Abstract
Purpose: Stereotactic radiosurgery is currently used to treat patients who are not good candidates for conventional neurosurgical procedures. For treatments of nonvascular tumor cells, it appears that fractionation offers a radiobiological advantage between tumor and normal tissues. Therefore, fractionated stereotactic radiotherapy (FSR) is preferred because it minimizes normal tissue complications and maximizes local tumor control probability. We have implemented a methodology clinically to perform the noninvasive patient repositioning technique. The 3-D data correlation method for high-precision and multiple fraction stereotactic treatments has been presented.
Methods and Materials: Three different optimization algorithms (Hooke and Jeeves optimization, simplex optimization, and simulated annealing optimization) are evaluated to calculate the transformation parameters necessary for FSR. A least-square object function is created to perform the 3-D data matching process. By minimizing the unconstrained object function value the best fit can be approached for the reference 3-D data sets. Simulation shows that these algorithms deliver results that are comparable to the previously published correlation algorithm
(1, 2) (singular value decomposition [SVD] method). The advantage for optimization algorithms is easily understood and can be readily implemented by using a personal computer (PC). The mathematical framework provides a tool to calculate the transformation matrix which can be used to adjust patient position for fractionated treatments. Therefore, using these algorithms for a high-precision fractionated treatment is possible without an invasive repeat fixation device and has been implemented clinically. A bite plate system was incorporated to acquire 3-D patient data. With a 3-D digital camera localization device, the patient motion can be followed in real time with the system calibrated to the isocenter.
Results: Two types of data sets are utilized to study the correlation results. One is using the digitized patient data which were retrieved clinically. The other is using the randomly generated data sets. Simulation errors for the optimization algorithms are all less than 1 mm in translation and less than 1° in rotation. Currently, FSR is performed using special designed repeat fixation devices which assure reproducible patient position for multiple fractions of radiation treatment. Clinical results indicated that this technique provided excellent treatment results.
Conclusion: Three optimization algorithms have been applied and evaluated in calculating the transformation parameters between two 3-D contours or digitized data points. The mathematical functions behind these optimization algorithms are straightforward and can be easily implemented. When incorporated with the proper CT/MR image data with an electronic portal imaging (EPI) system, this process can possibly verify the patient’s treatment position whenever there is doubt about the movement during the treatment procedure.
