Mean Scatterer Spacing Research Articles

Scatterer spacing based on Gabor atoms matched from harmonic ultrasound echoes to improve assessment of microwave‐induced thermal lesions

AbstractBackgroundMicrowave ablation (MWA) is a minimally invasive alternative for the treatment of unresectable liver tumors. To verify the effectiveness and safety of MWA, it is critical to measure the temperature variation and assess the regions of the microwave‐induced thermal lesions.PurposeRecent studies have indicated that the locations of optimally matched Gabor atoms (LOMGA) from ultrasound radiofrequency (RF) echo signals allow accurate and stable scatterer spacing estimation. Herein, a harmonic‐based LOMGA method is proposed to estimate the scatterer spacing for improving the assessment of microwave‐induced thermal lesions.MethodsThe mean scatterer spacing (MSS) is estimated via the LOMGA method incorporating the selection of concise atoms from separated second‐harmonic RF echo signals with the pulse‐inversion algorithm for thermal lesion evaluation. In vitro experiments, 10 fresh porcine liver samples were ablated at different time nodes during the ablation period, and 200 sets of second‐harmonic and fundamental RF echo signals were randomly selected from the regions of interest in the coagulated liver samples for MSS estimation. The means and standard deviations of the MSSs, as well as the linear regression for the mean MSSs, were calculated from fundamental and second‐harmonic signals for comparison and evaluation, the receiver operating characteristic (ROC) curves for the 200 sets of fundamental‐based and harmonic‐based MSS estimates from the 10 liver samples at five pairs of adjacent time nodes were calculated, and one‐way analysis of variance (ANOVA) tests were performed for the five pairs of adjacent time nodes. The fundamental and harmonic‐based p‐values in the ANOVA tests and the areas under the ROC curves (AUCs) were calculated to statistically analyze the differences in the MSSs between adjacent time nodes.ResultsThe harmonic‐based increments in the intensity variation and coherent components were larger than the fundamental‐based increments with the increasing ablation time. The harmonic‐based MSSs from the 10 liver samples at five pairs of adjacent time nodes were found to be highly statistically significant (p < 0.01). Thus, the harmonic‐based MSSs had greater variations. Compared with the fundamental‐based results, for the five preset ST values, the average increment in the harmonic‐based mean slopes was 69.22% and the average decrement in the mean standard deviations was 11.67% for the linear‐fitting MSS results, and the results were statistically significant (p < 0.05).ConclusionHarmonic‐based MSSs are more sensitive and robust to variations in coagulated tissues, which is advantageous for the assessment of microwave‐induced thermal lesions.

The mean scatterer spacing estimation with the Gabor atoms selected based on the Nakagami distribution

Abstract Mean scatterer spacing (MSS) is a quantitative signature for disease diagnosis and tissue characterization. However, it is difficult to objectively extract the coherent components from the ultrasound RF echo signals to improve the accuracy performance of the MSS estimation. In the present study, a novel MSS estimation method with the matched Gabor atoms selected based on Nakagami parameters is proposed. Firstly, the ultrasound signals are decomposed into a series of Gabor atoms, and the envelopes of the residual signals are fitted with the Nakagami distribution. Then, the second order difference (SOD) of the shape parameters in Nakagami distribution is calculated to select the optimal atoms to match the coherent components. Finally, the locations of the selected Gabor atoms are used to estimate the MSS. In order to evaluate the performance of the proposed method, the simulated RF echo signals are modelled based on four regular degrees of the scatterer distributions, and then the optimally matched atoms are selected to estimate MSSs. The results based on simulated signals demonstrate that the propose method can provide accurate MSS estimation, which are advantageous for improving quantitative diagnosis of diseases and tissue characterization

Quadratic versus linear models to estimate the mean scattering spacing as a function of temperature in ex-vivo tissue

Previous works have shown the feasibility of temperature estimation during ultrasonic therapy using pulse-echo diagnostic ultrasound. These methods are based on the measurement of thermally induced changes in backscattered RF echoes due to thermal expansion and changes in ultrasonic velocity. They assume a joint contribution of these two parameters and a linear dependence with temperature. In this work, the contributions of velocity changes and thermal expansion to the evolution of the mean scatterer spacing of ex vivo bovine skeletal muscle tissue samples were decoupled. This was achieved by employing an experimental setup which allows measuring the absolute velocity value, using the through-transmission technique in a direct transmission configuration. The mean-scatterer spacing was estimated from spectral analysis of the backscattered signals obtained in pulse-echo mode. We propose a quadratic model of the thermal expansion coefficient to fit the evolution of the mean-scatterer spacing with temperature. The temperature increase estimated by the linear model, in the range of 29.5–47 °C, presents a percentage error (mean square error) of 11 %, while for the quadratic model the error is 4.8 %.

