@article {Fischer2007a, title = {Validation system of MR image overlay and other needle insertion techniques}, journal = {Studies in health technology and informatics}, volume = {125}, year = {2007}, pages = {130{\textendash}135}, publisher = {Engineering Research Center, Johns Hopkins University, Baltimore, MD, USA}, abstract = {

In order to develop accurate, effective augmented reality (AR) systems used in MR, CT guided needle placement procedures, a comparative validation environment is necessary Clinical equipment is prohibitively expensive, often inadequate for precise measurement Therefore, we have developed a laboratory validation system for measuring operator performance using different assistance techniques Electromagnetically tracked needles are registered with the preoperative plan to measure placement accuracy, the insertion path The validation system provides an independent measure of accuracy that can be applied to varying methods of assistance ranging from augmented reality guidance methods to tracked navigation systems, autonomous robots In preliminary studies, this validation system is used to evaluate the performance of the image overlay, bi-plane laser guide, and traditional freehand techniques

}, keywords = {Computer Simulation, Magnetic Resonance Imaging, Needles, Reproducibility of Results, Tomography, United States, X-Ray Computed}, author = {Gregory Fischer and Eva Dyer and Csaba Csoma and Anton Degeut 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 {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} }