Intraoperative 3D reconstruction of prostate brachytherapy implants with automatic pose correction

Abstract

The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland, and the capability of intraoperatively localizing implanted seeds provides potential for dose evaluation and optimization during therapy. REDMAPS is a recently reported algorithm that carries out seed localization by detecting, matching and reconstructing seeds in only a few seconds from three acquired x-ray images (Lee et al 2011 IEEE Trans. Med. Imaging 29 38-51). In this paper, we present an automatic pose correction (APC) process that is combined with REDMAPS to allow for both more accurate seed reconstruction and the use of images with relatively large pose errors. APC uses a set of reconstructed seeds as a fiducial and corrects the image pose by minimizing the overall projection error. The seed matching and APC are iteratively computed until a stopping condition is met. Simulations and clinical studies show that APC significantly improves the reconstructions with an overall average matching rate of ⩾99.4%, reconstruction error of ⩽0.5 mm, and the matching solution optimality of ⩾99.8%.

Figure 1

APC-REDMAPS flowchart.

Figure 2

Simulation results. Performance comparison between REDMAPS and APC-REDMAPS. (a, b) Seed matching rate, (c, d) relative reconstruction error, (e, f) solution optimality. We considered different levels of (a, c, e) rotation and (b, d, f) translation pose errors.

Figure 3

Example images of reconstruction results on a clinical data set. The reconstructed seeds are reprojected onto one of three images (white dots) used for reconstruction. Left image shows one of the worst REDMAPS reconstruction caused by large image pose errors and small image acquisition angle separation. The pose errors were compensated and the seeds were reconstructed correctly by APC-REDMAPS (right image).

Figure 4

Dimensionality reduction threshold (left) and the computation time (right) plots at each iteration. Blue circles indicate individual measurements at each iteration, and the red lines show the mean±std variations.

Figure 5

Image acquisition geometry for the seed reconstruction without external tracker.