Permanent implantation of low-dose-rate (LDR) brachytherapy seeds is a well-established treatment modality for patients with localized prostate cancer. The quality of the implant is assessed within 30 days following implantation through post-implant dosimetry. The standard recommended procedure for post-implant dosimetry is based on computed tomography (CT). CT provides excellent seed visualization and localization; however, due to poor soft tissue contrast and challenging anatomical identificatio,n it leads to significant interobserver variabilities. The current MRI-CT fusion-based workflow for post-implant dosimetry LDR prostate brachytherapy takes advantage of the superior soft tissue contrast of MRI but still relies on CT for seed visualization and detection, and it suffers from image fusion uncertainties and extra cost and logistics. The lack of positive contrast from brachytherapy seeds in conventional MR images remains a major challenge towards an MRI-only workflow for post-implant dosimetry of Low- Dose-Rate (LDR) brachytherapy.
In this thesis, a clinically feasible MRI-based workflow has been developed for brachytherapy seed visualization and localization. The seed visualization is based on a novel Quantitative Susceptibility Mapping (QSM) algorithm. The proposed seed localization on QSM utilizes machine learning algorithms. The reliability of the proposed workflow has been validated on 23 patients by comparing the seed positions and final dosimetric parameters between the proposed MRI-only workflow and the clinical CT-MRI fusion-based approach and there was excellent agreement between the two methods.