Overlapped seed localization in seed implant brachytherapy

A review of brachytherapy physical phantoms developed over the last 20 years: clinical purpose and future requirements

Within the brachytherapy community, many phantoms are constructed in-house, and less commercial development is observed as compared to the field of external beam. Computational or virtual phantom design has seen considerable growth; however, physical phantoms are beneficial for brachytherapy, in which quality is dependent on physical processes, such as accuracy of source placement. Focusing on the design of physical phantoms, this review paper presents a summary of brachytherapy specific phantoms in published journal articles over the last twenty years (January 1, 2000 - December 31, 2019). The papers were analyzed and tabulated by their primary clinical purpose, which was deduced from their associated publications. A substantial body of work has been published on phantom designs from the brachytherapy community, but a standardized method of reporting technical aspects of the phantoms is lacking. In-house phantom development demonstrates an increasing interest in magnetic resonance (MR) tissue mimicking materials, which is not yet reflected in commercial phantoms available for brachytherapy. The evaluation of phantom design provides insight into the way, in which brachytherapy practice has changed over time, and demonstrates the customised and broad nature of treatments offered.

REDMAPS: reduced-dimensionality matching for prostate brachytherapy seed reconstruction

The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland. Intraoperative localization of the implanted seeds provides potential for dose evaluation and optimization during therapy. A reduced-dimensionality matching algorithm for prostate brachytherapy seed reconstruction (REDMAPS) that uses multiple X-ray fluoroscopy images obtained from different poses is proposed. The seed reconstruction problem is formulated as a combinatorial optimization problem, and REDMAPS finds a solution in a clinically acceptable amount of time using dimensionality reduction to create a smaller space of possible solutions. Dimensionality reduction is possible since the optimal solution has approximately zero cost when the poses of the acquired images are known to be within a small error. REDMAPS is also formulated to address the "hidden seed problem" in which seeds overlap on one or more observed images. REDMAPS uses a pruning algorithm to avoid unnecessary computation of cost metrics and the reduced problem is solved using linear programming. REDMAPS was first evaluated and its parameters tuned using simulations. It was then validated using five phantom and 21 patient datasets. REDMAPS was successful in reconstructing the seeds with an overall seed matching rate above 99% and a reconstruction error below 1 mm in less than 5 s.