Automatic tracking of the aorta in cardiovascular MR images using deformable models.
We present a new algorithm for the robust and accurate tracking of the aorta in cardiovascular magnetic resonance (MR) images. First, a rough estimate of the location and diameter of the aorta is obtained by applying a multiscale medial-response function using the available a priori knowledge. Then, this estimate is refined using an energy-minimizing deformable model which we define in a Markov-random-field (MRF) framework. In this context, we propose a global minimization technique based on stochastic relaxation, Simulated annealing (SA), which is shown to be superior to other minimization techniques, for minimizing the energy of the deformable model. We have evaluated the performance and robustness of the algorithm on clinical compliance studies in cardiovascular MR images. The segmentation and tracking has been successfully tested in spin-echo MR images of the aorta. The results show the ability of the algorithm to produce not only accurate, but also very reliable results in clinical routine applications.
Rueckert
D
D
Department of Computing, Imperial College, London, U.K. D.Ruckert@doc.ic.ac.uk
Burger
P
P
Forbat
S M
SM
Mohiaddin
R D
RD
Yang
G Z
GZ
D000465:Algorithms; D001011:Aorta / Q000033:anatomy & histology* / Q000502:physiology; D001794:Blood Pressure; D001810:Blood Volume; D003187:Compliance; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D008955:Models, Cardiovascular; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D013269:Stochastic Processes; D018924:Vascular Capacitance