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Proceedings Paper

Automatic online quality control of synthetic CTs
Author(s): Louis D. van Harten; Jelmer M. Wolterink; Joost J. C. Verhoeff; Ivana Išgum
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Paper Abstract

Accurate MR-to-CT synthesis is a requirement for MR-only work flows 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 work flows.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131M (10 March 2020); doi: 10.1117/12.2549286
Show Author Affiliations
Louis D. van Harten, Amsterdam Univ. Medical Ctr., Univ. of Amsterdam (Netherlands)
Univ. Medical Ctr. Utrecht (Netherlands)
Image Sciences Institute (Netherlands)
Jelmer M. Wolterink, Amsterdam Univ. Medical Ctr., Univ. of Amsterdam (Netherlands)
Univ. Medical Ctr. Utrecht (Netherlands)
Joost J. C. Verhoeff, Univ. Medical Ctr. Utrecht (Netherlands)
Ivana Išgum, Amsterdam Univ. Medical Ctr., Univ. of Amsterdam (Netherlands)
Image Sciences Image Sciences Institute (Netherlands)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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