Automatic Online Quality Control of Synthetic CTs
This addresses the need for robust quality control in MR-only radiotherapy workflows to prevent treatment errors, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of ensuring reliability in MR-to-CT synthesis for radiotherapy by proposing an ensemble-based uncertainty measure to detect out-of-distribution inputs and errors in synthetic CT images, showing its potential for integration into clinical workflows.
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.