Umeå universitets logga

Both computed tomography (CT) and magnetic resonance imaging (MRI) have a purpose for radiotherapy. But having two imaging sessions brings uncertainty which makes it beneficial to create synthetic CT (sCT) images from MR images. In this work a Generative Adversarial Network (GAN) was designed and implemented for sCT generation. The purpose of the work was to broaden the understanding of how variation in training data affects a model’s performance on generating sCTs. This was done by increasing the training sets with patients with artifacts, female patients and synthetic MR contrasts.

Eight different machine learning models with varying training data were trained and evaluated. Four models were trained using T2-weighted data only while the other four used both real T2-weighted images and synthetic T1 (sT1) images in their training sets. The models were evaluated on the pixel value difference between the CT and the resulting sCTs using a mean absolute error (MAE) evaluation. Afterwards, dose calculations were made with patients’ treatment plans on both their CTs and their corresponding sCTs and compared doses to some of the structures. Finally the models were compared based on their performance on synthetic MR contrasts. This means I used a contrast transfer model to change the contrast from a T1-weighted image to a synthetic T2 or from a T2-weighted image to a synthetic T1 and then generated sCT images from the synthetic contrasts.

These experiments showed that when I increased the models’ training sets with relevant patients MAE decreased between the CT and a generated sCT. Importantly, this was also true for our models trained on sT1s when evaluating on T1 weighted images. Increasing the size of the datasets also increased the performance in a treatment planning purpose and it also decreased the difference when evaluating the models on original MR images and synthetic MR images.

In conclusion an improved performance was shown for models evaluated on images with artifacts, female patients and other MR contrast when including more images from those image types in the training dataset.