@article {Boctor2006b, title = {Ultrasound monitoring of tissue ablation via deformation model and shape priors}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {9}, number = {Pt 2}, year = {2006}, pages = {405{\textendash}412}, abstract = {

A rapid approach to monitor ablative therapy through optimizing shape, elasticity parameters is introduced Our motivating clinical application is targeting, intraoperative monitoring of hepatic tumor thermal ablation, but the method translates to the generic problem of encapsulated stiff masses (solid organs, tumors, ablated lesions, etc ) in ultrasound imaging The approach involves the integration of the following components: a biomechanical computational model of the tissue, a correlation approach to estimate/track tissue deformation, and an optimization method to solve the inverse problem, recover the shape parameters in the volume of interest Successful convergence, reliability studies were conducted on simulated data Then ex-vivo studies were performed on 18 ex-vivo bovine liver samples previously ablated under ultrasound monitoring in controlled laboratory environment While B-mode ultrasound does not clearly identify the development of necrotic lesions, the proposed technique can potentially segment the ablation zone The same framework can also yield both partial, full elasticity reconstruction

}, keywords = {Algorithms, Animals, Automated, Biological, Cattle, Computer-Assisted, Elasticity, Hepatectomy, Image Enhancement, Image Interpretation, Imaging, Interventional, Liver, Mechanical, methods, Models, Pattern Recognition, physiology/surgery/ultrasonography, Reproducibility of Results, Sensitivity, Specificity, Statistical, Stress, Subtraction Technique, Surgery, Three-Dimensional, Ultrasonography}, author = {Emad Boctor and Michelle de Oliveira and Michael Choti and Roger Ghanem and Russell H. Taylor and Gregory D. Hager and Gabor Fichtinger} } @article {Jain2005e, title = {Matching and reconstruction of brachytherapy seeds using the Hungarian algorithm (MARSHAL)}, journal = {Journal of Medical Physics}, volume = {32}, number = {11}, year = {2005}, month = {Nov}, pages = {3475{\textendash}3492}, abstract = {

Intraoperative dosimetric quality assurance in prostate brachytherapy critically depends on discerning the three-dimensional (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 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 combinatorial optimization problem, which has salient features: (a) extensively studied solutions by the computer science community; (b) proof for the nonexistence of any polynomial time exact algorithm;, (c) a practical pseudo-polynomial algorithm that mostly runs in O(N3) time using any number of images We prove that two images are insufficient to correctly match the seeds, while a third image renders the matching problem to be of nonpolynomial complexity We utilize the special structure of the problem, propose a pseudopolynomial time algorithm Using three presegmented images, matching, reconstruction of brachytherapy seeds using the Hungarian algorithm achieved complete matching in simulation experiments;, 98 5\% in phantom experiments 3D reconstruction error for correctly matched seeds has a mean of 0 63 mm, and 0 9 mm for incorrectly matched seeds The maximum seed reconstruction error in each implant was typically around 1 32 mm Both on synthetic data, in phantom experiments, matching rate, reconstruction error achieved using presegmented images was found to be sufficient for prostate brachytherapy The algorithm is extendable to deal with arbitrary number of images without any loss in speed or accuracy The algorithm is sufficiently generic to provide a practical solution to any correspondence problem, across different imaging modalities, features

}, keywords = {Algorithms, Automated, Brachytherapy, Calibration, Computer Simulation, Computer-Assisted, Humans, Image Interpretation, Imaging, Male, methods, Models, Numerical Analysis, Pattern Recognition, Phantoms, Prostatic Neoplasms, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiometry, Radiotherapy, Radiotherapy Dosage, Radiotherapy Planning, Reproducibility of Results, Statistical, Theoretical, Three-Dimensional, Time Factors, Ultrasonography}, author = {Ameet K. Jain and Yu Zhou and Tabish Mustufa and E. Clif Burdette and Gregory Chirikjian and Gabor Fichtinger} } @article {Shen2004a, title = {Optimized prostate biopsy via a statistical atlas of cancer spatial distribution}, journal = {Medical image analysis}, volume = {8}, number = {2}, year = {2004}, month = {Jun}, pages = {139{\textendash}150}, abstract = {

A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy An adaptive-focus deformable model is used for the spatial normalization, registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer Experimental results using cross-validation show that the proposed method can detect cancer with a 99\% success rate using seven needles, in these samples

}, keywords = {Algorithms, Biological, Biopsy, Cohort Studies, Computer Simulation, Computer-Assisted, Forecasting, Humans, Image Processing, Imaging, Male, Models, Needle, pathology, Probability, Prostate, Prostatic Neoplasms, Reproducibility of Results, Statistical, statistics /\&/ numerical data, Three-Dimensional}, doi = {10 1016/j media 2003 11 002}, url = {http://dx doi org/10 1016/j media 2003 11 002}, author = {Dinggang Shen and Zhiqiang Lao and Jianchao Zeng and Wei Zhang and Isabel Sesterhenn and Leon Sun and Judd Moul and Edward Herskovits and Gabor Fichtinger and Christos Davatzikos} }