[Development of an automated method for analysis of Winston-Lutz test results using digital radiography and photostimulable storage phosphor].

Stereotactic radiosurgery (SRS) and radiotherapy (SRT) are intricate techniques that deliver a highly precise radiation dose to a localized target, usually a tumor. At our hospital, we perform SRS and SRT on brain tumors using a linear accelerator (linac) mounted with an external micro multi-leaf system. The Task Group TG-142 Report by the American Association of Physicists in Medicine recommends the coincidence of the radiation and mechanical isocenter to be within ±1 mm. The Winston-Lutz test is commonly used to verify the linac isocenter position: it has the advantages of being a simple method that uses a film or electronic portal imaging device (EPID). However, the film method requires a higher radiation dose, which makes it more time-consuming than the EPID method, and the results are highly dependent on the skills of the observer. The EPID method has certain advantages over the film method, but it has low resolution and can only be used for a few combinations of gantry and couch angles. This prompted us to develop an in-house-designed radiation receptor system based on digital radiography, using a photostimulable storage phosphor and automated analysis algorithm for Winston-Lutz test images using a template-matching technique based on cross-correlation coefficients. Our proposed method shows a maximum average absolute error of 0.222 mm (less than 2 pixels) for 0.5 mm and 1.0 mm displacement from the isocenter toward the inline and crossline directions. Our proposed method is thus potentially useful for verifying the Linac isocenter position with a small error and good reproducibility, as demonstrated by improved accuracy of evaluation.

Automated estimation of number of implanted iodine-125 seeds for prostate brachytherapy based on two-view analysis of pelvic radiographs.

Digital pelvic radiographs are used to identify the locations of implanted iodine-125 seeds and their numbers after insertion. However, it is difficult and laborious to visually identify and count all implanted seeds on the pelvic radiographs within a short time. Therefore, our purpose in this research was to develop an automated method for estimation of the number of implanted seeds based on two-view analysis of pelvic radiographs. First, the images of the seed candidates on the pelvic image were enhanced using a difference of Gaussian filter, and were identified by binarizing the enhanced image with a threshold value determined by multiple-gray level thresholding. Second, a simple rule-base method using ten image features was applied for false positive removal. Third, the candidates for the likely number of a multiply overlapping seed region, which may include one or more seeds, were estimated by a seed area histogram analysis and calculation of the probability of the likely number of overlapping seeds. As a result, the proposed method detected 99.9% of implanted seeds with 0.71 false positives per image on average in a test for training cases, and 99.2% with 0.32 false positives in a validation test. Moreover, the number of implanted seeds was estimated correctly at an overall recognition rate of 100% in the validation test using the proposed method. Therefore, the verification time for the number of implanted seeds could be reduced by the provision of several candidates for the likely number of seeds.