Respiratory-motion prediction, Adaptive linear regression, Gating signals, Patient-specific, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

Abstract Background This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer. Materials and methods We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals. Results In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P