Mean Scatterer Spacing Estimation Using Cepstrum-Based Continuous Wavelet Transform.

The goal of this study was to develop an ultrasound (US) scatterer spacing estimation method using an enhanced cepstral analysis based on continuous wavelet transforms (CWTs). Simulations of backscattering media containing periodic and quasi-periodic scatterers were carried out to test the developed algorithm. Experimental data from HT-29 pellets and in vivo PC3 tumors were then used to estimate the mean scatterer spacing. For simulated media containing quasi-periodic scatterers at 1-mm and 100- [Formula: see text] spacing with 5% positional variation, the developed algorithm yielded a spacing estimation error of ~1% for 25- and 55-MHz US pulses. The mean scatterer spacing of HT-29 cell pellets (31.97 [Formula: see text]) was within 3% of the spacing obtained from histology and agreed with the predicted spacing from simulations based on the same pellets for both frequencies. The agreement extended to in vivo PC3 tumors estimation of the spacing with a variance of 1.68% between the spacing derived from the tumor histology and the application of the CWT to the experimental results. The developed technique outperformed the traditional cepstral methods as it can detect nonprominent peaks from quasi-random scatterer configurations. This work can be potentially used to detect morphological tissue changes during normal development or disease treatment.

Classification of breast lesions using quantitative ultrasound biomarkers

Quantitative ultrasound (QUS) parameters are gaining attention recently as non-invasive biomarkers for soft tissue characterization. In this work, we use two QUS parameters, namely effective scatterer diameter (ESD) and mean scatterer spacing (MSS), for binary classification of breast lesions. To produce improved classification results, we propose a modified frequency-domain technique for ESD estimation of breast tissues from the diffuse component of backscattered radio-frequency (RF) data. Ensemble empirical mode decomposition (EEMD) is performed to separate the diffuse component from the coherent component of the backscattered RF data. A non-parametric Kolmogorov–Smirnov (K-S) test is employed for automatic selection of the intrinsic mode functions (IMFs). A novel multi-step system effect minimization process is also used. The ESD is estimated using a novel nearest neighborhood average regression line fitting (NNARLF) algorithm. Furthermore, we use an ameliorated EEMD domain autoregressive (AR) spectral estimation technique for MSS estimation. On using the ESD for binary classification of 139 lesions, we obtain high sensitivity, specificity, accuracy values of 95.45%, 95.79%, and 95.68%, respectively. The area under the receiver operating characteristics (ROC) curve is 0.95. On combining ESD with MSS we obtain even more improved sensitivity, specificity, and accuracy values of 97.73%, 95.79%, and 96.40%, respectively. The area under the ROC also increases to 0.97. This high classification performance demonstrates the potential of the use of these QUS parameters as non-invasive biomarkers for breast cancer detection.

EEMD Domain AR Spectral Method for Mean Scatterer Spacing Estimation of Breast Tumors From Ultrasound Backscattered RF Data

We present a novel method for estimating the mean scatterer spacing (MSS) of breast tumors using ensemble empirical mode decomposition (EEMD) domain analysis of deconvolved backscattered radio frequency (RF) data. The autoregressive (AR) spectrum from which the MSS is estimated is obtained from the intrinsic mode functions (IMFs) due to regular scatterers embedded in RF data corrupted by the diffuse scatterers. The IMFs are chosen by giving priority to the presence of an enhanced fundamental harmonic and the presence of a greater number of higher harmonics in the AR spectrum estimated from the IMFs. The AR model order is chosen by minimizing the mean absolute percentage error (MAPE) criterion. In order to ensure that the backscattered data is indeed from a source of coherent scattering, we begin by performing a non-parametric Kolmogorov-Smirnov (K-S) classification test on the backscattered RF data. Deconvolution of the backscattered RF data, which have been classified by the K-S test as sources of significant coherent scattering, is done to reduce the system effect. EEMD domain analysis is then performed on the deconvolved data. The proposed method is able to recover the harmonics associated with the regular scatterers and overcomes many problems encountered while estimating the MSS from the AR spectrum of raw RF data. Using our technique, a mean absolute percentage error (MAPE) of 5.78% is obtained while estimating the MSS from simulated data, which is lower than that of the existing techniques. Our proposed method is shown to outperform the state of the art techniques, namely, singular spectrum analysis, generalized spectrum (GS), spectral autocorrelation (SAC), and modified SAC for different simulation conditions. The MSS for in vivo normal breast tissue is found to be 0.69 ± 0.04 mm; for benign and malignant tumors it is found to be 0.73 ± 0.03 and 0.79 ± 0.04 mm, respectively. The separation between the MSS values of normal and benign tissues for our proposed method is similar to the separations obtained for the conventional methods, but the separation between the MSS values for benign and malignant tissues for our proposed method is slightly higher than that for the conventional methods. When the MSS is used to classify breast tumors into benign and malignant, for a threshold-based classifier, the increase in specificity, accuracy, and area under curve are 18%, 19%, and 22%, respectively, and that for statistical classifiers are 9%, 13%, and 19%, respectively, from that of the next best existing technique.

