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Automatic setup deviation measurements with electronic portal images for pelvic fields
Author(s) -
Girouard L. M.,
Pouliot J.,
Maldague X.,
Zaccarin A.
Publication year - 1998
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598296
Subject(s) - standard deviation , imaging phantom , fiducial marker , nuclear medicine , image guided radiation therapy , artificial intelligence , computer science , medical imaging , mathematics , medicine , statistics
The purpose of this work was to develop a fully automatic tool for the detection of setup deviation for small pelvic fields using, in external beam radiotherapy, an electronic portal imaging device (EPID). The algorithm processes electronic portal images of prostate cancer patients. No fiducial points or user interventions are needed. Deviation measurements are based on bone edge detection performed with the Laplacian of a Gaussian (LoG) operator. Two bone edge images are then correlated, one of which is a reference image taken as the first fraction image for the purpose of this study. The electronic portal images (EPI) also show band artefacts which are removed using the morphological top‐hat transform. The algorithm was first validated with 59 phantom images acquired in clinical treatment conditions with known displacements. The algorithm was then validated with 79 clinical images where bone contours were delineated manually. For the phantom images, the setup deviations were measured with an absolute mean error of 0.59 mm and 0.47 mm with a standard deviation of 0.64 mm and 0.42 mm, horizontally and vertically, respectively. A second validation was performed using clinical prostate cancer images. The measured patient displacements have an absolute mean error of 0.48 mm and 1.41 mm with a standard deviation of 0.58 mm and 1.30 mm in the X and Y directions, respectively. The algorithm execution time on a SUN workstation is 5 s. This algorithm shows good potential as a setup deviation measurement tool in clinical practice. The possibility of using this algorithm combined with decision rules based on statistical observations is very promising.

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