S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement

Chaoyang Zhang, Shaojie Chang, Ti Bai, Xi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting energy properties of the scanned object. Due to the limited photon numbers and the non-ideal detector response of each energy channel, the reconstructed images usually contain much noise. With the development of Deep Learning (DL) technique, different kinds of DL-based models have been proposed for noise reduction. However, most of the models require clean data set as the training labels, which are not always available in medical imaging field. Inspiring by the similarities of each channel's reconstructed image, we proposed a self-supervised learning based PCCT image enhancement framework via multi-spectral channels (S2MS). In S2MS framework, both the input and output labels are noisy images. Specifically, one single channel image was used as output while images of other single channels and channel-sum image were used as input to train the network, which can fully use the spectral data information without extra cost. The simulation results based on the AAPM Low-dose CT Challenge database showed that the proposed S2MS model can suppress the noise and preserve details more effectively in comparison with the traditional DL models, which has potential to improve the image quality of PCCT in clinical applications.

Original languageEnglish (US)
Title of host publication7th International Conference on Image Formation in X-Ray Computed Tomography
EditorsJoseph Webster Stayman
ISBN (Electronic)9781510656697
StatePublished - 2022
Event7th International Conference on Image Formation in X-Ray Computed Tomography - Virtual, Online
Duration: Jun 12 2022Jun 16 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference7th International Conference on Image Formation in X-Ray Computed Tomography
CityVirtual, Online


  • deep learning
  • denoising
  • Noise2Noise
  • Spectral CT

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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