Purpose To investigate the impact of image registration on deep learning-based synthetic CT (sCT) generation. Methods Paired MR images and CT scans of the pelvic region of radiotherapy patients were obtained and non-rigidly registered. After a manual verification of the registrations, the dataset was split into two groups containing either well-registered or poorly-registered MR-CT pairs. In three scenarios, a patch-based U-Net deep learning architecture was trained for sCT generation on (i) exclusively well-registered data, (ii) mixtures of well-registered and poorly-registered data or on (iii) poorly-registered data only. Furthermore, a failure case was designed by introducing a single misregistered subject in the training set of six well-registered subjects. Reconstruction quality was assessed using mean absolute error (MAE) in the entire body and specifically in bone and Dice similarity coefficient (DSC) evaluated cortical bone geometric fidelity. Results The model trained on well registered data had an average MAE of 27.6 ± 2.6HU on the entire body contour and 79.1 ± 16.1HU on the bone. The average cortical bone DSC was 0.89. When patients with registration errors were added to the training, MAE’s were higher and DSC lower with variations by up to 36HU for the average MAEbone. The failure mode demonstrated the potential far-reaching consequences of a single misregistered subject in the training set with variations by up to 38HU for MAEbone. Conclusion Poor registration quality of the training set had a negative impact on paired, deep learning-based sCT generation. Notably, as low as one poorly-registered MR-CT pair in the training phase was capable of drastically altering a model.