Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows

TL;DR

This paper introduces an unsupervised low-dose CT reconstruction method using one-way conditional normalizing flows, which improves detail preservation and reduces artifacts compared to previous two-way strategies.

Contribution

It proposes a novel one-way transformation strategy and an unsupervised conditionalization approach for high-resolution CT reconstruction with normalizing flows.

Findings

Outperforms some state-of-the-art unsupervised methods

Achieves high-quality reconstruction without labeled data

Reduces artifacts and detail loss in reconstructed images

Abstract

Deep-learning methods have shown promising performance for low-dose computed tomography (LDCT) reconstruction. However, supervised methods face the problem of lacking labeled data in clinical scenarios, and the CNN-based unsupervised denoising methods would cause excessive smoothing in the reconstructed image. Recently, the normalizing flows (NFs) based methods have shown advantages in producing detail-rich images and avoiding over-smoothing, however, there are still issues: (1) Although the alternating optimization in the data and latent space can well utilize the regularization and generation capabilities of NFs, the current two-way transformation strategy of noisy images and latent variables would cause detail loss and secondary artifacts; and (2) Training NFs on high-resolution CT images is hard due to huge computation. Though using conditional normalizing flows (CNFs) to learn conditional probability can reduce the computational burden, current methods require labeled data for conditionalization, and the unsupervised CNFs-based LDCT reconstruction remains a problem. To tackle these problems, we propose a novel CNFs-based unsupervised LDCT iterative reconstruction algorithm. It employs strict one-way transformation when performing alternating optimization in the dual spaces, thus effectively avoiding the problems of detail loss and secondary artifacts. By proposing a novel unsupervised conditionalization strategy, we train CNFs on high-resolution CT images, thus achieving fast and high-quality unsupervised reconstruction. Experiments on different datasets suggest that the performance of the proposed algorithm could surpass some state-of-the-art unsupervised and even supervised methods.