Unsupervised clustering method to convert high-resolution magnetic resonance volumes to three-dimensional acoustic models for full-wave ultrasound simulations

Abstract

Simulations of acoustic wave propagation, including both the forward and the backward propagations of the wave (also known as full-wave simulations), are increasingly utilized in ultrasound imaging due to their ability to more accurately model important acoustic phenomena. Realistic anatomic models, particularly those of the abdominal wall, are needed to take full advantage of the capabilities of these simulation tools. We describe a method for converting fat-water-separated magnetic resonance imaging (MRI) volumes to anatomical models for ultrasound simulations. These acoustic models are used to map acoustic imaging parameters, such as speed of sound and density, to grid points in an ultrasound simulation. The tissues of these models are segmented from the MRI volumes into five primary classes of tissue in the human abdominal wall (skin, fat, muscle, connective tissue, and nontissue). This segmentation is achieved using an unsupervised machine learning algorithm, fuzzy c-means clustering (FCM), on a multiscale feature representation of the MRI volumes. We describe an automated method for utilizing FCM weights to produce a model that achieves ∼90% agreement with manual segmentation. Two-dimensional (2-D) and three-dimensional (3-D) full-wave nonlinear ultrasound simulations are conducted, demonstrating the utility of realistic 3-D abdominal wall models over previously available 2-D abdominal wall models.

Virtual clinical trials in medical imaging: a review

“…Fundamental mechanisms of image degradation due to acoustic clutter are just beginning to be understood through simulation study using nonlinear tools 356 combined with digitized histological samples 357 or tissue models derived from other imaging methods. 358 Point targets can provide information on resolution not available in clinical imaging, whereas anechoic and echogenic targets with known geometry and scattering contrast predict clinical imaging performance using a ground truth with which to compare across imaging methods. 359 – 363 Blood vessels of varying geometries have been simulated by pairing computational fluid dynamics software with pulse echo acoustic simulation for the development of flow estimation techniques, mimicking in a controlled environment the complex flow patterns observed in vivo .…”