Unsupervised low-dose CT reconstruction with one-way conditional normalizing flows

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An, Ran and Li, Lemeng and Chen, Ke and Li, Hongwei (2025) Unsupervised low-dose CT reconstruction with one-way conditional normalizing flows. IEEE Transactions on Computational Imaging, 11. 485 - 496. ISSN 2333-9403 (https://doi.org/10.1109/TCI.2025.3553039)

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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.

ORCID iDs

An, Ran, Li, Lemeng, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623 and Li, Hongwei;
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