Markov random field texture generation with an internalized database using a conditional encoder-decoder structure

Yongfeng Gao, Ti Bai, Siming Lu, Shaojie Chang, Hao Zhang, Mahsa Hoshmand-Kochi, Zhengrong Liang

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


The tissue specific MRF type texture prior (MRFt) proposed in our previous work has been demonstrated to be advantageous in various clinical tasks. However, this MRFt model requires a previous full-dose CT (FdCT) scan of the same patient to extract the texture information for LdCT reconstructions. This requirement may not be met in practice. To alleviate this limitation, we propose to build a MRFt generator by internalizing a database with paired FdCT and LdCT scans using a (conditional) encoder-decoder structure model. We denote this method as the MRFtG-ConED. This generation model depends only on physiological features thus is robust for ultra-low dose CT scans (i.e., dosage < 10mAs). When the dosage is not extremely low (i.e., dosage > 10mAs), some texture information from LdCT images reconstructed by filtered back projection (FBP) can be also used to provide extra information.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationPhysics of Medical Imaging
EditorsWei Zhao, Lifeng Yu
ISBN (Electronic)9781510649378
StatePublished - 2022
EventMedical Imaging 2022: Physics of Medical Imaging - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Physics of Medical Imaging
CityVirtual, Online


  • conditional encoder-decoder structure
  • LdCT
  • physiological knowledge
  • tissue specific MRF prior

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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