Simulating cardiac ultrasound image based on MR diffusion tensor imaging

Abstract

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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. C 2015 American Association of Physicists in Medicine.

Imaging technologies for cardiac fiber and heart failure: a review

“…Based on MR-DTI heart data, Qin et al [97] proposed a cardiac ultra-sound image simulation method which utilized both the cardiac geometry and the fiber orientation information to simulate the anisotropic intensities in ultrasound images from rat hearts. Prior to the simulation procedure, geometry orientations were obtained from high-resolution, structural MRI data while fiber orientations of the heart were obtained from MRDTI data.…”

“…Prior to the simulation procedure, geometry orientations were obtained from high-resolution, structural MRI data while fiber orientations of the heart were obtained from MRDTI data. The simulation included two steps: (1) backscatter coefficients of the point scatters inside the myocardium were processed according to the fiber orientations using an anisotropic model and (2) the cardiac ultrasound images were simulated with anisotropic myocardial intensities [97]. This method was compared to two, non-anisotropic intensity methods using 50 B-mode ultrasound image volumes of five, different rat hearts.…”

“…The findings suggest that the average relative errors (AREs) of the five parameters, i.e., mean intensity, Rayleigh distribution parameter, 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, and this method can be used as a clinical application to generate cardiac ultrasound images with anisotropic intensities inside the myocardium [97]. …”

Breast cancer detection using image enhancement and segmentation algorithms

“…The first significant features of the breast cancer represent as mass with specific site, texture, border and shape, all these characteristics it easy to study under the image processing techniques by using the MATLAB program [14,15]. The idea of image processing by converting the result image into matrix as the binary image in addition to series of processes until reach to the appropriate result that helps to take a decision of diagnostic breast lesions [16,17]. There are many types of techniques used for mammography images detection such as edge-based transform, smoothing, noise removal and extraction techniques.…”

Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging

“…However, an increase in either DED or NEX is directly proportional to an increase in acquisition time (TA). Previous ex-vivo cardiac DTI studies have used different DED-NEX combinations, and based on the spatial resolution the corresponding TA has ranged anywhere from between 30 minutes to 60 hours [13-16]. To the best of our knowledge, there is no extensive cardiac DTI literature that explores the optimal DED-NEX combination (in a minimal TA setting) required for accurate tensor estimation.…”