Probabilistic self-learning framework for Low-dose CT Denoising

Ti Bai, Dan Nguyen, Biling Wang, Steve Jiang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - May 30 2020

Keywords

  • CT
  • Deep learning
  • Denoise
  • Self-learning

ASJC Scopus subject areas

  • General

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