Probabilistic self-learning framework for Low-dose CT Denoising

• URL: http://arxiv.org/abs/2006.00327v2
• Date: Fri, 22 Jan 2021 04:41:30 GMT
• Title: Probabilistic self-learning framework for Low-dose CT Denoising
• Authors: Ti Bai, Dan Nguyen, Biling Wang and Steve Jiang
• Abstract summary: Decreasing the exposure can reduce the dose and hence the radiation-related risk. Supervised deep learning can be used to train a neural network to denoise the low-dose CT (LDCT)
• Score: 1.8734449181723827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases. Decreasing the exposure can reduce the dose and hence the radiation-related risk, but will also induce higher quantum noise. Supervised deep learning can be used to train a neural network to denoise the low-dose CT (LDCT). However, its success requires massive pixel-wise paired LDCT and normal-dose CT (NDCT) images, which are rarely available in real practice. To alleviate this problem, in this paper, a shift-invariant property based neural network was devised to learn the inherent pixel correlations and also the noise distribution by only using the LDCT images, shaping into our probabilistic self-learning framework. Experimental results demonstrated that the proposed method outperformed the competitors, producing an enhanced LDCT image that has similar image style as the routine NDCT which is highly-preferable in clinic practice.

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