Improving Reproducibility and Efficiency of Interstitial HDR Brachytherapy Through MRI-based Targeted Innovations

Abstract

<p>Interstitial high dose rate (HDR) brachytherapy is a well-established treatment for prostate and gynecological cancers. HDR brachytherapy involves delivery of a high dose of radiation to the target with minimum dose to the surrounding tissue. Magnetic resonance imaging (MRI) has been explored as a main imaging modality for accurate target identification. Superior soft tissue resolution has been reported with MRI, increasing the accuracy of brachytherapy procedure. Clinical HDR brachytherapy workflows employing MRI are seeing increases in adoption. However, these require additional resources and patient transfers, which increases the planning time and the risk of catheters displacement. In addition, interstitial catheter reconstruction during interstitial brachytherapy procedure is challenging. Thus, accurate and efficient methods of incorporating the MRI imaging in interstitial HDR brachytherapy procedures need to be considered. </p> <p>In this thesis, a robust MRI-based algorithm and workflow were developed for treatment planning of interstitial HDR brachytherapy for prostate and gynecological cancers. An in-house MRI-to Ultrasound image registration algorithm was developed and evaluated for accurate identification of dominant intraprostatic lesions. The dosimetric impact of the registration was also assessed. Furthermore, an in-house MR-line marker was used in evaluating the feasibility of MR-only interstitial gynecological HDR brachytherapy workflow. The impact of the MR-only workflow on the target coverage and normal tissue sparing was evaluated. The dosimetric difference between the conventional approach and the proposed MR-only approaches was also compared and reported. Finally, a deep learning-assisted algorithm was developed for robust catheter reconstruction. The algorithm was benchmarked against the manual reconstruction and their efficiency was evaluated. </p> <p>Based on the results, the MRI-based innovations developed in this work are feasible for implementation in clinical brachytherapy workflows for treatment planning of prostate and gynecological cancers.</p>