@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 = {<p>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</p>
},
	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 {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 = {<p>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</p>
},
	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}
}
