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Abstract #0813

Diaphragm motion prediction with a LSTM network using MRI k-space data

Carola Fischer1,2, Florian Friedrich1,2, Peter Bachert1,2, Mark E. Ladd1, and Benjamin R. Knowles1
1Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany

Hybrid MRI linear accelerators (MR-linac) enable real-time tracking of tumor motion during treatment. Due to system latencies that delay treatment adjustments, one has to predict as well as track motion. This abstract presents a feasibility study to predict diaphragm motion using MRI k-space data using a long short-term memory (LSTM) recurrent neural network, by comparing simulation, phantom and an in vivo study. First experiments show that prediction accuracies of approximately 1.7mm are possible at 400ms latencies for the diaphragm with guided breathing.

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