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Lombardo, Elia ORCID: 0000-0003-4522-8298; Rabe, Moritz ORCID: 0000-0002-7085-4066; Xiong, Yuqing; Nierer, Lukas; Cusumano, Davide ORCID: 0000-0001-8626-0003; Placidi, Lorenzo; Boldrini, Luca; Corradini, Stefanie; Niyazi, Maximilian; Belka, Claus ORCID: 0000-0002-1287-7825; Riboldi, Marco ORCID: 0000-0002-2431-4966; Kurz, Christopher; Landry, Guillaume ORCID: 0000-0003-1707-4068 (2022): Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy. In: Physics in Medicine & Biology, Vol. 67, No. 9, 095006
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Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.