Predicting respiratory motion for real-time tumour tracking in radiotherapy

TL;DR

This paper presents ExSmi, a real-time respiratory motion prediction algorithm using exponential smoothing, which improves tumor targeting accuracy in radiotherapy by predicting lung tumor motion with minimal calibration.

Contribution

The study introduces ExSmi, a novel exponential smoothing-based algorithm for real-time respiratory motion prediction with low calibration time and validated on clinical datasets.

Findings

Achieves prediction errors of 4-9 mm/s

Maintains jitter within 5-7 mm/s

Validated on clinical datasets with different respiratory behaviors

Abstract

Purpose. Radiation therapy is a local treatment aimed at cells in and around a tumor. The goal of this study is to develop an algorithmic solution for predicting the position of a target in 3D in real time, aiming for the short fixed calibration time for each patient at the beginning of the procedure. Accurate predictions of lung tumor motion are expected to improve the precision of radiation treatment by controlling the position of a couch or a beam in order to compensate for respiratory motion during radiation treatment. Methods. For developing the algorithmic solution, data mining techniques are used. A model form from the family of exponential smoothing is assumed, and the model parameters are fitted by minimizing the absolute disposition error, and the fluctuations of the prediction signal (jitter). The predictive performance is evaluated retrospectively on clinical datasets capturing different behavior (being quiet, talking, laughing), and validated in real-time on a prototype system with respiratory motion imitation. Results. An algorithmic solution for respiratory motion prediction (called ExSmi) is designed. ExSmi achieves good accuracy of prediction (error $4-9$ mm/s) with acceptable jitter values (5-7 mm/s), as tested on out-of-sample data. The datasets, the code for algorithms and the experiments are openly available for research purposes on a dedicated website. Conclusions. The developed algorithmic solution performs well to be prototyped and deployed in applications of radiotherapy.