Multi-layer clustering-based residual sparsifying transform for low-dose CT image reconstruction | JoVE Visualize

Area of Science:

Medical Imaging
Computational Imaging
Signal Processing

Background:

Sparsifying transform (ST) models offer computational efficiency in medical imaging.
Deep learning models with nested structures excel at feature learning.
Low-dose CT (LDCT) reconstruction requires advanced techniques for image quality.

Purpose of the Study:

To propose a network-structured ST learning approach for X-ray computed tomography (CT), termed multi-layer clustering-based residual sparsifying transform (MCST) learning.
To apply the MCST model to LDCT reconstruction by integrating it into the penalized weighted least squares (PWLS) framework.
To enhance image reconstruction quality in LDCT by leveraging multi-layer residual maps and input clustering for accurate sparsification.

Main Methods:

The MCST model combines a multi-layer sparse representation with clustered features, each modeled by unitary transforms.
The model is trained unsupervisedly using a block coordinate descent (BCD) algorithm on a patch-based approach.
A novel PWLS-MCST algorithm is developed by integrating the pre-learned MCST signal model with PWLS optimization.

Main Results:

Experiments on phantom, numerical, and clinical LDCT datasets demonstrate the effectiveness of the MCST model.
The learned transforms within layers capture rich features, with additional information from representation residuals.
PWLS-MCST significantly outperforms conventional filtered back-projection (FBP) and PWLS with edge-preserving (EP) regularizers.
The method shows superior performance compared to advanced techniques like MARS and ULTRA, particularly in edge clarity and detail preservation.

Conclusions:

A novel multi-layer sparse signal model (MCST) with a nested network structure is introduced for CT reconstruction.
The MCST model utilizes multi-layer residual maps and input clustering for effective image sparsification.
The proposed PWLS-MCST framework provides clearer LDCT reconstructions, outperforming several baseline methods.
The code for PWLS-MCST is publicly available for further research and application.