A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing.

Tissues exhibiting quasi-periodic structures can be modeled as a collection of diffuse scatterers and coherent scatterers. The mean scatterer spacing (MSS) of coherent and quasi-periodic components is directly related to tissue microstructure and has become an important quantitative ultrasound (QUS) parameter in the characterization of quasi-periodic tissues. In this paper, a review of the literature on the development of MSS as a QUS parameter was conducted. First, a unified theoretical background of MSS estimates was provided. Then, the application of MSS estimates was summarized with respect to liver, spleen, breast, bone, muscle, and other tissues. MSS estimation techniques were applied to (a) the diagnosis of hepatitis, liver fibrosis and cirrhosis, and lesions in tissues such as liver, breast, and spleen; (b) the differentiation between benign and malignant breast tumors, and the grading of breast cancer; (c) the detection of cancellous bone; and (d) the monitoring of the efficacy of treatments such as thermal ablation, with various levels of success. Future developments were also discussed in terms of real-time implementation of MSS estimates, local MSS estimation, relationship of MSS to other QUS parameters, combination of MSS with other QUS parameters, in vivo validation of MSS estimates, MSS parametric imaging, and three-dimensional ultrasound tissue characterization.

Comparative study of periodicity estimation methods using ultrasonic signals

Introduction Various signal-processing techniques have been proposed to extract quantitative information about internal structures of tissues from the original radio frequency (RF) signals instead of an ultrasound image. The quantifiable parameter called the mean scatterer spacing (MSS) can be useful to detect changes in the quasi-periodic microstructure of tissues such as the liver or the spleen, using ultrasonic signals. Methods We evaluate and compare the performance of three classic methods of spectral estimation to calculate the MSS without operator intervention: Tufts-Kumaresan, SAC (Spectral Autocorrelation) and MUSIC (MUltiple SIgnal Classification). Initially the evaluations were performed with 10,000 signals simulated from a model in which the variables of interest are controlled, and then, real signals from sponge phantoms were used. Results For the simulated signals, the performance of all three methods decreased with increasing Ad or jitter levels. For the sponges, none of the methods accurately estimated the pore size. Conclusion For the simulated signals, Tufts-Kumaresan had the lowest performance, whereas SAC and MUSIC had similar results. For sponges, only Tufts-Kumaresan was able to detect the increase in the size of the pores of the sponge, although most often, it estimated sizes larger than expected.

Can early hepatic fibrosis stages be discriminated by combining ultrasonic parameters?

In this study, we put forward a new approach to classify early stages of fibrosis based on a multiparametric characterization using backscatter ultrasonic signals. Ultrasonic parameters, such as backscatter coefficient (Bc), speed of sound (SoS), attenuation coefficient (Ac), mean scatterer spacing (MSS), and spectral slope (SS), have shown their potential to differentiate between healthy and pathologic samples in different organs (eye, breast, prostate, liver). Recently, our group looked into the characterization of stages of hepatic fibrosis using the parameters cited above. The results showed that none of them could individually distinguish between the different stages. Therefore, we explored a multiparametric approach by combining these parameters in two and three, to test their potential to discriminate between the stages of liver fibrosis: F0 (normal), F1, F3, and/without F4 (cirrhosis), according to METAVIR Score. Discriminant analysis showed that the most relevant individual parameter was Bc, followed by SoS, SS, MSS, and Ac. The combination of (Bc, SoS) along with the four stages was the best in differentiating between the stages of fibrosis and correctly classified 85% of the liver samples with a high level of significance (p<0.0001). Nevertheless, when taking into account only stages F0, F1, and F3, the discriminant analysis showed that the parameters (Bc, SoS) and (Bc, Ac) had a better classification (93%) with a high level of significance (p<0.0001). The combination of the three parameters (Bc, SoS, and Ac) led to a 100% correct classification. In conclusion, the current findings show that the multiparametric approach has great potential in differentiating between the stages of fibrosis, and thus could play an important role in the diagnosis and follow-up of hepatic fibrosis.

