Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug;24(8):e13990.
doi: 10.1002/acm2.13990. Epub 2023 Apr 8.

IMRT QA result prediction via MLC transmission decomposition

John T Stasko  1 , William S Ferris  1 , David P Adam  1 , Wesley S Culberson  1 , Sean P Frigo  1   2
Affiliations

IMRT QA result prediction via MLC transmission decomposition

John T Stasko et al. J Appl Clin Med Phys. 2023 Aug.

Abstract

Background: Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable.

Purpose: We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics.

Methods: The tool was created in Python to read in DICOM plan files and determine the beam fluence fraction incident on each of seven different zones, each classified based on the RayStation MLC model. The fractions, termed grid point fractions, were validated by analyzing simple test plans. The average grid point fractions, over all control points for 46 plans were then computed. These values were then compared with gamma analysis pass percentages and median dose differences to determine if any significant correlations existed.

Results: Significant correlation was found between the grid point fraction metrics and median dose differences, but not with gamma analysis pass percentages. Correlations were positive or negative, suggesting differing model parameter value sensitivities, as well as potential insight into the treatment planning system dose model.

Conclusions: By decomposing MLC control points into different transmission zones, it is possible to create a metric that predicts whether the analyzed plan will pass a quality assurance measurement from a dose calculation accuracy standpoint. The tool and metrics developed in this work have potential applications in comparing clinical beam models or identifying their weak points. Implementing the tool within a treatment planning system would also provide more potential plan optimization parameters.

Keywords: measurement; multi leaf collimator; quality assurance; transmission.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
(a) Classification scheme applied to a control point from a clinical plan. (b) Close‐up providing zone detail.
FIGURE 2
FIGURE 2
Test plan classification grid for one static beam. All leaves are positioned along the center of the field except for the 20 innermost leaf pairs, which are retracted to create a 10 × 10 cm2 zone of open field.
FIGURE 3
FIGURE 3
Average grid point fractions for all plans. The solid black lines indicate the mean of the values. The points for a given plan add to 1.0.
FIGURE 4
FIGURE 4
Gamma analysis pass percentages versus (a) average leaf tip grid point fractions and (b) average leaf body grid point fractions.
FIGURE 5
FIGURE 5
Median dose difference versus (a) average tip grid point fractions and (b) plan MU‐weighted average body grid point fractions.
FIGURE 6
FIGURE 6
Median dose difference versus (a) plan MU‐weighted average tip grid point fractions and (b) plan MU‐weighted average body grid point fractions, both normalized by the open zone grid point fractions.
See this image and copyright information in PMC

References

    1. Kry SF, Glenn MC, Peterson CB, et al. Independent recalculation outperforms traditional measurement‐based IMRT QA methods in detecting unacceptable plans. Med Phys. 2019;46(8):3700‐3708. doi:10.1002/mp.13638 - DOI - PMC - PubMed
    1. Chiavassa S, Bessieres I, Edouard M, Mathot M, Moignier A. Complexity metrics for IMRT and VMAT plans: a review of current literature and applications. Br J Radiol. 2019;92(1102):20190270. doi:10.1259/bjr.20190270 - DOI - PMC - PubMed
    1. Valdes G, Chan MF, Lim SB, Scheuermann R, Deasy JO, Solberg TD. IMRT QA using machine learning: a multi‐institutional validation. J Appl Clin Med Phys. 2017;18(5):279‐284. doi:10.1002/acm2.12161 - DOI - PMC - PubMed
    1. Interian Y, Rideout V, Kearney VP, et al. Deep nets vs expert designed features in medical physics: an IMRT QA case study. Med Phys. 2018;45(6):2672‐2680. doi:10.1002/mp.12890 - DOI - PubMed
    1. Lam D, Zhang X, Li H, et al. Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning. Med Phys. 2019;46(10):4666‐4675. doi:10.1002/mp.13752 - DOI - PubMed