@article {Lee2008a, title = {Prostate brachytherapy seed localization with Gaussian blurring and camera self-calibration}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {11}, number = {Pt 2}, year = {2008}, pages = {636{\textendash}643}, publisher = {Department of Electrical, Computer Engineering, Johns Hopkins University, Baltimore, MD, USA}, abstract = {

A tomosynthesis-based prostate brachytherapy seed localization method is described Gaussian-blurred images are computed from a limited number of X-ray images, and a 3-D volume is reconstructed by backprojection Candidate seed locations are extracted from the reconstructed volume, false positive seeds are removed by optimizing a local cost function In case where the estimated pose error is large, a self-calibration process corrects the estimation error of the intrinsic camera parameters, the translation of the pose in order to improve the reconstruction Simulation, phantom experiment results imply that the implanted seed locations can be estimated from four or five images depending on the number of seeds The algorithm was also validated using patient data, successfully localizing the implanted seeds

}, keywords = {Artifacts, Brachytherapy, Calibration, Computer-Assisted, Humans, instrumentation/methods/standards, Male, methods, methods/standards, Normal Distribution, Prostatic Neoplasms, Radiographic Image Interpretation, radiography/radiotherapy, Radiotherapy, Tomography, X-Ray Computed}, author = {Junghoon Lee and Xiaofeng Liu and Jerry L. Prince and Gabor Fichtinger} } @article {Boctor2006a, title = {Bootstrapped ultrasound calibration}, journal = {Studies in health technology and informatics}, volume = {119}, year = {2006}, pages = {61{\textendash}66}, publisher = {Engineering Research Center, Johns Hopkins University, USA}, abstract = {

This paper introduces an enhanced (bootstrapped) method for tracked ultrasound probe calibration Prior to calibration, a position sensor is used to track an ultrasound probe in 3D space, while the US image is used to determine calibration target locations within the image From this information, an estimate of the transformation matrix of the scan plane with respect to the position sensor is computed While all prior calibration methods terminate at this phase, we use this initial calibration estimate to bootstrap an additional optimization of the transformation matrix on independent data to yield the minimum reconstruction error on calibration targets The bootstrapped workflow makes use of a closed-form calibration solver, associated sensitivity analysis, allowing for rapid, robust convergence to an optimal calibration matrix Bootstrapping demonstrates superior reconstruction accuracy

}, keywords = {Calibration, Imaging, standards, Three-Dimensional, Ultrasonography, United States}, author = {Emad Boctor and Iulian Iordachita and Michael Choti and Gregory D. Hager and Gabor Fichtinger} } @article {Jain2006a, title = {C-arm tracking and reconstruction without an external tracker}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {9}, number = {Pt 1}, year = {2006}, pages = {494{\textendash}502}, publisher = {Department of Computer Science, Johns Hopkins University, USA}, abstract = {

For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence, reconstruction, without the use of optical/electromagnetic trackers or precision-made fiducial fixtures Our method uses randomly distributed unknown points in the imaging volume, either naturally present or induced by randomly sticking beads or other simple markers in the image pace After these points are segmented, a high dimensional non-linear optimization computes all unknown parameters for calibration, C-arm pose, correspondence, reconstruction Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0 9 degrees, a 3D reconstruction error of 0 8 mm, with an 80 region of convergence for both the AP, lateral axes The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation, does not interfere with the workspace

}, keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods, methods, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional}, author = {Ameet K. Jain and Gabor Fichtinger} } @article {Jain2005, title = {C-arm calibration-is it really necessary?}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {8}, number = {Pt 1}, year = {2005}, pages = {639{\textendash}646}, abstract = {

C-arm fluoroscopy is modelled as a perspective projection, the parameters of which are estimated through a calibration procedure It has been universally accepted that precise intra-procedural calibration is a prerequisite for accurate quantitative C-arm fluoroscopy guidance Calibration, however, significantly adds to system complexity, which is a major impediment to clinical practice We challenge the status quo by questioning the assumption that precise intra-procedural calibration is really necessary We derived theoretical bounds for the sensitivity of 3D measurements to mis-calibration Experimental results corroborated the theory in that mis-calibration in the focal spot by as much as 50 mm still allows for tracking with an accuracy of 0 5 mm in translation, 0 65 degrees in rotation, and such mis-calibration does not impose any additional error on the reconstruction of small objects

}, keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods/standards, methods/standards, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, United States}, author = {Ameet K. Jain and Ryan Kon and Yu Zhou and Gabor Fichtinger} } @article {Fichtinger2005, title = {Image overlay for CT-guided needle insertions}, journal = {Journal of Computer Aided Surgery}, volume = {10}, number = {4}, year = {2005}, month = {Jul}, pages = {241{\textendash}255}, abstract = {

OBJECTIVE: We present a 2D image overlay device to assist needle placement on computed tomography (CT) scanners MATERIALS AND METHODS: The system consists of a flat display, a semitransparent mirror mounted on the gantry When the physician looks at the patient through the mirror, the CT image appears to be floating inside the body with correct size, position as if the physician had 2D {\textquoteright}X-ray vision{\textquoteright} The physician draws the optimal path on the CT image The composite image is rendered on the display, thus reflected in the mirror The reflected image is used to guide the physician in the procedure In this article, we describe the design, various embodiments of the 2D image overlay system, followed by the results of phantom, cadaver experiments in multiple clinical applications RESULTS: Multiple skeletal targets were successfully accessed with one insertion attempt Generally, successful access was recorded on liver targets when a clear path opened, but the number of attempts, accuracy showed variability because of occasional lack of access Soft tissue deformation further reduced the accuracy, consistency in comparison to skeletal targets CONCLUSION: The system demonstrated strong potential for reducing faulty needle insertion attempts, thereby reducing X-ray dose, patient discomfort

}, keywords = {Animals, Biopsy, Cadaver, Calibration, Computer-Assisted, Equipment Design, Humans, Image Processing, Imaging, instrumentation, Interventional, Needle, Phantoms, Radiography, Swine, Tomography, User-Computer Interface, X-Ray Computed}, doi = {10 1080/10929080500230486}, url = {http://dx doi org/10 1080/10929080500230486}, author = {Gabor Fichtinger and Anton Degeut and Gregory Fischer and Iulian Iordachita and Emese Balogh and Ken Masamune and Russell H. Taylor and Laura Fayad and Michelle de Oliveira and James Zinreich} } @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} }