Automatic couch position calculation using eclipse scripting for external beam radiotherapy

Abstract

Purpose The treatment couch position of a patient in external beam radiation therapy (EBRT) is usually acquired during initial treatment setup. This procedure has shown potential failure modes leading to near misses and adverse events in radiation treatment. This study aims to develop a method to automatically determine the couch position before setting up a patient for initial treatment. Methods The Qfix couch‐tops (kVue and DoseMax) have embedded reference marks (BBs) indicating its index levels and couch centerline. With the ESAPI, a C# script was programmed to automatically find the couch‐top and embedded BBs in the planning CT and derive the treatment couch position according to treatment isocenter of a plan. Couch positions of EBRT plans with the kVue couch‐top and SBRT plans using the DoseMax were calculated using the script. The calculation was evaluated by comparing calculated positions with couch coordinates captured during the initial treatment setup after image guidance. The calculations were further compared with daily treatment couch positions post image‐guided adjustment for each treatment fraction. Results For plans using the kVue couch‐top for various treatment sites, the median (5–95 percentiles) differences between calculated and captured couch positions were 0.1 (−0.2 – 0.9), 0.5 (−1.1–2.0), 0.10 (−1.3–1.3) cm in the vertical, longitudinal, and lateral direction respectively. For the DoseMax couch‐top, the median differences were 0.1 (−0.2–0.7), 0.2 (−0.3–1.1), and 0.2 (−0.7–0.9) cm in respective direction. The calculated positions were within 1 and 2 cm from the mean fraction positions for 95% patients on DoseMax and kVue couch‐top respectively. Conclusions A method that automatically and accurately calculates treatment couch position from simulation CT was implemented in Varian Eclipse for Qfix couch‐tops. This technique increases the efficiency of patient setup and enhances patient safety by reducing the risks of positioning errors.

Machine learning-based treatment couch parameter prediction in support of surface guided radiation therapy

“…However, both methods involve manual selection of a point on CT images during treatment planning, which is potentially subjected to user errors. Recently, an automated solution is developed to determine the treatment couch position by computerized detection of the embedded BBs and index levels on the couch from CT images [14] . Despite the automatic character, the fixed threshold to detect markers is a potential pitfall due to reduced CT contrast resulting from the partial volume effect in a voxel.…”