Encrypted login | home

Program Information

Computer Vision in Autonomous Quality Assurance of Linear Accelerators

no image available
H Yu

H Yu*, C Jenkins , S Yu , Y Yang , L Xing , Stanford University, Stanford, CA


TU-FG-201-4 (Tuesday, August 2, 2016) 1:45 PM - 3:45 PM Room: 201


Routine quality assurance (QA) of linear accelerators represents a critical and costly element of a radiation oncology center. Recently, a system was developed to autonomously perform routine quality assurance on linear accelerators. The purpose of this work is to extend this system and contribute computer vision techniques for obtaining quantitative measurements for a monthly multi-leaf collimator (MLC) QA test specified by TG-142, namely leaf position accuracy, and demonstrate extensibility for additional routines.


Grayscale images of a picket fence delivery on a radioluminescent phosphor coated phantom are captured using a CMOS camera. Collected images are processed to correct for camera distortions, rotation and alignment, reduce noise, and enhance contrast. The location of each MLC leaf is determined through logistic fitting and a priori modeling based on knowledge of the delivered beams. Using the data collected and the criteria from TG-142, a decision is made on whether or not the leaf position accuracy of the MLC passes or fails.


The locations of all MLC leaf edges are found for three different picket fence images in a picket fence routine to 0.1mm/1pixel precision. The program to correct for image alignment and determination of leaf positions requires a runtime of 21- 25 seconds for a single picket, and 44 - 46 seconds for a group of three pickets on a standard workstation CPU, 2.2 GHz Intel Core i7.


MLC leaf edges were successfully found using techniques in computer vision. With the addition of computer vision techniques to the previously described autonomous QA system, the system is able to quickly perform complete QA routines with minimal human contribution.

Contact Email: