An Optimization-based Approach to Dosimetry Planning for Brachytherapy

Prostate cancer is the second leading cause of death from cancer in North American men, with a reported 32,050 deaths in the U.S. alone for 2010; lung cancer is reported as the number one leading cause of death from cancer in both men and women in North America, its estimated death toll in the U.S. alone in 2010 is over 157,000. One method of treating prostate cancer patients nowadays is by Low Dose Rate Brachytherapy, a process where radioactive seeds are placed in or near the tumor site to kill cancerous cells. For lung cancer, brachytherapy has begun to attract attention due to the advent of robotics assistance and there is increasing research currently in the area. While brachytherapy is gaining popularity as a commonly practiced method for treating cancer patients, the procedure itself has several drawbacks that require further research. One such drawback is that the dosimetry plan created based on the pre-operative imaging may not be accurate due to (a) the change in the tumor’s size as a result of the time elapsed between pre-operative imaging and seed implantation; and (b) movement of the organ under treatment from the position and orientation in pre­ operative imaging; this is particularly important in the case of lung brachytherapy as it would have to take into account lung deflation and respiratory and cardiac motions as well. In addition, seeds may be misplaced during implantation as a result of limitation of the manual or robotic procedures. When this happens, the final dose coverage of the tumor is no longer the same as the intended coverage in the dosimetry plan. In this thesis, the development, implementation and evaluation of two algorithms are presented.The first algorithm is the pre-planning algorithm, which aims to reduce the errors in the dosimetry plan caused by the change in the tumor’s size by providing a mechanism to perform dosimetry planning on-line. By doing this, the first algorithm can also eliminate the need for the patient to be imaged twice, so that the same set of images can be used for dosimetry planning as well as seed implantation. The second algorithm deals with intra-operative dynamic dose optimization, where real­ time seed compensation is performed to compensate for any seed misplacements so that an optimal final coverage can be achieved. The results of the experimental evaluation performed in this project indicate that these algorithms are feasible and have the potential to be applied in the operating room following appropriate animal and clinical validation