Ultrasound is a widely used medical imaging technique because of its low cost, relative safety, and versatility. Since biological tissues are composed of characteristic structures whose ultrasonic properties often change due to diseases, the ultrasound RF echo contains information that can be used to study the underlying tissue. The goal is to model and process the ultrasound RF echo in order to extract tissue characterization features that are observer-independent.
In this project, we propose a new model for the radio-frequency (RF) ultrasound echo, namely the shot noise process with narrow-band power-law filter function. This model can be justified by considering the tissue as a collection of point scatterers embedded in a uniform medium and assuming a power-law decay instead of exponential decay for attenuation. The model is characterized by the exponent n of the power-law filter and Poisson rate l. Based on this model, the in-phase and quadrature components of the echo can be shown to exhibit 1/f b -type spectral behavior with b = 2(1-n). The envelope also exhibits this type of spectral behavior but with a different exponent and furthermore if the power-law exponent n is equal to 0.5 the envelope follows the well-known Rayleigh statistic. Although the shot noise model has been used in the past for modeling the RF echo, this is the first time that a power-law impulse response filter is used and the resulting power-law spectral behavior of the RF echo is investigated. The theoretical derivations were validated via simulations, while the validity of the proposed model was tested based on clinical ultrasound images of the breast.
The spectral exponents in the proposed model are associated to tissue attenuation, whereas l is associated to the number of scatterers. Since both properties change due to disease, the estimates of these parameters are natural candidates for tissue signatures. We have proposed algorithms for estimating b and l from the data, and investigated the potential significance of the model parameters in characterizing breast tissue. We conducted experiments based on clinical ultrasound images of the breast, provided to us by our collaborators at Thomas Jefferson University Hopsittal in Philadelphia PA. The model parameters were first estimated based on a database of 100 clinical and Receiver Operating Characteristic (ROC) analysis was subsequently applied to quantify their ability to differentiate between tumorous and non-tumorous tissue. High detection probabilities at low false-alarm rates (97.1% at 4.2% for the spectral exponent of the envelope, and 84.3% at 17.1% for the spectral exponent of the in-phase component), and respectively 97% and 93% ROC area values were obtained, which suggest that these parameters can e used for tissue characterization. The performance of the parameter l (ROC area of 89%) was not as good as that of the spectral exponents.
Although the results on differentiating between normal and abnormal tissue are very good, the ability of model parameters in differentiating between malignant and benign tumor regions is moderate. ROC areas of 72.5%, 70%, and 68% were obtained respectively for the exponent of the envelope, the in-phase component and l. Our results were based on 56 images, 28 of which contained benign tumors and rest contained malignant tumors. We are currently investigating the amount of independent information carried by b and l and the potential advantages of combining the two parameters to obtain a better tissue signature.
More
details on our research can be found in our publications. For published
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MATLAB
code for parameter estimation and constrcution of ROC graphs can be obtained
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