most citedPrediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy

16 citations · 27 across the 2 of their papers we have counts for

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2 papers

eess.IV2022★ 16 cited

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy

Michel Pohl, Mitsuru Uesaka, Kazuyuki Demachi +1

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of resp…

eess.IV2021★ 11 cited

Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy

Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi +2

During lung radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tumor location. However, radiotherapy systems have a latency inhe…