Probabilistic self-learning framework for Low-dose CT Denoising
This addresses the challenge of reducing radiation exposure in medical CT scans while maintaining image quality, which is crucial for patient safety and clinical diagnostics, though it is an incremental improvement over existing unsupervised methods.
The paper tackled the problem of denoising low-dose CT images without requiring paired normal-dose CT data by developing a probabilistic self-learning framework that leverages shift-invariant properties to learn pixel correlations and noise distribution from LDCT images alone, resulting in enhanced images that closely match the style of routine NDCT images and outperform competitors.
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.