No-reference denoising of low-dose CT projections
This work is significant for radiologists and patients by enabling effective denoising of low-dose CT images, which reduces radiation exposure without compromising diagnostic quality, especially when paired high-dose data is not available.
This paper addresses the problem of denoising low-dose CT projections without reference high-dose data, which is often unavailable. The authors propose a self-supervised method that leverages connections between adjacent images, achieving accuracy comparable to supervised methods and outperforming existing self-supervised approaches.
Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising. Unlike existing self-supervised approaches, the proposed method requires only noisy CT projections and exploits the connections between adjacent images. The experiments carried out on an LDCT dataset demonstrate that our method is almost as accurate as the supervised approach, while also outperforming the considered self-supervised denoising methods.