@article {Jain2007, title = {Intra-operative 3D guidance in prostate brachytherapy using a non-isocentric C-arm}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {10}, number = {Pt 2}, year = {2007}, pages = {9{\textendash}17}, publisher = {Johns Hopkins University, USA}, abstract = {

Intra-operative guidance in Transrectal Ultrasound (TRUS) guided prostate brachytherapy requires localization of inserted radioactive seeds relative to the prostate Seeds were reconstructed using a typical C-arm, and exported to a commercial brachytherapy system for dosimetry analysis Technical obstacles for 3D reconstruction on a non-isocentric C-arm included pose-dependent C-arm calibration; distortion correction; pose estimation of C-arm images; seed reconstruction;, C-arm to TRUS registration In precision-machined hard phantoms with 40-100 seeds, we correctly reconstructed 99 8\% seeds with a mean 3D accuracy of 0 68 mm In soft tissue phantoms with 45-87 seeds, clinically realistic 15 degrees C-arm motion, we correctly reconstructed 100\% seeds with an accuracy of 1 3 mm The reconstructed 3D seed positions were then registered to the prostate segmented from TRUS In a Phase-1 clinical trial, so far on 4 patients with 66-84 seeds, we achieved intra-operative monitoring of seed distribution, dosimetry We optimized the 100\% prescribed iso-dose contour by inserting an average of 3 75 additional seeds, making intra-operative dosimetry possible on a typical C-arm, at negligible additional cost to the existing clinical installation

}, keywords = {Algorithms, Brachytherapy, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Humans, Imaging, instrumentation/methods, Interventional, Intraoperative Care, Male, methods, Prostatic Neoplasms, Prosthesis Implantation, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiography, radiography/radiotherapy, Radiotherapy, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed}, author = {Ameet K. Jain and Anton Degeut and Iulian Iordachita and Gouthami Chintalapani and Jack Blevins and Le Yi and Elwood Armour and E. Clif Burdette and Danny Y Song and Gabor Fichtinger} } @article {Liu2007, title = {Prostate implant reconstruction with discrete tomography}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {10}, number = {Pt 1}, year = {2007}, pages = {734{\textendash}742}, publisher = {Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA}, abstract = {

We developed a discrete tomography method for prostate implant reconstructions using only a limited number of X-ray projection images A 3D voxel volume is reconstructed by back-projection, using distance maps generated from the projection images The true seed locations are extracted from the voxel volume while false positive seeds are eliminated using a novel optimal geometry coverage model The attractive feature of our method is that it does not require exact seed segmentation of the X-ray images, it yields near 100\% correct reconstruction from only six images with an average reconstruction accuracy of 0 86 mm (std = 0 46mm)

}, keywords = {Brachytherapy, Computer-Assisted, Humans, Imaging, instrumentation/methods, Male, methods, Prostatic Neoplasms, Prosthesis Implantation, Radiographic Image Enhancement, Radiographic Image Interpretation, radiography/radiotherapy, Three-Dimensional}, author = {Xiaofeng Liu and Ameet K. Jain and Gabor Fichtinger} } @article {Labat2007, title = {Toward optimal matching for 3D reconstruction of brachytherapy seeds}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {10}, number = {Pt 2}, year = {2007}, pages = {701{\textendash}709}, publisher = {Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA}, 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

}, keywords = {Algorithms, Artificial Intelligence, Automated, Brachytherapy, Computer-Assisted, Image Enhancement, Image Interpretation, Imaging, instrumentation/methods, methods, Pattern Recognition, Phantoms, Prosthesis Implantation, Radiotherapy, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed}, author = {Christian Labat and Ameet K. Jain and Gabor Fichtinger and Jerry L. Prince} }