The impact of variable MRI acquisition parameters on deep learning-based synthetic CT generation

Deep learning-based synthetic CT generation models are generally trained and evaluated on MR images obtained with a single set of acquisition parameters. In this study, we investigated the robustness of such models to clinically plausible changes in acquisition parameters by training and evaluating models on MR images acquired and reconstructed from gradient echo sequences at different echotimes (TE), resolution and flip angles. We investigated the sensitivity to TEs by training models on randomly interspersed multi-echo gradient echo MR images acquired at different TEs. Multi-echo trained models achieved better generalization performance to varying acquisition parameters without excessively compromising results on dedicated data.