Automatic prostate brachytherapy preplanning using joint sparse analysis

Abstract

Prostate brachytherapy preplanning is the process of determining treatment target volume and arrangement of radioactive seeds w.r.t. the target volume prior to the implantation. Although preplanning is typically performed by a trained expert using a strict set of guidelines, the process remains highly subjective, resulting in significant user-dependent variability in the plans. In this work, we aim to reduce the preplanning variability by automating the seed arrangement process. We propose a novel framework which uses a retrospective treatment dataset to extract common radioactive seed patterns. The framework captures the inter-relation between the treatment volume delineation and seed arrangements through a joint sparse representation of retrospective data. This representation is used to estimate an initial seed arrangement for a new treatment volume, followed by a novel optimization process which captures the clinical guidelines, to fine-tune the seed arrangement. The proposed framework is evaluated on a dataset of 590 brachytherapy treatment cases by 5-fold cross validation. It achieves 86% success rate, when compared to the clinical guidelines and the actual plans.