Low Dose CT Image Reconstruction With Learned Sparsifying Transform
This work addresses the challenge of reducing X-ray dose while maintaining image quality in computed tomography, representing an incremental improvement over prior regularization techniques.
The authors tackled low-dose CT image reconstruction by proposing a method combining penalized weighted-least squares with a learned sparsifying transform, which dramatically improved image quality compared to existing methods at low dose levels, as shown in numerical experiments on the XCAT phantom.
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).