Abstract #0151

Towards Real-Time Beam Adaptation on an MRI-Linac using AUTOMAP

David Waddington^1,2, Nicholas Hindley¹, Neha Koonjoo^2,3, Tess Reynolds¹, Bo Zhu^2,3, Chiara Paganelli⁴, Matthew Rosen^2,3,5, and Paul Keall¹

¹ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia, ²A. A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Department of Physics, Harvard University, Cambridge, MA, United States, ⁴Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy, ⁵Harvard Medical School, Boston, MA, United States

MRI-Linacs are new cancer treatment machines integrating radiotherapy with MRI. Dynamically adapting the radiation beam on the basis of MR-detected anatomical changes (e.g. respiratory and cardiac motion) promises to increase the accuracy of MRI-Linac treatments. A key challenge in real-time beam adaptation is accurately reconstructing images in real time. Historically, reconstruction of data acquired with accelerated techniques, such as compressed sensing, has been very slow. Here, we use AUTOMAP, a machine-learning framework, to quickly and accurately reconstruct radial MRI data simulated from a digital thorax phantom. These results will guide development of real-time adaptation technologies on MRI-Linacs.

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