Prediction-based compensation for gate on/off latency during respiratory-gated radiotherapy

TL;DR

This paper introduces a prediction-based framework to compensate for gate latency in respiratory-gated radiotherapy, aiming to improve dose accuracy during treatment.

Contribution

It proposes a novel two-step prediction and gating framework specifically designed to address latency issues in RGRT systems.

Findings

System showed superior performance on most respiratory signals

Some signals with irregularity or baseline drift posed challenges

Potential for improved dose delivery accuracy in RGRT

Abstract

During respiratory-gated radiotherapy (RGRT), gate on and off latencies cause deviations of gating windows, possibly leading to delivery of low- and high-dose radiations to tumors and normal tissues, respectively. Currently, there are no RGRT systems that have definite tools to compensate for the delays. To address the problem, we propose a framework consisting of two steps: 1) multi-step-ahead prediction and 2) prediction-based gating. For each step, we have devised a specific algorithm to accomplish the task. Numerical experiments were performed using respiratory signals of a phantom and ten volunteers, and our prediction-based RGRT system exhibited superior performance in more than a few signal samples. In some, however, signal prediction and prediction-based gating did not work well, maybe due to signal irregularity and/or baseline drift. The proposed approach has potential applicability in RGRT, and further studies are needed to verify and refine the constituent algorithms.