CT-MRI pelvic deformable registration via deep learning

An accurate and robust image registration of computed tomography (CT) and magnetic resonance imaging (MRI) plays an important role in establishing a desired radiation treatment plan. Traditional image similarity measures such as cross-correlation, mean absolute error, mean squared error have very limited success in multi modal MRI-CT image registration. In this study, we propose a deformable registration method based on unsupervised deep neural networks to register MRI and CT for pelvic patients. No ground truth deformation vector field (DVF) is needed during training. A cross-modality image similarity loss, called as self-correlation descriptor, is used as loss function to learn the trainable parameters in deep neural networks. After training, for a new patient's CT and MRI, the deformed MRI is obtained via first feeding the MRI and CT into the deep neural networks to derive the DVF, then deformed via spatial transformation on MRI and DVF. We evaluated our method by retrospectively revisiting 25 patients with MRI and CT acquired at pelvic region. Target registration error (TRE) was used to quantify the performance of the proposed method. The average TRE of the proposed method is 2.23±1.11 mm. It demonstrates the great potential of the proposed method in performing accurate image registration that can facilitate multimodality imaging treatment planning workflow in prostate cancer radiotherapy.