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Int J Radiat Oncol Biol Phys. 2013 Oct 1;87(2):390-3. doi: 10.1016/j.ijrobp.2013.05.021. Epub 2013 Jul 9.

Automated patient identification and localization error detection using 2-dimensional to 3-dimensional registration of kilovoltage x-ray setup images.

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Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA.



To determine whether kilovoltage x-ray projection radiation therapy setup images could be used to perform patient identification and detect gross errors in patient setup using a computer algorithm.


Three patient cohorts treated using a commercially available image guided radiation therapy (IGRT) system that uses 2-dimensional to 3-dimensional (2D-3D) image registration were retrospectively analyzed: a group of 100 cranial radiation therapy patients, a group of 100 prostate cancer patients, and a group of 83 patients treated for spinal lesions. The setup images were acquired using fixed in-room kilovoltage imaging systems. In the prostate and cranial patient groups, localizations using image registration were performed between computed tomography (CT) simulation images from radiation therapy planning and setup x-ray images corresponding both to the same patient and to different patients. For the spinal patients, localizations were performed to the correct vertebral body, and to an adjacent vertebral body, using planning CTs and setup x-ray images from the same patient. An image similarity measure used by the IGRT system image registration algorithm was extracted from the IGRT system log files and evaluated as a discriminant for error detection.


A threshold value of the similarity measure could be chosen to separate correct and incorrect patient matches and correct and incorrect vertebral body localizations with excellent accuracy for these patient cohorts. A 10-fold cross-validation using linear discriminant analysis yielded misclassification probabilities of 0.000, 0.0045, and 0.014 for the cranial, prostate, and spinal cases, respectively.


An automated measure of the image similarity between x-ray setup images and corresponding planning CT images could be used to perform automated patient identification and detection of localization errors in radiation therapy treatments.

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