• Progress in Medical Physics
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• v.9 no.3
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• pp.175-184
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• 1998

The dose distribution evaluation program for the stereotactic radiosurgery treatment planning system using a gamma knife has been built in order to work on PC. And this custom-made dose distribution is compared with that of commercial treatment planning program. 201 source position of a radiation unit were determined manually using a gamma knife collimator draft and geometrical coordinates. Dose evaluation algorithm was modified for our purpose from the original KULA, a commercial treatment planning program. With the composed program, dose distribution at the center of a spherical phantom, 80 mm in diameter, was evaluated into axial, coronal and sagittal image per each collimator. Along with this evaluated data, the dose distribution at a arbitrary point of inside the phantom was compared with those from KULA. Radiochromic film was set up at the center of the phantom and was irradiated by gamma knife, for the verification of dose distribution. In result, the deviation of the dose distribution from that of KULA is less than ${\pm}$3%, which is equivalent to ${\pm}$0.3 mm in 50% isodose distribution for all examined coordinates and film verification. The custom-made program, GPl is proven to be a good tool for the stereotactic radiosurgery treatment planning program.

• Journal of Radiation Protection and Research
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• v.26 no.2
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• pp.59-66
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• 2001

It is a hot issue to determine the spatial location and shape of tumor boundary in fractionated stereotactic radiotherapy (FSRT). We could get consecutive transaxial plane images from the phantom (paraffin) and 4 patients with brain tumor using helical computed tomography(HCT). K-means classification algorithm was adjusted to change raw data pixel value in CT images into classified average pixel value. The classified images consists of 5 regions that ate tumor region (TR), normal region (NR), combination region (CR), uncommitted region (UR) and artifact region (AR). The major concern was how to separate the normal region from tumor region in the combination area. Relative average deviation analysis was adjusted to alter average pixel values of 5 regions into 2 regions of normal and tumor region to define maximum point among average deviation pixel values. And then we drawn gross tumor volume (GTV) boundary by connecting maximum points in images using semi-automatic contour method by IDL(Interactive Data Language) program. The error limit of the ROI boundary in homogeneous phantom is estimated within ${\pm}1%$. In case of 4 patients, we could confirm that the tumor lesions described by physician and the lesions described automatically by the K-mean classification algorithm and relative average deviation analyses were similar. These methods can make uncertain boundary between normal and tumor region into clear boundary. Therefore it will be useful in the CT images-based treatment planning especially to use above procedure apply prescribed method when CT images intermittently fail to visualize tumor volume comparing to MRI images.