We propose an optimization algorithm to solve the brachytherapy seed localization problem in prostate brachytherapy. Our algorithm is based on novel geometric approaches to exploit the special structure of the problem and relies on a number of key observations which help us formulate the optimization problem as a minimization integer program (IP). Our IP model precisely defines the feasibility polyhedron for this problem using a polynomial number of half-spaces; the solution to its corresponding linear program is rounded to yield an integral solution to our task of determining correspondences between seeds in multiple projection images. The algorithm is efficient in theory as well as in practice and performs well on simulation data (approximately 98% accuracy) and real X-ray images (approximately 95% accuracy). We present in detail the underlying ideas and an extensive set of performance evaluations based on our implementation.

The harmful effects of ionizing radiation, as employed in a variety of medical imaging procedures, have been well studied and documented. To minimize risk to patients, operators must continually assess the dose rate and cumulative dose to the patient at each area of exposure. We have developed a computer graphic dose management display system which provides this operator feedback. The system is comprised of a signal processing module which reads the state of a fluoroscopy machine, a transmission ionization chamber for exposure measurement, and a visualization of the patient that displays the current level of radiation intensity and accumulated dose at every location on the body. The system shows the beam projection and orientation of the machine and color-coded dose metrics on the patient graphic model in real time. Additionally, a database system has been incorporated to allow for recording and playback of the entire procedure.

This paper proposes a new discrete optimization framework for tomographic reconstruction and segmentation of CT volumes when only a few projection views are available. The problem has important clinical applications in coronary angiographic imaging. We first show that the limited view reconstruction and segmentation problem can be formulated as a "constrained" version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings metric labeling classification and classical algebraic tomographic reconstruction (ART) together in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients (a realistic assumption), given a regular limited view reconstruction, we view it as a task of voxels reassignment subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct (or segment) volumes with several multiple contrast objects. We present evaluations using experiments on cone beam computed tomography.

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