ArticleJournal of applied clinical medical physics2022
Measurement of the temporal latency of a respiratory gating system using two distinct approaches.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Log file-based quality assurance method for respiratory gating system.Journal of applied clinical medical physics · 2025Article
- Efficient EPID-based quality assurance of beam time delay for respiratory-gated radiotherapy with validation on Catalyst™ and AlignRT™ systems.Journal of applied clinical medical physics · 2024Article
- Measurement of the temporal latency of a respiratory gating system using two distinct approaches.Journal of applied clinical medical physics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a methodology that can be used to measure the temporal latency of a respiratory gating system.
methodsThe gating system was composed of an automatic gating interface (Response) and an in-house respiratory motion monitoring system featuring an optically tracked surface marker. Two approaches were used to measure gating latencies. A modular approach involved measuring separately the latency of the gating system's complementary metal-oxide-semiconductor tracking camera, tracking software, and a gating latency of the LINAC. Additionally, an end-to-end approach was used to measure the total latency of the gating system. End-to-end latencies were measured using the displacement of a radiographic target moving at known velocities during the gating process.
resultsSumming together the latencies of each of the modular components investigated yielded a total beam-on latency of 1.55 s and a total beam-off latency of 0.49 s. End-to-end beam-on and beam-off latency was found to be 1.49 and 0.34 s, respectively. In each case, no statistically significant differences were found between the end-to-end latency of the gating system and the summation of the individually measured components.
conclusionTwo distinct approaches to quantify gating latencies were presented. Measuring the end-to-end latency of the gating system provided an independent means of validating the modular approach. It is expected that the beam-on latencies reported in this work could be reduced by altering the control system configuration of the LINAC. The modular approach can be used to decouple the individual latencies of the gating system, but future improvements in the temporal resolution of the service graphing feature are needed to reduce the uncertainty of LINAC-related gating latencies measured using this approach. Both approaches are generalizable and can be used together when designing a quality assurance program for a respiratory gating system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.