Real-time MRI-guided radiotherapy with deep learning

Abstract

Radiotherapy is a common treatment for cancer, which uses radiation to destroy cancer cells. The goal is to deliver a high dose to the tumor without irradiating healthy tissue, but the organ motion due to, for example, respiration makes this difficult. You can see what you treat by irradiating patients with hybrid MRI/radiation devices (MRI-Linac). With real-time adaptive MRI-guided radiotherapy (MRIgRT), the radiation beam is continuously adjusted to compensate for respiratory motion. A major challenge is the speed of MRI acquisitions, and it can take several minutes to acquire high-quality, high-resolution three-dimensional MRI. To compensate for respiratory motion, MRI must be acquired, reconstructed into an image, and the motion computation must be completed within 200 milliseconds. Deep learning is a promising solution due to its speed and quality when minimal MRI data is available. In this thesis we investigated the applicability of deep learning for MRI-guided radiotherapy. The research shows that neural networks can determine three-dimensional motion within 200 milliseconds with an error of less than 1 millimeter while using 25x fewer data than conventional MRI scans. Moreover, we discovered that traditional training of neural networks causes systematic errors in motion models. In particular, minimizing the L2 norm in the complex domain results in an underestimation of the recovered magnitude. This is problematic for radiotherapeutic applications as this will result in systematic errors. To resolve this, we have proposed a new loss function to train neural networks that is fully symmetric with respect to the recovered magnitude and phase. Finally, we have investigated the acceleration of the acquisition and reconstruction of the so-called 4D-MRI, which is important for MRI-guided radiotherapy to quantify the motion magnitude but takes a long time to acquire. Deep learning can potentially reconstruct accelerated 4D-MRI acquisitions with high quality, but training these models is challenging due to the high-dimensional nature of 4D-MRI. This makes it difficult to capture all spatio-temporal information in a computationally feasible way. We have proposed a new deep learning model that uses separate spatial and temporal sub-networks, which enables high-quality 4D-MRI reconstruction with significantly fewer trainable parameters. Applying this model to 4D-MRI reconstruction resulted in a significant acceleration of 4D-MRI acquisition and reconstruction without losing quality. This thesis has shown that deep learning is a feasible technology for radiotherapy and could enable more effective treatments with fewer side effects and reduce the number of treatment fractions. Although challenges remain for clinical implementation, neural networks are a promising technology for real-time adaptive MRIgRT.