Room: AAPM ePoster Library
Purpose: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a cycle generative adversarial network (CycleGAN) approach.
Methods: We conducted a study in 77 patients with brain tumors who had undergone both MRI and computed tomography (CT) imaging as part of their simulation for external beam treatment planning. We designed a CycleGAN network to generate synthetic CT images from MRI images. We used Mutual Information (MI) as the loss function in the generator to overcome the misalignment between MRI and CT images (unregistered data). The model was trained using all MRI slices with corresponding CT slices from each training subject’s MRI/CT pair.
Results: The results extracted from treatment planning system showed excellent agreement for most DVH metrics computed on the CT and sCT, with a mean absolute difference below 2% (1.2 Gy) of the prescription dose for the planning target volume (PTV) and the organs at risk (OARs). This demonstrates a high dosimetric accuracy for the generated sCT images.
Conclusions: Our CycleGAN model developed were produced highly accurate synthetic CT images from conventional, single-sequence MRI images in seconds. Our proposed method has strong potential to perform well in a clinical workflow for MRI-only brain treatment planning.