Self-supervised Physics-based Denoising for Computed Tomography

• URL: http://arxiv.org/abs/2211.00745v1
• Date: Tue, 1 Nov 2022 20:58:50 GMT
• Title: Self-supervised Physics-based Denoising for Computed Tomography
• Authors: Elvira Zainulina and Alexey Chernyavskiy and Dmitry V. Dylov
• Abstract summary: Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation. Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images. Modern deep learning noise suppression methods alleviate the challenge but require low-noise-high-noise CT image pairs for training. We introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM that can be trained without the high-dose CT projection ground truth images.
• Score: 2.2758845733923687
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images. Ultimately, these issues could affect the perception of medical personnel and could cause misdiagnosis. Modern deep learning noise suppression methods alleviate the challenge but require low-noise-high-noise CT image pairs for training, rarely collected in regular clinical workflows. In this work, we introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM that can be trained without the high-dose CT projection ground truth images. Unlike previously proposed self-supervised techniques, the introduced method exploits the connections between the adjacent projections and the actual model of CT noise distribution. Such a combination allows for interpretable no-reference denoising using nothing but the original noisy LDCT projections. Our experiments with LDCT data demonstrate that the proposed method reaches the level of the fully supervised models, sometimes superseding them, easily generalizes to various noise levels, and outperforms state-of-the-art self-supervised denoising algorithms.

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