Institute of Fundamental Technological Research

Texture of ultrasound images contain information about the properties of examined tissues. The analysis of statistical properties of backscattered ultrasonic echoes has been recently successfully applied to differentiate healthy breast tissue from the benign and malignant lesions. We propose a novel procedure of tissue characterization based on acquiring backscattered echoes from the heated breast. We have proved that the temperature increase inside the breast modifies the intensity, spectrum of the backscattered signals and the probability density function of envelope samples. We discuss the differences in probability density functions in two types of tissue regions, e.g. cysts and the surrounding glandular tissue regions. Independently, Pennes bioheat equation in heterogeneous breast tissue was used to describe the heating process. We applied the finite element method to solve this equation. Results have been compared with the ultrasonic predictions of the temperature distribution. The results confirm the possibility of distinguishing the differences in thermal and acoustical properties of breast cyst and surrounding glandular tissues.

medical ultrasound, temperature changes in vivo, breast tissue, ultrasonic temperature measurement

The aim of this work was to find the differences between random media by analyzing the properties of the ultrasound signals backscattered from the inhomogeneities. A numerical model is used to generate two types of random media. The first has the randomness in scatterers’ positions and the second has the randomness in the size and acoustical properties of scatterers. The numerical model of wave scattering has been used to simulate the RF (radio frequency) signals caused by the incident pulse traveling as a plane wave. The markers of randomness type differences between the scattering media were obtained with the help of the spectral and wavelet analysis. The effect of differences in randomness type is more spectacular when the wavelet analysis is performed.

spectrogram, scalogram, wavelets, random scattering structure

The aim of the paper is to find links between the dynamics of changes of statistical parameters and changes in spectral properties of the signal envelope of backscattered RF signals during the thermal process. We have shown previously that by using wavelet approximations these tendencies are better recognized in the case of the heating of a phantom sample than in the parallel analysis performed for a full signal envelope. Here we are currently expanding this statement to the case of heating a soft tissue sample in vitro. The shape parameter of the K- distributed random variable is considered as a statistical marker of temperature level changes. Additionally, the spectral properties of different levels of wavelet approximations are calculated and their sensitivity to temperature increase and decrease is demonstrated. Both approaches registering changes in temperature, are used in the case of the pork loin tissue sample in vitro, heated by an ultrasound beam with a different power.

ultrasound echoes, soft tissue sample in vitro, statistical marker of temperaturę rise

The paper contains the wavelet approach to registered backscattered RF signals from two different cases. First, the wavelet analysis has been performed for RF signals registered from soft tissue phantoms. The second case is the wavelet analyses of RF scattered signals from regions of healthy and BCC changed human skin. The three phantoms made from tissuemimicking material with different structures have been measured. We claim that there are visible differences in the statistical parameters of wavelets coefficients of signals between healthy and BCC changed skin regions as well as between phantoms without scatterers and with different number of strong small scatterers.

backscattered RF signals, skin cancer differentiation, scatterers numer, wavelet approximations

The goal of this study was to find the macroscopic characteristics of the random nature of ultrasonic backscattering signals which would be sensitive to the temperature changes. The sample made of Polyvinyl Alcohol – Cryogel (PVA-C, the pre-freezing in one cycle aqueous solution of PVA) was heated in a water bath starting from the room temperature up to the temperature below the soft tissue ablation temperature. The RF signals were collected during the heating/cooling process and the signals envelopes had been calculated. The wavelet approximation of subsequent level worked as a low-pass filter what qualitatively improved the temperature estimating. The latter was realized by observing the variations of the shape parameter of K-distribution. The trend of the shape parameter variation with temperature was calculated including the wavelet decomposition and was compared with the real temperature changes measured by the thermometer. We have found that tracking changes in echoes envelope statistics allows to distinguish between heating and cooling process, and determine the time required to reach maximum temperature.

random signals, Polyvinyl Alcohol – Cryogel, wavelet approximation, temperaturę monitoring

The soft tissue structure possess the multi-scale anatomical inhomogeneities. There are many types of tissues, where one can recognize at least two main scales: millimeter scale and micrometer scale which are the basic scattering structures for an ultrasound wave of the diagnostic frequency range penetrating the tissue. The millimeter scale is due to the existence of quasi-periodic blocks of cells forming semi-regular lobules e.g. in the anatomical units of the liver tissue, and the micrometer scale is formed with many small scatterers like cell walls or large cell nuclei. The breaking of anatomical structure is often caused by the beginning of the cancer process. It is very important and unresolved problem to find any tools in qualitative ultrasound to recognize this two type of scattering. To this end some phantom experiment were performed. 3D thread structure in the form of nylon threads with a thickness of 0.1 mm (or of 0.35 mm) placed at regular periodic structure with distances of 1 mm (or of 1.5 mm) was immersed in the water. This threads structure was used as a model to analyse the properties of ultrasound signal echoes registered with the use of different transducers, both focusing and plane ones. The range of carried frequencies for used transducers were of 1MHz to 15 MHz. Additionally, the threads structure with threads of of 0.35 mm thickness and located in distances of 1.5 mm, immersed in an oil and starch gel instead of the water were · was used to analyse the influence of the background medium properties on the ultrasound backscattering signals. Having measured pulse properties of a transducer and applied the wavelet analysis to the registered signals the identification of the threads positions in space, namely MSS (Mean Scatterer Spacing) was calculated and some aspects of the differences between scattering and reflection phenomena were discussed as a function of ratio between pulse length and geometrical parameters, i.e. threads thickness and distances between them.

The practical aim of this research is to detect the temperature by the selected properties of the backscattered ultrasound signals collected during heating/cooling of the soft tissue sample. The initial data are the raw backscattered signals, RF (radio frequency) signals, which form the two-dimensional matrix. These data are divided according to the regions of interest (ROI) analyzed piece-wise in the following way:
• absolute value of Hilbert transform in each time sample is calculated,
• the approximations with Daubeschies 6 wavelets is performed,
• Kolmogorov-Smirnov distance and Kullback-Laibler divergence between initial ROI statistics and the statistics of the ROI in succesive temperature level are used to visualization of the dynamic temperature changes on the map of the sample volume.

temperaturę detection, non-parametric statistics, backscattered ultrasound, wavelet

For the generation and receiving ultrasonic pulses JSR Ultrasonics DPR 300 Pulser / Receiver and Imasonic Head (center frequency 6MHz, diameter 9 mm, 62 mm focal length) have been used. During the performed experiment three types of phantoms of soft tissue have been used: pure phantom (Phantom A), the second one with glass balls inside with density 6 items per mm3 (Phantom B), the third - 30 balls per mm3 (Phantom C). 10 RF signals were collected for each of the three tissue mimicking phantoms. R interpreter was used, which made automatic import of data, in the packet „wavelet”, reconstruction of signals by Daubechies 6 wavelet family, Multiresolution Analysis i.e. distribution of the different levels of approximations, and at the end the results have been statistically analysed. Histograms and fitting to the Beta and to the normalized and non-normalized Gamma distributions have been performed.
It has been shown that the statistical properties of the signal characteristics include good differentiation between each pattern. They are: the coefficients of non-normalized Gamma distribution in the range of 1-5 levels of approximation, the coefficients of normalized Gamma distribution in the range of 1-7 levels. Beta distributions do not differentiate patterns, as well as higher levels of approximations in the Gamma distributions.

soft tissue phantoms, differentiation of microstructure, backscattered ultrasound, wavelets