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.
Abstract. X-ray C-arm fluoroscopy is a natural choice for intra-operative seed localization in prostate brachytherapy. Resolving the correspondence of seeds in the projection images can be modeled as an assignment problem that is NP-hard. Our approach rests on the practical observation that the optimal solution has almost zero cost if the pose of the C-arm is known accurately. This allowed us to to derive an equivalent problem of reduced dimensionality that, with linear programming, can be solved efficiently in polynomial time. Additionally, our method demonstrates significantly increased robustness to C-arm pose errors when compared to the prior art. Because under actual clinical circumstances it is exceedingly difficult to track the C-arm, easing on this constraint has additional practical utility.
In prostate brachytherapy, x-ray fluoroscopy has been used for intra-operative dosimetry to provide qualitative assessment of implant quality. More recent developments have made possible 3D localization of the implanted radioactive seeds. This is usually modeled as an assignment problem and solved by resolving the correspondence of seeds. It is, however, NP-hard, and the problem is even harder in practice due to the significant number of hidden seeds. In this paper, we propose an algorithm that can find an optimal solution from multiple projection images with hidden seeds. It solves an equivalent problem with reduced dimensional complexity, thus allowing us to find an optimal solution in polynomial time. Simulation results show the robustness of the algorithm. It was validated on 5 phantom and 18 patient datasets, successfully localizing the seeds with detection rate of ≥ 97.6 % and reconstruction error of ≤ 1.2 mm. This is considered to be clinically excellent performance.
Our contribution deals with blind deconvolution of sparse spike trains. More precisely, we examine the problem in the Markov chain Monte-Carlo (MCMC) framework, where the unknown spike train is modeled as a Bernoulli-Gaussian process. In this context, we point out that time-shift and scale ambiguities jeopardize the robustness of basic MCMC methods, in quite a similar manner to the label switching effect studied by Stephens (2000) in mixture model identification. Finally, we propose proper modifications of the MCMC approach, in the same spirit as Stephens' contribution.
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