Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

2019 ◽  
Vol 46 (10) ◽  
pp. 4666-4675 ◽  
Author(s):  
Dao Lam ◽  
Xizhe Zhang ◽  
Harold Li ◽  
Yang Deshan ◽  
Brayden Schott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imrt Qa ◽  
Passing Rates ◽  
Portal Dosimetry

Patient-specific IMRT QA verification using machine learning and gamma radiomics

2021 ◽  
Vol 82 ◽  
pp. 100-108
Author(s):  
Jéssica Caroline Lizar ◽  
Carolina Cariolatto Yaly ◽  
Alexandre Colello Bruno ◽  
Gustavo Arruda Viani ◽  
Juliana Fernandes Pavoni
Keyword(s):  
Machine Learning ◽  
Patient Specific ◽  
Imrt Qa

SU-FF-T-232: IMRT QA Using a Hybrid Mapcheck / Electronic Portal Dosimetry Environment

2009 ◽  
Vol 36 (6Part12) ◽  
pp. 2574-2574 ◽  
Author(s):  
H Malhotra ◽  
M Bakhtiari ◽  
L Kumaraswamy ◽  
D Bailey ◽  
J Liu ◽  
...  
Keyword(s):  
Imrt Qa ◽  
Portal Dosimetry

MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation

2016 ◽  
Vol 43 (6Part31) ◽  
pp. 3714-3714 ◽  
Author(s):  
G Valdes ◽  
M Chan ◽  
R Scheuermann ◽  
J Deasy ◽  
T Solberg
Keyword(s):  
Machine Learning ◽  
Imrt Qa

SU-E-T-123: Understanding the Meaning of IMRT QA Passing Rates with a 2D Diode Array

2012 ◽  
Vol 39 (6Part11) ◽  
pp. 3731-3731
Author(s):  
A Perez-Andujar ◽  
O Morin ◽  
C Chuang ◽  
J Pouliot
Keyword(s):  
Diode Array ◽  
Imrt Qa ◽  
Passing Rates

Patient-specific quality assurance measurements for VMAT treatments: Do we really catch errors?

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 81-81 ◽  
Author(s):  
Wolfram Laub ◽  
Charles R. Thomas
Keyword(s):  
Quality Assurance ◽  
Head And Neck ◽  
Treatment Plan ◽  
Patient Specific ◽  
Imrt Qa ◽  
Passing Rates ◽  
Passing Rate ◽  
Gamma Index ◽  
Clinically Significant ◽  
Alignment Errors

81 Background: Patient-specific quality assurance measurements are time consuming and Gamma pass/fail criteria are often picked based on typical criteria used for IMRT QA measurements in the past. The questions needs to be asked if with these criteria QA plans could still show clinically significant deviations from the treatment plan calculated and how we should be doing QA for treatment delivery of complex treatment plans. In our study DICOM files of clinical Rapidarc plans were modified with in-house developed software to mimic leaf alignment errors and gravitation shifts. The Octavius 2D-ARRAY (PTW-Freiburg) and the Delta4 device (Scandidos) were used to investigate the effect of the simulated errors on the passing rate of quality assurance results. The manipulated Rapidarc plans were recalculated on patient CT scans in Eclipse. Methods: Three different types of errors were simulated and applied to five prostate (two arcs), three 2-arc head and neck cases and three 3-arc head and neck cases. The MLC modifications were: (1) both MLC banks are opened by 0.25mm, 0.50mm and 1.00mm in opposing directions resulting in larger fields, (2) both MLC banks are closed by 0.10mm, 0.25mm and 0.50mm, (3) both MLC banks are shifted in the same direction for lateral gantry angles to simulate effects of gravitational forces onto the leaves by 1mm, 2mm and 3mm, (4) 25%, 50% 70% and 100% of all active leaves are shifted by 3mm as in (3). QA evaluations were performed according to a gamma-index criterion of 3mm and 3% as well as 2mm and 2%. Results: All unmodified plans and the majority of the plans with the smallest modification pass the gamma-index criterion of 2%/2mm with >90%. After that the passing rate drops below 90%. For the largest modifications passing rates were typically below 85%. The Delta4 is generally more sensitive and the passing rate for modified plans drops below 90% earlier and more drastically. With the standard criteria (3mm, 3%) even the largest modifications would satisfy a >90% passing rate. Conclusions: A stricter gamma-index (2mm, 2%) is necessary in order to detect MLC positional errors and a passing rate of >90% should be expected. Clinical pass/fail criteria need to be developed.

scholarly journals IMRT QA using machine learning: A multi‐institutional validation

2017 ◽  
Vol 18 (5) ◽  
pp. 279-284 ◽  
Author(s):  
Gilmer Valdes ◽  
Maria F. Chan ◽  
Seng Boh Lim ◽  
Ryan Scheuermann ◽  
Joseph O. Deasy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imrt Qa

SU-E-T-185: Passing Rates of 83 Patient-Specific IMRT QA Measurements with ArcCHECK Using Various Gamma Criteria

2011 ◽  
Vol 38 (6Part13) ◽  
pp. 3528-3529
Author(s):  
B Sintay ◽  
T Ramer ◽  
D Wiant
Keyword(s):  
Patient Specific ◽  
Imrt Qa ◽  
Passing Rates

scholarly journals Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

2011 ◽  
Vol 38 (2) ◽  
pp. 1037-1044 ◽  
Author(s):  
Benjamin E. Nelms ◽  
Heming Zhen ◽  
Wolfgang A. Tomé
Keyword(s):  
Patient Dose ◽  
Imrt Qa ◽  
Passing Rates ◽  
Dose Errors

A Clinical Objective IMRT QA Method Based on Portal Dosimetry and Electronic Portal Imager Device (EPID) Measurement

2013 ◽  
Vol 12 (2) ◽  
pp. 145-150
Author(s):  
Miao Zhang ◽  
Songbing Qin ◽  
Ting Chen ◽  
Sung Kim ◽  
Salma Jabbour ◽  
...  
Keyword(s):  
Imrt Qa ◽  
Portal Dosimetry
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