Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction

TL;DR

This paper introduces a novel multi-layer residual sparsifying transform (MARS) model that combines sparse representations and deep learning for improved low-dose CT image reconstruction, demonstrating superior results over traditional methods.

Contribution

The paper develops a new multi-layer transform learning framework called MARS, extending classical sparsifying models with residual layers, and applies it to low-dose CT reconstruction with an efficient unsupervised learning algorithm.

Findings

MARS outperforms FBP and PWLS with edge-preserving regularizer in RMSE and SSIM.

MARS better preserves subtle details compared to single-layer models.

Experimental results on XCAT and Mayo data validate the effectiveness of MARS.

Abstract

Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multi-layer model learned in an unsupervised manner by combining both sparse representations and deep models. The proposed framework extends the classical sparsifying transform model for images to a Multi-lAyer Residual Sparsifying transform (MARS) model, wherein the transform domain data are jointly sparsified over layers. We investigate the application of MARS models learned from limited regular-dose images for low-dose CT reconstruction using Penalized Weighted Least Squares (PWLS) optimization. We propose new formulations for multi-layer transform learning and image reconstruction. We derive an efficient block coordinate descent algorithm to learn the transforms across layers, in an unsupervised manner from limited regular-dose images. The learned model is then incorporated into the low-dose image reconstruction phase. Low-dose CT experimental results with both the XCAT phantom and Mayo Clinic data show that the MARS model outperforms conventional methods such as FBP and PWLS methods based on the edge-preserving (EP) regularizer in terms of two numerical metrics (RMSE and SSIM) and noise suppression. Compared with the single-layer learned transform (ST) model, the MARS model performs better in maintaining some subtle details.