Tumor tracking in 4D CT images for adaptive radiotherapy

Radiotherapy plays an important role in the management of lung cancer for many patients. During radiation treatment the respiratory motion of the patient impedes the possibility to accurately target the tumor, requiring large treatment margins. This leads to additional radiation to healthy tissue, and the associated toxicity. Real-time adaptive radiotherapy is a promising direction for solving this problem, where radiation beams are shaped continuously to track the tumor based on real-time image analysis, which is possible with e.g. MRI-guided radiotherapy. To assist in the MR-Linac planning process, we developed a U-Net based tumor tracking method that uses a double encoder structure to incorporate both 3D+t planning CT images and a 3D planning scan with corresponding segmentation. Our best model achieves 0.60 surface Dice and 92% recall.