Intra-operative quality assurance and dosimetry optimization in prostate brachytherapy critically depends on the ability of discerning the locations of implanted seeds. Various methods exist for seed matching and reconstruction from multiple segmented C-arm images. Unfortunately, using three or more images makes the problem NP-hard, i.e. no polynomial-time algorithm can provably compute the complete matching. Typically, a statistical analysis of performance is considered sufficient. Hence it is of utmost importance to exploit all the available information in order to minimize the matching and reconstruction errors. Current algorithms use only the information about seed centers, disregarding the information about the orientations and length of seeds. While the latter has little dosimetric impact, it can positively contribute to improving seed matching rate and 3D implant reconstruction accuracy. It can also become critical information when hidden and spuriously segmented seeds need to be matched, where reliable and generic methods are not yet available. Expecting orientation information to be useful in reconstructing large and dense implants, we have developed a method which incorporates seed orientation information into our previously proposed reconstruction algorithm (MARSHAL). Simulation study shows that under normal segmentation errors, when considering seed orientations, implants of 80 to 140 seeds with the density of 2.0- 3.0 seeds/cc give an average matching rate >97% using three-image matching. It is higher than the matching rate of about 96% when considering only seed positions. This means that the information of seed orientations appears to be a valuable additive to fluoroscopy-based brachytherapy implant reconstruction.
Purpose: C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct 3D information. A major obstacle in fluoroscopic reconstruction is discerning the pose of the X-ray image, in 3D space. Optical/magnetic trackers are prohibitively expensive, intrusive and cumbersome. Method: We present single-image-based fluoroscope tracking (FTRAC) with the use of an external radiographic fiducial consisting of a mathematically optimized set of points, lines, and ellipses. The fiducial encodes six degrees of freedom in a single image by creating a unique view from any direction. A non-linear optimizer can rapidly compute the pose of the fiducial using this image. The current embodiment has salient attributes: small
dimensions (3 x 3 x 5 cm), it need not be close to the anatomy of interest and can be segmented automatically. Results: We tested the fiducial and the pose recovery method on synthetic data and also experimentally on a precisely machined mechanical phantom. Pose recovery had an error of 0.56 mm in translation and 0.33° in orientation. Object reconstruction had a mean error of 0.53 mm with 0.16 mm STD. Conclusion: The method offers accuracies similar to commercial tracking systems, and is sufficiently robust for intra-operative quantitative
Purpose: Intraoperative dosimetric quality assurance in prostate brachytherapy critically depends on discerning the 3D locations of implanted seeds. The ability to reconstruct the implanted seeds intraoperatively will allow us to make immediate provisions for dosimetric deviations from the optimal implant plan. A method for seed reconstruction from segmented C-arm fluoroscopy images is proposed. Method: The 3D coordinates of the implanted seeds can be calculated upon resolving the correspondence of seeds in multiple X-ray images. We formalize seed-matching as a network flow problem, which has salient features: (a) extensively studied exact solutions, (b) performance claims on the space-time complexity, (c) optimality bounds on the final solution. A fast implementation is realized using the Hungarian algorithm. Results: We prove that two images can correctly match only about 67% of the seeds, and that a third image renders the matching problem to be of non-polynomial complexity. We utilize the special structure of the problem and propose a pseudo-polynomial time algorithm. Using three images, MARSHAL achieved 100% matching in simulation experiments; and 98.5% in phantom experiments. 3D reconstruction error for correctly matched seeds has a mean of 0:63 mm, and 0:91 mm for incorrectly matched seeds. Conclusion: Both on synthetic data and in phantom experiments, matching rate and reconstruction accuracy were found to be sufficient for prostate brachytherapy. The algorithm is extendable to deal with arbitrary number of images without loss in speed or accuracy. The algorithm is sufficiently generic to be used for establishing correspondences across any choice of features in different imaging modalities.