Low-dose CT reconstruction assisted by a global CT image manifold prior

Chenyang Shen, Guoyang Ma, Xun Jia

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

Abstract

The use of X-ray Computed Tomography (CT) leads to the concern of lifetime cancer risk. Low-dose CT scan with reduced mAs can reduce the radiation exposure, but the image quality is usually degraded due to excessive image noise. Numerous studies have been conducted to regularize CT image during reconstruction for better image quality. In this paper, we propose a fully data-driven manifold learning approach. An auto-encoder-decoder convolutional neural network is established to map an entire CT image to the inherent low-dimensional manifold, and then to restore the CT image from its manifold representation. A novel reconstruction algorithm assisted by the leant manifold prior is developed to achieve high quality low-dose CT reconstruction. We perform comprehensive simulation studies using patient abdomen CT images. The trained network is capable of restoring high-quality CT images with average error of ∼ 20 HU. The manifold prior assisted reconstruction scheme achieves high-quality low-dose CT reconstruction, with average reconstruction error of ∼ 38.5 HU, 4.6 times and 3 times lower than that of filtered back projection method and total-variation based iterative reconstruction method, respectively.

Original languageEnglish (US)
Title of host publication15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
EditorsSamuel Matej, Scott D. Metzler
PublisherSPIE
ISBN (Electronic)9781510628373
DOIs
StatePublished - Jan 1 2019
Event15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 - Philadelphia, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11072
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019
CountryUnited States
CityPhiladelphia
Period6/2/196/6/19

Keywords

  • Iterative CT reconstruction
  • Low-dose CT
  • Manifold constraint

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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  • Cite this

    Shen, C., Ma, G., & Jia, X. (2019). Low-dose CT reconstruction assisted by a global CT image manifold prior. In S. Matej, & S. D. Metzler (Eds.), 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine [1107205] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11072). SPIE. https://doi.org/10.1117/12.2534959