Title :
A Probabilistic Framework Based on Hidden Markov Model for Fiducial Identification in Image-Guided Radiation Treatments
Author :
Mu, Zhiping ; Fu, Dongshan ; Kuduvalli, Gopinath
Author_Institution :
Accuray Inc., Sunnyvale, CA
Abstract :
Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate´s resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials´ geometrical configuration; and th.e Viterbi algorithm provides an efficient solution. We then discuss the problem of identifying fiducials using stereo projections, and propose a special, higher order HMM, which consists of two parallel HMMs, connected by an association measure that captures the inherent correlation between the two projections. A novel algorithm, the concurrent viterbi with association (CVA) algorithm, is introduced to efficiently identify fiducials in the two projections simultaneously. This probabilistic framework is highly flexible and provides a buffer to accommodate deformations. A simple implementation of the CVA algorithm is presented to evaluate the efficacy of the framework. Experiments were carried out using clinical images acquired during patient treatments, and several examples are presented to illustrate a variety of clinical situations. In the experiments, the algorithm demonstrated a large tracking range, computational efficiency, ease of use, and robustness that meet the requirements for clinical use.
Keywords :
hidden Markov models; maximum likelihood estimation; medical computing; probability; radiation therapy; stereo image processing; target tracking; CVA algorithm; HMM; Viterbi algorithm; context information; fiducial tracking; hidden Markov model; image-guided radiation treatments; multifiducial identification; probabilistic framework; radiosurgery; radiotherapy; state sequence problem; stereo projections; target tracking method; transition probability; Computed tomography; Hidden Markov models; Image converters; Liver neoplasms; Lung neoplasms; Medical treatment; Optical imaging; Target tracking; Ultrasonic imaging; Viterbi algorithm; Fiducial tracking; Hidden Markov Model; Image-guided radiosurgery and radiotherapy; Viterbi algorithm; fiducial tracking; hidden Markov model; image-guided radiosurgery and radiotherapy; viterbi algorithm; Algorithms; Artificial Intelligence; Computer Simulation; Data Interpretation, Statistical; Imaging, Three-Dimensional; Markov Chains; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Radiosurgery; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Surgery, Computer-Assisted; Tomography, X-Ray Computed;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2008.922693