Summary
Objectives
: A comprehensive model of the human heart that covers multiple surfaces, like those of the four chambers and the attached vessels, is presented. It also contains the coronary arteries and a set of 25 anatomical landmarks. The statistical model is intended to provide a priori information for automated diagnostic and interventional procedures.
Methods
: The end-diastolic phase of the model was adapted to fit 27 clinical multi-slice computed tomography images, thus reflecting the anatomical variability to be observed in that sample. A mean cardiac motion model was also calculated from a set of eleven multi-phase computed tomography image sets. A number of experiments were performed to determine the accuracy of model-based predictions done on unseen cardiac images.
Results
: Using an additional deformable surface technique, the model allows for determination of all chambers and the attached vessels on the basis of given anatomical landmarks with an average accuracy of 1.1 mm. After such an individualization of the model by surface adaptation the centerlines of the three main coronary arteries may be estimated with an average accuracy of 5.2 mm. The mean motion model was used to estimate the cardiac phase of an unknown multislice computed tomography image.
Conclusion
: The mean shape model of the human heart as presented here complements automated image analysis methods with the required a priori information about anatomical constraints to make them work fast and robustly.
Bayesian recognition of a moving object information-measuring system state: a priori information weight correction