Application of motion prediction based on a long short-term memory network for imaging dose reduction in real-time tumor-tracking radiation therapy.

Purpose: To demonstrate the possibility of using a lower imaging rate while maintaining acceptable accuracy by applying motion prediction to minimize the imaging dose in real-time image-guided radiation therapy.

Methods: Time-series of three-dimensional internal marker positions obtained from 98 patients in liver stereotactic body radiation therapy were used to train and test the long-short-term memory (LSTM) network. For real-time imaging, the root mean squared error (RMSE) of the prediction on three-dimensional marker position made by LSTM, the residual motion of the target under respiratory-gated irradiation, and irradiation efficiency were evaluated.

Dynamic gating window technique for the reduction of dosimetric error in respiratory-gated spot-scanning particle therapy: An initial phantom study using patient tumor trajectory data.

Spot-scanning particle therapy possesses advantages, such as high conformity to the target and efficient energy utilization compared with those of the passive scattering irradiation technique. However, this irradiation technique is sensitive to target motion. In the current clinical situation, some motion management techniques, such as respiratory-gated irradiation, which uses an external or internal surrogate, have been clinically applied.