Automatic online quality control of synthetic CTs

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

doi:10.1117/12.2549286

10 March 2020 Automatic online quality control of synthetic CTs

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

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113131M (2020) https://doi.org/10.1117/12.2549286
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Louis D. van Harten, Jelmer M. Wolterink, Joost J. C. Verhoeff, and Ivana Išgum "Automatic online quality control of synthetic CTs", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131M (10 March 2020); https://doi.org/10.1117/12.2549286

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