Golay improvement of the robustness of mean scatterer spacing measurement with ultrasonic backscattering.

Mean scatterer spacing (MSS) estimated from ultrasonic backscattering is of valuable information for tissue characterization. However, low ultrasound frequency, sound attenuation, and diffuse scattering significantly disturb the current MSS measurement methods. The aim of this study is to improve MSS measurement with Coded Excitation (CE) enhanced cepstrum estimation. The study proposes a Golay code-based cepstrum estimation and uses an equivalent Faran cylinder model of cancellous bone. By solving the elastic wave equation, ultrasonic backscattering signals were obtained through simulations. The adopted ultrasonic excitation is 0.9 MHz and is coded with 4-bit complementary sequences, which is modulated by employing a sinusoidal signal. Also, white Gaussian noise of 60 dB, 55 dB, and 45 dB was added to the backscattering data to study the robustness of the proposed method. Simulation results show that the Golay-based method successfully improved ultrasound energy transmission into the tissue and effectively suppressed interference peaks while identifying the right peaks with permitted errors. In addition, the robustness against noise was enhanced.

Variation of ultrasound image lateral spectrum with assumed speed of sound and true scatterer density

One method of estimating sound speed in diagnostic ultrasound imaging consists of choosing the speed of sound that generates the sharpest image, as evaluated by the lateral frequency spectrum of the squared B-mode image. In the current work, simulated and experimental data on a typical (47mm aperture, 3.3–10.0MHz response) linear array transducer are used to investigate the accuracy of this method. A range of candidate speeds of sound (1240–1740m/s) was used, with a true speed of sound of 1490m/s in simulations and 1488m/s in experiments. Simulations of single point scatterers and two interfering point scatterers at various locations with respect to each other gave estimate errors of 0.0–2.0%. Simulations and experiments of scatterer distributions with a mean scatterer spacing of at least 0.5mm gave estimate errors of 0.1–4.0%. In the case of lower scatterer spacing, the speed of sound estimates become unreliable due to a decrease in contrast of the sharpness measure between different candidate speeds of sound. This suggests that in estimating speed of sound in tissue, the region of interest should be dominated by a few, sparsely spaced scatterers. Conversely, the decreasing sensitivity of the sharpness measure to speed of sound errors for higher scatterer concentrations suggests a potential method for estimating mean scatterer spacing.

Alterations in Ultrasound Scattering Following Thermal Ablation in ex vivo Bovine Liver.

Thermal ablation is a minimally invasive cancer treatment which has been rapidly gaining clinical acceptance. It is well known that thermal ablation increases the acoustic attenuation and shear modulus of tissue. In this work, we examine changes to the spatial distribution of scatterers in liver tissue following thermal ablation. Acoustic scatterers within liver tissue have frequently been modeled as pseudo-periodic. The positions of pseudo-periodic scatterers have been Gamma distributed along the beam dimension, and these scatterers are characterized by their mean scatterer spacing (MSS). Prior work have demonstrated significant changes in MSS due to diffuse liver disease, such as steatosis progressing to cirrhosis. However, relatively few results have been reported regarding changes in MSS following thermal ablation. In this study, we estimated MSS in ex vivo bovine liver by detecting local maxima in spectral coherence functions calculated using Thomson's multi-taper method. We examined a large number of uncorrelated regions of interest recorded from five normal bovine livers (~300 images from each animal). We also examined a large number of ROI's from five bovine livers following thermal coagulation. All bovine livers were obtained from a commercial meat production facility immediately following animal sacrifice and imaged within 12 hours. Thermal coagulation was induced by heating liver in saline water baths at 80° C for 45 minutes. For normal, unheated liver an MSS of approximately 1.5 mm was estimated. Following thermal ablation, an MSS of approximately 0.5 mm in thermally coagulated tissue was obtained. Frequently, studies estimating MSS in liver tissue provide an MSS estimate regardless of the state of tissue. Authors rarely present what their MSS estimation algorithm would produce if it were applied to tissue which is better modeled as a collection of uniformly, randomly distributed scatterers lacking periodicity. In this study, we found that thermal coagulation results in a loss of periodicity. The MSS of 0.5 mm corresponds to the value that a spectral coherence-based MSS algorithm would produce if presented with a signal that was generated from uniform, randomly distributed scatterers.

