An Enhanced Multi-frequency Distorted Born Iterative Method for Ultrasound Tomography based on Fundamental Tone and Overtones

Conventional Distorted Born Iterative Method (DBIM) using single frequency has low resolution and is prone to creating images with high-contrast subjects. We propose a productive frequency combination method to better result in tomographic ultrasound imaging based on the multi-frequency technique. This study uses the natural mechanism of emitting oscillators' frequencies and uses these frequencies for imaging in iterations. We use a fundamental tone (i.e., the starting frequency f0) for the first iteration in DBIM, then consecutively use its overtones for the next ones. The digital simulation scenarios are tested with other multi-frequency approaches to prove our method's feasibility. We performed 57 different simulation scenarios on the use of multi-frequency information for the DBIM method. As a result, the proposed method for the smallest normalization error (RRE = 0.757). The proposed method's imaging time is not significantly longer than the way of using single frequency information.

Ультразвуковой адаптивный томограф бетонных изделий с нестандартной конфигурацией

It is shown that should be used adaptive antenna arrays, the shape of which can take the form of a non-planar surface of the tested product, for ultrasonic tomography of concrete building structures with a non-standard surface configuration. It should also be used adaptive methods of ultrasound tomography, which allows both to determine the coordinates of defects and the velocity of ultrasound in concrete, as well as adjust the parameters of the probing signals to the characteristics of concrete products.

The Potential Role of the Fat–Glandular Interface (FGI) in Breast Carcinogenesis: Results from an Ultrasound Tomography (UST) Study

This study explored the relationship between the extent of the fat–glandular interface (FGI) and the presence of malignant vs. benign lesions. Two hundred and eight patients were scanned with ultrasound tomography (UST) as part of a Health Insurance Portability and Accountability Act (HIPAA)-compliant study. Segmentation of the sound speed images, employing the k-means clustering method, was used to help define the extent of the FGI for each patient. The metric, α, was defined as the surface area to volume ratio of the segmented fibroglandular volume and its mean value across patients was determined for cancers, fibroadenomas and cysts. ANOVA tests were used to assess significance. The means and standard deviations of α for cancers, fibroadenomas and cysts were found to be 4.0 ± 2.0 cm−1, 3.1 ± 1.7 cm−1 and 2.3 ± 0.9 cm−1, respectively. The differences were statistically significant (p < 0.001). The separation between the groups increased when α was measured on only the image slice where the finding was most prominent, with values for cancers, fibroadenomas and cysts of 5.4 ± 3.6 cm−1, 3.6 ± 2.3 cm−1 and 2.4 ± 1.5 cm−1, respectively. Of the three types of masses studied, cancer was associated with the most extensive FGIs, suggesting a potential role for the FGI in carcinogenesis, a subject for future studies.

Multicenter Study of Whole Breast Stiffness Imaging by Ultrasound Tomography (SoftVue) for Characterization of Breast Tissues and Masses

We evaluated whole breast stiffness imaging by SoftVue ultrasound tomography (UST), extracted from the bulk modulus, to volumetrically map differences in breast tissues and masses. A total 206 women with either palpable or mammographically/sonographically visible masses underwent UST scanning prior to biopsy as part of a prospective, HIPAA-compliant multicenter cohort study. The volumetric data sets comprised 298 masses (78 cancers, 105 fibroadenomas, 91 cysts and 24 other benign) in 239 breasts. All breast tissues were segmented into six categories, using sound speed to separate fat from fibroglandular tissues, and then subgrouped by stiffness into soft, intermediate and hard components. Ninety percent of women had mammographically dense breasts but only 11.2% of their total breast volume showed hard components while 69% of fibroglandular tissues were softer. All smaller masses (<1.5 cm) showed a greater percentage of hard components than their corresponding larger masses (p < 0.001). Cancers had significantly greater mean stiffness indices and lower mean homogeneity of stiffness than benign masses (p < 0.05). SoftVue stiffness imaging demonstrated small stiff masses, mainly due to cancers, amongst predominantly soft breast tissues. Quantitative stiffness mapping of the whole breast and underlying masses may have implications for screening of women with dense breasts, cancer risk evaluations, chemoprevention and treatment monitoring.

Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

This paper describes the method developed using the Extreme Gradient Boosting (Xgboost) algorithm that allows high-resolution imaging using the ultrasound tomography (UST) signal. More precisely, we can locate, isolate, and use the reflective peaks from the UST signal to achieve high-resolution images with low noise, which are far more useful for the location of points where the reflection occurred inside the experimental tank. Each reconstruction is divided into two parts, estimation of starting points of wave packets of raw signal (SAT—starting arrival time) and image reconstruction via XGBoost algorithm based on SAT matrix. This technology is the basis of a project to design non-invasive monitoring and diagnostics of technological processes. In this paper, we present a method of the complete solution for monitoring industrial processes. The measurements used in the study were obtained with the author’s solution of ultrasound tomography.