SU-F-BRB-03: Quantifying Patient Motion During Deep-Inspiration Breath-Hold Using the ABC System with Simultaneous Surface Photogrammetry




Active breathing control (ABC) has been used to reduce treatment margin due to respiratory organ motion by enforcing temporary breath-holds. However, in practice, even if the ABC device indicates constant lung volume during breath-hold, the patient may still exhibit minor chest motion. Consequently, therapists are given a false sense of security that the patient is immobilized. This study aims at quantifying such motion during ABC breath-holds by monitoring the patient chest motion using a surface photogrammetry system, VisionRT.


A female patient with breast cancer was selected to evaluate chest motion during ABC breath-holds. During the entire course of treatment, the patient's chest surface was monitored by a surface photogrammetry system, VisionRT. Specifically, a user-defined region-of-interest (ROI) on the chest surface was selected for the system to track at a rate of ∼3Hz. The surface motion was estimated by rigid image registration between the current ROI image captured and a reference image. The translational and rotational displacements computed were saved in a log file.


A total of 20 fractions of radiation treatment were monitored by VisionRT. After removing noisy data, we obtained chest motion of 79 breath-hold sessions. Mean chest motion in AP direction during breath-holds is 1.31mm with 0.62mm standard deviation. Of the 79 sessions, the patient exhibited motion ranging from 0–1 mm (30 sessions), 1–2 mm (37 sessions), 2–3 mm (11 sessions) and >3 mm (1 session).


Contrary to popular assumptions, the patient is not completely still during ABC breath-hold sessions. In this particular case studied, the patient exhibited chest motion over 2mm in 14 out of 79 breath-holds. Underestimating treatment margin for radiation therapy with ABC could reduce treatment effectiveness due to geometric miss or overdose of critical organs.

The senior author receives research funding from NIH, VisionRT, Varian Medical Systems and Elekta