A dynamic statistical cardiac atlas with adjustable shape and motion features

Background and Objectives: Digital heart atlases play important roles in computational cardiac simulation and medical image analysis. During the past decades, various heart anatomy models were developed, but they mostly focused on the ventricular part. Recently, a number of whole-heart atlases were developed but they rarely modelled the motion features. This study constructed a whole-heart atlas incorporating dynamic cardiac motion. Materials and Methods: The shape and motion features of the atlas were learnt from a training set of 57 dynamic computed tomographic angiography images including 20 cardiac phases. Inter-subject variations of the heart anatomy and motion were incorporated into the atlas using the statistical shape modelling approach. Clinically relevant physiological parameters (e.g., chamber volumes, ejection fraction, and percentage of systolic phase) were correlated with the shape and motion variations using the linear regression approach. The shape and motion pattern of the atlas can be adapted by adjusting the physiological parameters. Results: Quantitative experiments were conducted to measure the anatomical accuracy of the atlas for whole-heart shape reconstruction of different subjects, a mean Dice score of 0.89-0.93 and a mean surface distance of 1.02-1.91 mm were achieved for the four heart chambers, respectively. Conclusions: This atlas provides a novel computational tool with adjustable shape and motion parameters for cardiac simulation research.