducing the number of forward and backward projections. Numerical experiments on the XCAT phantom show that for low dose levels, the proposed PWLS-ST method dramatically improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP).
Keywords
Cite
@article{arxiv.1707.02914,
title = {Low Dose CT Image Reconstruction With Learned Sparsifying Transform},
author = {Xuehang Zheng and Zening Lu and Saiprasad Ravishankar and Yong Long and Jeffrey A. Fessler},
journal= {arXiv preprint arXiv:1707.02914},
year = {2017}
}
Comments
This is a revised and corrected version of the IEEE IVMSP Workshop paper DOI: 10.1109/IVMSPW.2016.7528219