Publications by authors named "Shahyar Saramad"
Background: Respiratory motion is a challenge for accurate radiotherapy that may be mitigated by real-time tracking. Commercial tracking systems utilize a hybrid external-internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X-ray imaging of the internal tumor position.
Purpose: This study investigates the feasibility of using the next generation reservoir computing (NG-RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions.
Article Synopsis
- The study looks at how to estimate the movement of liver tumors during radiation therapy by tracking both external breathing motion and sparse internal imaging.
- It tested four different models to see how accurately they could predict internal tumor motion based on external movements, measuring their performance with root-mean-square error (RMSE).
- The results showed that while the augmented quadratic model (ECM3) had the best overall fitting accuracy, the simpler augmented linear model (ECM2) with frequent updates was more effective for estimating motion accurately, especially in a specific directional movement.
Purpose: This study investigates a new approach for estimating the planning target volume (PTV) margin for moving tumors treated with robotic stereotactic body radiotherapy (SBRT).
Methods: In this new approach, the covariance of modeling and prediction errors was estimated using error propagation and implemented in the Van Herk formula to form a Modified Van Herk formula (MVHF). To perform a retrospective multi-center analysis, the MVHF was studied using 163 patients treated with different system versions of robotic SBRT (G3 version 6.
Background: In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural networks to propose an accurate and reliable prediction model.
Methods: Seven deep predictor models are trained and tested with 800 breathing signals.
Purpose: Calculating the adequate target margin for real-time tumor tracking using the Cyberknife system is a challenging issue since different sources of error exist. In this study, the clinical log data of the Cyberknife system were analyzed to adequately quantify the planned target volume (PTV) margins of tumors located in the lung and abdomen regions.
Methods: In this study, 45 patients treated with the Cyberknife module were examined.