Unsupervised Low-Dose CT Reconstruction With One-Way Conditional Normalizing Flows

Abstract

Deep-learning techniques have demonstrated significant potential in low-dose computed tomography (LDCT) reconstruction. Nevertheless, supervised methods are limited by the scarcity of labeled data in clinical scenarios, while CNN-based unsupervised denoising methods often result in excessive smoothing of reconstructed images. Although normalizing flows (NFs) based methods have shown promise in generating detail-rich images and avoiding over-smoothing, they face two key challenges: (1) Existing two-way transformation strategies between noisy images and latent variables, despite leveraging the regularization and generation capabilities of NFs, can lead to detail loss and secondary artifacts; and (2) Training NFs on high-resolution CT images is computationally intensive. While conditional normalizing flows (CNFs) can mitigate computational costs by learning conditional probabilities, current methods rely on labeled data for conditionalization, leaving unsupervised CNF-based LDCT reconstruction an unresolved challenge. To address these issues, we propose a novel unsupervised LDCT iterative reconstruction algorithm based on CNFs. Our approach implements a strict one-way transformation during alternating optimization in the dual spaces, effectively preventing detail loss and secondary artifacts. Additionally, we propose an unsupervised conditionalization strategy, enabling efficient training of CNFs on high-resolution CT images and achieving fast, high-quality unsupervised reconstruction. Experimental results across multiple datasets demonstrate that the proposed method outperforms several state-of-the-art unsupervised methods and even rivals some supervised approaches.