A physically based, probabilistic model for ultrasonic images incorporating shape, microstructure, and system characteristics

A simulation model for ultrasonic temperature imaging using change in backscattered energy

Ultrasound backscattered from tissue has previously been shown theoretically and experimentally to change predictably with temperature in the hyperthermia range, i.e., 37 degrees C to 45 degrees C, motivating use of the change in backscattered ultrasonic energy (CBE) for ultrasonic thermometry. Our earlier theoretical model predicts that CBE from an individual scatterer will be monotonic with temperature, with, e.g., positive change for lipid-based scatterers and negative for aqueous-based scatterers. Experimental results have previously confirmed the presence of these positive and negative changes in one-dimensional ultrasonic signals and in two-dimensional images acquired from in vitro bovine, porcine and turkey tissues. In order to investigate CBE for populations of scatterers, we have developed an ultrasonic image simulation model, including temperature dependence for individual scatterers based on predictions from our theoretical model. CBE computed from images simulated for populations of randomly distributed scatterers behaves similarly to experimental results, with monotonic variation for individual pixel measurements and for image regions. Effects on CBE of scatterer type and distribution, size of the image region and signal-to-noise ratio have been examined. This model also provides the basis for future work regarding significant issues relevant to temperature imaging based on ultrasonic CBE such as effects of motion on CBE, limitations of motion-compensation techniques and accuracy of temperature estimation, including tradeoffs between temperature accuracy and available spatial resolution.

Estimation of surface pose with a physically-based ultrasonic image model

State-of-the-art approaches to shape analysis in medical images use a variety of sophisticated models for object shape. We have developed an image model that permits the application of these approaches to ultrasonic images, with detailed methods for representing rough surfaces. Our physically-based, probabilistic image model incorporates the combined effects of the system point-spread function (PSF), the tissue microstructure, and the gross tissue shape. At each image pixel, the amplitude mean and variance are computed directly from the model, characterizing the combined influence of shape, microstructure, and system PSF. Calculation of the SNR0 is used to further classify each pixel as Rayleigh- or non-Rayleigh-distributed. This characterization was used here to generate a data likelihood representing any set of images of a given surface by a probability density conditioned on the surface pose, or rotation and translation. The utility of this likelihood was demonstrated by applying maximum likelihood estimation to infer the pose of a cadaveric vertebra from simulated images of its surface. Successful results were achieved using derivative-based optimization algorithms for a data set of only three images. With a quasi-Newton BFGS algorithm, error in 15 of 20 trials was less than 0.4 degrees in rotation and 0.2 mm in translation. Estimation was inaccurate in only 1 of 20 trials. These results illustrate the potential of a physically-based image model in a rigorous approach to image analysis and also serve as an example of quantitative assessment of the model via performance in a specific application.