Online Adaptive Radiotherapy for Locally Advanced Lung Cancer
Envisaged AI dose accumulation workflow.
The objective of project S3, carried out at the LMU University Hospital Munich, is to develop a novel workflow for dose accumulation using deep learning, tailored to lung cancer patients treated with MR-guided online adaptive radiotherapy. To this aim, deformation vector fields (DVFs) are required to sum up the dose distributions of the individual treatment fractions on the reference planning anatomy. Since the generation of DVFs is generally improved by providing additional constraints, such as organ boundaries, project S3 aims at employing automated segmentation of the thorax region to guide the deformation. The first objective of the project will thus be to establish an MR-based deep learning auto-segmentation framework tailored to the lung region and capable of segmenting the lung, heart, esophagus, spinal cord as well as the tumor itself. In a second step, a deep convolutional neural network will be trained to output DVFs directly from a given pair of input images (fraction and planning, see Figure). Finally, the DVFs will be used to compute accumulated doses and compared to a state-of-the-art non-AI accumulation workflow. The accumulated doses will serve as a basis for analysis in terms of patient outcome modelling as suggested in subproject S5 and will also be used in subprojects S6 and S4.
Guillaume Landry
Professor of Image Guided Radiation Therapy
My research interests include image guidance and artificial intelligence.
Christopher Kurz
Group Leader in MR Guided Radiation Therapy
My research interests include MRgRT and CBCT imaging.
