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. 2009 Mar 13:7261:k.
doi: 10.1117/12.811225.

Prostate brachytherapy seed localization using a mobile c-arm without tracking

Affiliations

Prostate brachytherapy seed localization using a mobile c-arm without tracking

Maria S Ayad et al. Proc SPIE Int Soc Opt Eng. .

Abstract

The success of prostate brachytherapy depends on the faithful delivery of a dose plan. In turn, intraoperative localization and visualization of the implanted radioactive brachytherapy seeds enables more proficient and informed adjustments to the executed plan during therapy. Prior work has demonstrated adequate seed reconstructions from uncalibrated mobile c-arms using either external tracking devices or image-based fiducials for c-arm pose determination. These alternatives are either time-consuming or interfere with the clinical flow of the surgery, or both. This paper describes a seed reconstruction approach that avoids both tracking devices and fiducials. Instead, it uses the preoperative dose plan in conjunction with a set of captured images to get initial estimates of the c-arm poses followed by an auto-focus technique using the seeds themselves as fiducials to refine the pose estimates. Intraoperative seed localization is achieved through iteratively solving for poses and seed correspondences across images and reconstructing the 3D implanted seeds. The feasibility of this approach was demonstrated through a series of simulations involving variable noise levels, seed densities, image separability and number of images. Preliminary results indicate mean reconstruction errors within 1.2 mm for noisy plans of 84 seeds or fewer. These are attained for additive noise whose standard deviation of the 3D mean error introduced to the plan to simulate the implant is within 3.2 mm.

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Figures

Figure 1
Figure 1
(a) Prostate brachytherapy schematic. (b) X-ray image of brachytherapy seeds.
Figure 2
Figure 2
Fluoroscopic imaging geometry and the involved coordinate systems.
Figure 3
Figure 3
The symbolic intersection of three lines.
Figure 4
Figure 4
An example of the convergence rate of (a) the Hungarian cost and (b) the matching rate for the iterative algorithm for different noise levels.

References

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