  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.
