By Topic

A Probabilistic Framework Based on Hidden Markov Model for Fiducial Identification in Image-Guided Radiation Treatments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zhiping Mu ; Accuray Inc., Sunnyvale, CA ; Dongshan Fu ; Gopinath Kuduvalli

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.

Published in:

IEEE Transactions on Medical Imaging  (Volume:27 ,  Issue: 9 )