Mean scatterer spacing estimation in normal and thermally coagulated ex vivo bovine liver.

The liver has been hypothesized to have a unique arrangement of microvasculature that presents as an arrangement of quasiperiodic scatterers to an interrogating ultrasound pulse. The mean scatterer spacing (MSS) of these quasiperiodic scatterers has been proposed as a useful quantitative ultrasound biomarker for characterizing liver tissue. Thermal ablation is an increasingly popular method for treating hepatic tumors, and ultrasonic imaging approaches for delineating the extent of thermal ablation are in high demand. In this work, we examine the distribution of estimated MSS in thermally coagulated bovine liver and normal untreated bovine liver ex vivo. We estimate MSS by detecting local maxima in the spectral coherence function of radio frequency echoes from a clinical transducer, the Siemens VFX 9L4 transducer operating on an S2000 scanner. We find that normal untreated bovine liver was characterized by an MSS of approximately 1.3 mm. We examined regions of interest 12 mm wide laterally, and ranging from 12 mm to 18 mm axially, in 2 mm increments. Over these parameters, the mode of the MSS estimates was between 1.25 and 1.37 mm. On the other hand, estimation of MSS in thermally coagulated liver tissue yields a distribution of MSS estimates whose mode varied between 0.45 and 1.0 mm when examining regions of interest over the same sizes. We demonstrate that the estimated MSS in thermally coagulated liver favors small spacings because the randomly positioned scatterers in this tissue are better modeled as aperiodic scatterers. The submillimeter spacings result from the fact that this was the most probable spacing to be estimated if the discretely sampled spectral coherence function was a uniformly random two-dimensional function.

Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties

Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum--midband fit, slope, and 0-MHz-intercept--were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004). Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes.

Ultrasound signal wavelet analysis to quantify the microstructures of normal and frozen tissues in vitro

Cryosurgery has a number of advantages that make it particularly appealing in the treatment of liver cancer. However, a major problem for the wide clinical adoption of hepatic cryosurgery is the lack of a cost effective high resolution imaging way which is capable of both performing precise monitoring of the freezing process in situ and evaluating the postoperative effects after surgery. The mean scatterer spacing has been found to be an important parameter for describing the ultrasonic scattering and characterization of biological tissues. However, its potential values in the evaluation of cryosurgical effects of tissues reserved unclear so far. Here, we investigated the wavelet analysis to estimate the mean scatterer spacing parameter in normal and freeze–thawed tissues on porcine livers in vitro. The experimental results carried out at 10MHz using weakly focused pulse-echo signal element transducer indicated that the mean scatterer spacing in normal liver tissues is 1.12±0.13mm whereas it is 1.67±0.25mm in several pre-frozen and then thawed tissues. These results disclosed the good correlation between the wavelet data and microstructures of the normal or thawed tissues, and hence demonstrated that the wavelet analysis holds promise to be used as an effective method for the characterization of thawed tissues scatterer spacing. The present method offers a potential pragmatic strategy for monitoring the transition zone between frozen and unfrozen tissues during the surgical therapy, and evaluating postoperative effects.

Mean scatterer spacing estimation using multi-taper coherence.

It has been hypothesized that estimates of mean scatterer spacing are useful indicators for pathological changes to the liver. A commonly employed estimator of the mean scatterer spacing is the location of the maximum of the collapsed average of coherence of the ultrasound radio-frequency signal. To date, in ultrasound, estimators for this quantity have been calculated with a single taper. Using frequency-domain Monte Carlo simulations, we demonstrate that multi-taper estimates of coherence are superior to single-taper estimates for predicting mean scatterer spacing. Scattering distributions were modeled with Gamma-distributed scatterers for fractional standard deviations in scatterer spacings of 5, 10, and 15% at a mean scatterer spacing of 1 mm. Additionally, we demonstrate that we can distinguish between ablated liver tissue and unablated liver tissue based on signal coherence. We find that, on the average, signal coherence is elevated in the liver relative to signal coherence of received echoes from thermally ablated tissue. Additionally, our analysis indicates that a tissue classifier utilizing the multi-taper estimate of coherence has the potential to distinguish between ablated and unablated tissue types better than a single-taper estimate of coherence. For a gate length of 5 mm, we achieved an error rate of only 8.7% when sorting 23 ablated and 23 unablated regions of interest (ROIs) into classes based on multi-taper calculations of coherence.

