Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory
motion into treatments. To precisely account for the patient's respiratory motions and predict …

Respiration-induced organ motion is one of the major uncertainties in lung cancer
radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far …

PURPOSE: We report on an integrated system for real-time adaptive radiation delivery to
moving tumors. The system combines two promising technologies—three-dimensional …

The increasing interest in combined positron emission tomography (PET) and computed
tomography (CT) to guide lung cancer radiation therapy planning has been well …

Effective delivery of adaptive radiotherapy requires locating the target with high precision in
real time. System latency caused by data acquisition, streaming, processing and delivery …

Background The dynamic tracking of tumors with radiation beams in radiation therapy
requires the prediction of real-time target locations prior to beam delivery, as treatment …

Tumor motion caused by patient breathing creates challenges for accurate radiation dose
delivery to a tumor while sparing healthy tissues. Image-guided radiation therapy (IGRT) …

Purpose To develop a deep learning network that treats the three‐dimensional respiratory
motion signals as a whole and considers the inter‐dimensional correlation between signals …

For accurate respiration gated radiation therapy, compensation for the beam latency of the
beam control system is necessary. Therefore, we evaluate deep learning models for …

Purpose. To improve the prediction accuracy of respiratory signals by adapting the multi-
layer perceptron neural network (MLP-NN) model to changing respiratory signals. We have …