Proj2Proj: self-supervised low-dose CT reconstruction

• Published in: PeerJ. Computer science Vol. 10; p. e1849
• Main Authors: Unal, Mehmet Ozan, Ertas, Metin, Yildirim, Isa
• Format: Journal Article
• Language: English
• Published: United States PeerJ Inc 29.02.2024
• Subjects: Artificial Intelligence; Bioinformatics; Computer Vision; Deep learning; Image reconstruction; Low-dose CT; Neural Networks; Self-supervised learning; Deep learning; Low-dose CT; Image reconstruction; Self-supervised learning
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1849

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.