Simulating cardiac ultrasound image based on MR diffusion tensor imaging
Cardiac ultrasound simulation can have important applications in the design of ultrasound systems, understanding the interaction effect between ultrasound and tissue and setting the ground truth for validating quantification methods. Current ultrasound simulation methods fail to simulate the myocardial intensity anisotropies. New simulation methods are needed in order to simulate realistic ultrasound images of the heart.
The proposed cardiac ultrasound image simulation method is based on diffusion tensor imaging (DTI) data of the heart. The method utilizes both the cardiac geometry and the fiber orientation information to simulate the anisotropic intensities in B-mode ultrasound images. Before the simulation procedure, the geometry and fiber orientations of the heart are obtained from high-resolution structural MRI and DTI data, respectively. The simulation includes two important steps. First, the backscatter coefficients of the point scatterers inside the myocardium are processed according to the fiber orientations using an anisotropic model. Second, the cardiac ultrasound images are simulated with anisotropic myocardial intensities. The proposed method was also compared with two other nonanisotropic intensity methods using 50 B-mode ultrasound image volumes of five different rat hearts. The simulated images were also compared with the ultrasound images of a diseased rat heart in vivo. A new segmental evaluation method is proposed to validate the simulation results. The average relative errors (AREs) of five parameters, i.e., mean intensity, Rayleigh distribution parameter σ, and first, second, and third quartiles, were utilized as the evaluation metrics. The simulated images were quantitatively compared with real ultrasound images in both ex vivo and in vivo experiments.
The proposed ultrasound image simulation method can realistically simulate cardiac ultrasound images of the heart using high-resolution MR-DTI data. The AREs of their proposed method are 19% for the mean intensity, 17.7% for the scale parameter of Rayleigh distribution, 36.8% for the first quartile of the image intensities, 25.2% for the second quartile, and 19.9% for the third quartile. In contrast, the errors of the other two methods are generally five times more than those of their proposed method.
The proposed simulation method uses MR-DTI data and realistically generates cardiac ultrasound images with anisotropic intensities inside the myocardium. The ultrasound simulation method could provide a tool for many potential research and clinical applications in cardiac ultrasound imaging.