@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 {Xu2005b, title = {Lung deformation estimation and four-dimensional CT lung reconstruction}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {8}, number = {Pt 2}, year = {2005}, pages = {312{\textendash}319}, abstract = {

Four-dimensional (4D) computed tomography (CT) image acquisition is a useful technique in radiation treatment planning, interventional radiology in that it can account for respiratory motion of lungs Current 4D lung reconstruction techniques have limitations in either spatial or temporal resolution In addition, most of these techniques rely on auxiliary surrogates to relate the time of CT scan to the patient{\textquoteright}s respiratory phase In this paper, we propose a novel 4D CT lung reconstruction, deformation estimation algorithm Our algorithm is purely image based The algorithm can reconstruct high quality 4D images even if the original images are acquired under irregular respiratory motion The algorithm is validated using synthetic 4D lung data Experimental results from a swine study data are also presented

}, keywords = {Algorithms, Animals, Artificial Intelligence, Biological, Computer Simulation, Computer-Assisted, Elasticity, Humans, Imaging, Lung, methods, Models, physiology/radiography, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Respiratory Mechanics, Sensitivity, Specificity, Swine, Three-Dimensional, Tomography, X-Ray Computed}, author = {Sheng Xu and Russell H. Taylor and Gabor Fichtinger and Kevin Cleary} } @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} }