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Automated review of patient position in DIBH breast hybrid IMRT with EPID images
by
Rivest, Ryan
, Alpuche Aviles, Jorge E.
, Pistorius, Stephen
, Redekopp, Jonathan
, Sasaki, David
in
Algorithms
/ automated chest wall tracking
/ Automation
/ breast RT
/ DIBH
/ Dosimetry
/ EPID imaging
/ Heart
/ intrafraction motion
/ Lasers
/ Monitoring systems
/ Patients
/ Radiation Oncology Physics
/ Radiation therapy
/ setup error
2023
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Automated review of patient position in DIBH breast hybrid IMRT with EPID images
by
Rivest, Ryan
, Alpuche Aviles, Jorge E.
, Pistorius, Stephen
, Redekopp, Jonathan
, Sasaki, David
in
Algorithms
/ automated chest wall tracking
/ Automation
/ breast RT
/ DIBH
/ Dosimetry
/ EPID imaging
/ Heart
/ intrafraction motion
/ Lasers
/ Monitoring systems
/ Patients
/ Radiation Oncology Physics
/ Radiation therapy
/ setup error
2023
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Automated review of patient position in DIBH breast hybrid IMRT with EPID images
by
Rivest, Ryan
, Alpuche Aviles, Jorge E.
, Pistorius, Stephen
, Redekopp, Jonathan
, Sasaki, David
in
Algorithms
/ automated chest wall tracking
/ Automation
/ breast RT
/ DIBH
/ Dosimetry
/ EPID imaging
/ Heart
/ intrafraction motion
/ Lasers
/ Monitoring systems
/ Patients
/ Radiation Oncology Physics
/ Radiation therapy
/ setup error
2023
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Automated review of patient position in DIBH breast hybrid IMRT with EPID images
Journal Article
Automated review of patient position in DIBH breast hybrid IMRT with EPID images
2023
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Overview
Deep Inspiration Breath Hold (DIBH) is a respiratory‐gating technique adopted in radiation therapy to lower cardiac irradiation. When performing DIBH treatments, it is important to have a monitoring system to ensure the patient's breath hold level is stable and reproducible at each fraction. In this retrospective study, we developed a system capable of monitoring DIBH breast treatments by utilizing cine EPID images taken during treatment. Setup error and intrafraction motion were measured for all fractions of 20 left‐sided breast patients. All patients were treated with a hybrid static‐IMRT technique, with EPID images from the static fields analyzed. Ten patients had open static fields and the other ten patients had static fields partially blocked with the multileaf collimator (MLC). Three image‐processing algorithms were evaluated on their ability to accurately measure the chest wall position (CWP) in EPID images. CWP measurements were recorded along a 61‐pixel region of interest centered along the midline of the image. The median and standard deviation of the CWP were recorded for each image. The algorithm showing the highest agreement with manual measurements was then used to calculate intrafraction motion and setup error. To measure intrafraction motion, the median CWP of the first EPID frame was compared with that of the subsequent EPID images of the treatment. The maximum difference was recorded as the intrafraction motion. The setup error was calculated as the difference in median CWP between the MV DRR and the first EPID image of the lateral tangential field. The results showed that the most accurate image‐processing algorithm can identify the chest wall within 1.2 mm on both EPID and MV DRR images, and measures intrafraction motion and setup errors within 1.4 mm.
Publisher
John Wiley & Sons, Inc,John Wiley and Sons Inc
Subject
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