Ultrasonic evaluation of microwave-induced thermal lesions based on wavelet analysis of mean scatterer spacing

The microwave ablation has become an important manner for tumor treatment. In this paper, we proposed a new method for evaluation of microwave-induced thermal lesions using the wavelet analysis of the mean scatterer spacing (MSS). First, the ultrasonic radiofrequency (RF) data of normal and coagulated porcine liver tissues was collected through the temperature-controlled water bath heating experiments. The convex array ultrasound probe with a center frequency of 3.5MHz was used. Second, the wavelet analysis was used to compute the MSS of normal and coagulated porcine liver tissues, respectively. Finally, the microwave-induced thermal lesions were detected based on the differences in the MSS between normal tissues and thermal lesions. Eighteen cases of microwave ablation experiments and 20 cases of water bath heating experiments were conducted on fresh porcine liver samples. The MSS of normal porcine liver tissues was 1.15±0.12mm, and the MSS of coagulated porcine liver tissues was 0.93±0.07mm. Six cases of thermal lesions were compared between the MSS-detected area and the caliper-measured area, and the MSS-detected area had an error of (13.55±5.29) %. The experimental results indicated that the proposed method could be used in preliminary detection and evaluation of microwave-induced thermal lesions.

Detecção automática do espaçamento médio de meios periódicos por sinais ultrassônicos retroespalhados

The interpretation of ultrasound imaging is essentially visual and qualitative, so there are important inter and intra-observer variations. Quantification methods aim at decreasing this dependency. Among those, the quantification of the Mean Scatterer Spacing (MSS) can be useful to detect changes in the microstructure of quasi-periodic tissues, such as liver or spleen. This study evaluated the following methods of spectral estimation for calculating the MSS (without requiring operator intervention): BURG, WIENER and MUSIC. The aim is to compare their potential for automatic estimation of MSS from ultrasonic scattering signals. Initially, the evaluation has been carried out using 10,000 simulated signals, with the aim of studying the behavior of the methods using a model in which the variables of interest can be controlled. Then, the methods have been applied to real signals of nylon phantoms immersed in water. The BURG method could not estimate the spacing of US phantom signals, presenting results similar to the other methods only for simulated signals. The WIENER method for the simulated signals was in second place in terms of percentage of success, when considering signals from the phantoms. The subspace method MUSIC had the best performance from all three methods.

High-order statistic for mean scatterer spacing estimation for human tissues characterization

The mean scatterer spacing (MSS) has been identified as an important parameter for characterizing human tissues presenting a semi-regular lattice texture. Most of the work in this area involves the uses of an estimated power spectrum density (PSD) of an autoregressive (AR) model to deduce the MSS from backscattered ultrasonic signals. In this paper, we propose a new method of MSS estimation using the third and fourth-order cumulants, named here TFOCs, of the AR parametric model. The results obtained by this new method is compared with those obtained using PSD.

Characterization of in vitro healthy and pathological human liver tissue periodicity using backscattered ultrasound signals

This work studied the periodicity of in vitro healthy and pathologic liver tissue, using backscattered ultrasound (US) signals. It utilized the mean scatterer spacing (MSS) as a parameter of tissue characterization, estimated by three methods: the spectral autocorrelation (SAC), the singular spectrum analysis (SSA) and the quadratic transformation method (SIMON). The liver samples were classified in terms of tissue status using the METAVIR scoring system. Twenty tissue samples were classified in four groups: F0, F1, F3 and F4 (five samples for each). The Kolmogorov-Smirnov test (applied on group pairs) resulted as nonsignificant ( p > 0.05) for two pairs only: F1/F3 (for SSA) and F3/F4 (for SAC). A discriminant analysis was applied using as parameters the MSS mean ( MSS) and standard deviation ( σMSS), the estimates histogram mode ( mMSS), and the speed of US ( mc foie ) in the medium, to evaluate the degree of discrimination among healthy and pathologic tissues. The better accuracy ( Ac) with SAC (80%) was with parameter group ( MSS, σMSS, mc foie ), achieving a sensitivity ( Ss) of 92.3% and a specificity ( Sp) of 57.1%. For SSA, the group with all four parameters showed an Ac of 75%, an Ss of 78.6% and an Sp of 66.70%. SIMON obtained the best Ac of all (85%) with group ( MSS, mMSS, mc foie ), an Ss of 100%, but with an Sp of 50%. (E-mail: wagner@peb.ufrj.br)