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Abstract #0151

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

David Waddington1,2, Nicholas Hindley1, Neha Koonjoo2,3, Tess Reynolds1, Bo Zhu2,3, Chiara Paganelli4, Matthew Rosen2,3,5, 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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