Automatic online quality control of synthetic CTs

Louis D. van Harten*, Jelmer M. Wolterink, Joost J.C. Verhoeff, Ivana Išgum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Accurate MR-to-CT synthesis is a requirement for MR-only workflows in radiotherapy (RT) treatment planning. In recent years, deep learning-based approaches have shown impressive results in this field. However, to prevent downstream errors in RT treatment planning, it is important that deep learning models are only applied to data for which they are trained and that generated synthetic CT (sCT) images do not contain severe errors. For this, a mechanism for online quality control should be in place. In this work, we use an ensemble of sCT generators and assess their disagreement as a measure of uncertainty of the results. We show that this uncertainty measure can be used for two kinds of online quality control. First, to detect input images that are outside the expected distribution of MR images. Second, to identify sCT images that were generated from suitable MR images but potentially contain errors. Such automatic online quality control for sCT generation is likely to become an integral part of MR-only RT workflows.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE Press
ISBN (Electronic)9781510633933
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventSPIE Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling - Marriott Marquis Houston, Houston, United States
Duration: 15 Feb 202020 Feb 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11313
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging 2020
CountryUnited States
CityHouston
Period15/02/2020/02/20

Keywords

  • Convolutional neural network
  • Deep learning
  • Evaluation
  • Pseudo CT
  • Quality control
  • Synthetic CT
  • Uncertainty

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