Quality-guided deep reinforcement learning for parameter tuning in iterative CT reconstruction

Chenyang Shen, Min Yu Tsai, Yesenia Gonzalez, Liyuan Chen, Steve B. Jiang, Xun Jia

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

1 Scopus citations

Abstract

Tuning parameters in a reconstruction model is of central importance to iterative CT reconstruction, since it critically affects the resulting image quality. Manual parameter tuning is not only tedious, but becomes impractical when there exits a number of parameters. In this paper, we develop a novel deep reinforcement learning (DRL) framework to train a parameter-tuning policy network (PTPN) to automatically adjust parameters in a human-like manner. A quality assessment network (QAN) is trained together with PTPN to learn how to judge CT image quality, serving as a reward function to guide the reinforcement learning. We demonstrate our idea in an iterative CT reconstruction problem with pixel-wise total-variation regularization. Experimental results demonstrates the effectiveness of both PTPN and QAN, in terms of tuning parameter and evaluating image quality, 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

    Fingerprint

Keywords

  • CT reconstruction
  • Deep reinforcement learning
  • Discriminative learning
  • Parameter tuning

ASJC Scopus subject areas

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

Cite this

Shen, C., Tsai, M. Y., Gonzalez, Y., Chen, L., Jiang, S. B., & Jia, X. (2019). Quality-guided deep reinforcement learning for parameter tuning in iterative CT reconstruction. In S. Matej, & S. D. Metzler (Eds.), 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine [1107203] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11072). SPIE. https://doi.org/10.1117/12.2534948