Application of autoregressive spectral analysis to cepstral estimation of mean scatterer spacing

Effects of scattering on ultrasound wave transmission through bioinspired scaffolds

Enhancing tissue growth in scaffolds using ultrasound waves while maintaining the structural integrity of the scaffolds is a challenging problem. Previous studies have primarily focused on the effect of ultrasound waves directly on the tissue, but how the ultrasound wave interacts with the scaffold needs to be further understood, which will have a significant effect on the response of tissue to mechanical stimulation. In this study we investigate how ultrasound wave transmission differs between scaffolds with uniform pore shapes (triangle, square, rectangle, hexagon) and a bioinspired scaffold with higher structural integrity that is inspired from the atomic structure of hydroxyapatite which is a primary component of bone. We use finite element method and ultrasound experiments on 3D-printed scaffolds composed of Acrylonitrile butadiene styrene (ABS) with constant porosity to predict the effect of pore shape and wave signal frequency in the range of 1-20 MHz on acoustic wave scattering and transmission. We find that the pore shape of the scaffold affects the magnitude of ultrasound transmission even when porosity is constant, and that the bioinspired scaffolds can allow as much as 67% more wave transmission compared to scaffolds with rectangular or square pore shapes at 1 MHz frequency. Triangular and hexagonal pores are also found to produce more nonuniform transmitted wavefronts compared to the square and rectangular pores. Peak density is defined as the number of local extrema of the transmitted wave frequency power spectrum and measures the uniformity of the transmitted wave. We find that a higher peak density value for the bioinspired scaffold due to its nonsymmetric structure further produces more nonuniform wave scattering. The results of this study are important for designing bioinspired tissue scaffold geometries to control ultrasound wave penetration and to enhance mechanical stimulation for tissue growth and will also aid in the ultrasonic characterization of porous structures based on changes in pore geometry.

Analytic Global Regularized Backscatter Quantitative Ultrasound

Although a variety of techniques have been developed to reduce the appearance of B-mode speckle, quantitative ultrasound (QUS) aims at extracting the hidden properties of the tissue. Herein, we propose two novel techniques to accurately and precisely estimate two important QUS parameters, namely, the average attenuation coefficient and the backscatter coefficient. Both the techniques optimize a cost function that incorporates data and continuity constraint terms, which we call AnaLytical Global rEgularized BackscatteR quAntitative ultrasound (ALGEBRA). We propose two versions of ALGEBRA, namely, 1-D- and 2-D-ALGEBRA. In 1-D-ALGEBRA, the regularized cost function is formulated in the axial direction, and the QUS parameters are calculated for one line of radio frequency (RF) echo data. In 2-D-ALGEBRA, the regularized cost function is formulated for the entire image, and the QUS parameters throughout the image are estimated simultaneously. This simultaneous optimization allows 2-D-ALGEBRA to "see" all the data before estimating the QUS parameters. In both the methods, we efficiently optimize the cost functions by casting it as a sparse linear system of equations. As a result of this efficient optimization, 1-D-ALGEBRA and 2-D-ALGEBRA are, respectively, 600 and 300 times faster than optimization using the dynamic programming (DP) method previously proposed by our group. In addition, the proposed technique has fewer input parameters that require manual tuning. Our results demonstrate that the proposed ALGEBRA methods substantially outperform least-square and DP methods in estimating the QUS parameters in phantom experiments.

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results

BACKGROUND

Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting.

METHODS

Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models.

RESULTS

Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively.

CONCLUSION

QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.

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.

Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners

Quantitative ultrasound (QUS) techniques have been demonstrated to detect cell death in vitro and in vivo. Recently, multi-feature classification models have been incorporated into QUS texture-feature analysis methods to increase further the sensitivity and specificity of detecting treatment response in locally advanced breast cancer patients. To effectively incorporate these analytic methods into clinical applications, QUS and texture-feature estimations should be independent of data acquisition systems. The study here investigated the consistencies of QUS and texture-feature estimation techniques relative to several factors. These included the ultrasound system properties, the effects of tissue heterogeneity and the effects of these factors on the monitoring of response to neoadjuvant chemotherapy. Specifically, tumour-response-detection performance based on QUS and texture parameters using two clinical ultrasound systems was compared. Observed variations in data between the systems were small and the results exhibited good agreement in tumour response predictions obtained from both ultrasound systems. The results obtained in this study suggest that tissue heterogeneity was a dominant feature in the parameters measured with the two different ultrasound systems; whereas differences in ultrasound system beam properties only exhibited a minor impact on texture features. The McNemar statistical test performed on tumour response prediction results from the two systems did not reveal significant differences. Overall, the results in this study demonstrate the potential to achieve reliable and consistent QUS and texture-based analyses across different ultrasound imaging platforms.

Locations of optimally matched Gabor atoms from ultrasound RF echoes for inter-scatterer spacing estimation

BACKGROUND AND OBJECTIVE

The resolvable scatterer spacing related to biological tissue microstructures is a quantitative signature used for the disease diagnosis and tissue classification. In the present study, a method by locating optimally matched Gabor atoms (LOMGA) from ultrasound RF echo signals is proposed to improve the inter-scatterer spacing (ISS) estimation.

METHOD

A series of Gabor atoms are obtained from the signals with a matching pursuit algorithm. Then, the optimum atoms highly correlated with the coherent components are automatically selected according to the second-order difference of the reconstructed signal-to-residue ratio. The distances between the locations of adjacent atoms are applied to estimate the ISSs. In the simulation experiments, four regular degrees of the scatterer distributions are modeled with the Gamma distribution. One hundred sets of ultrasound RF echo signals are simulated based on the regular and diffuse scatterer distributions, and then combined to generate signals with preset coherent-to-diffuse ratios (CDRs). The accuracy performance of the LOMGA method is compared with that based on wavelet transform (WT) algorithm. In the microwave ablation experiments, the ultrasound RF echo signals of the region of interest (ROI) are collected from the normal and coagulated porcine liver tissues. The means and standard deviations of the LOMGA-based ISSs are compared with the WT-based results.

RESULTS

The results based on simulated signals with CDRs from 10 dB to -10 dB demonstrate that the proposed method improves the estimation accuracies of the mean ISSs by 5.10%, 9.00%, 19.80%, and 23.82%, and reduces the mean standard deviations by 27.20%, 22.50%, 11.50%, and 4.49% more than the WT method for the four regularities, respectively. The performance of the LOMGA method is also verified with the ultrasound RF echo signals from ex vivo porcine liver tissues in microwave ablation experiments.

CONCLUSIONS

It is concluded that the LOMGA method can provide more accurate and stable ISS estimation, which improves the performance of the tissue characterization with ISS-based quantitative ultrasound.

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim:

We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer.

Materials & methods:

Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers.

Results:

There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%.

Conclusion:

Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori.

In this study, quantitative ultrasound (QUS) and machine-learning classification was used to predict treatment outcomes in head and neck cancer patients. Metastatic lymph nodes in the neck were scanned using conventional frequency ultrasound (US). Quantitative data were collected from the US-radiofrequency signal a priori. Machine-learning classification models were computed using QUS features; these included the linear fit parameters of the power spectrum, and second-order texture parameters of the QUS parametric images. Treatment outcomes were measured based on radiological response. Patients were classified into binary groups: radiologic complete response (CR) or radiological partial response (PR), which was assessed 3 months following treatment. Initial results demonstrate high accuracy (%Acc = 87.5%) for predicting radiological response. The results of this study suggest that QUS can be used to predict head and neck cancer response to radiotherapy a priori.

The Ability of Ultrasonic Backscatter Parametric Imaging to Characterize Bovine Trabecular Bone

The ultrasonic backscatter technique holds the promise of characterizing bone density and microstructure. This paper conducts ultrasonic backscatter parametric imaging based on measurements of apparent integrated backscatter (AIB), spectral centroid shift (SCS), frequency slope of apparent backscatter (FSAB), and frequency intercept of apparent backscatter (FIAB) for representing trabecular bone mass and microstructure. We scanned 33 bovine trabecular bone samples using a 7.5 MHz focused transducer in a 20 mm × 20 mm region of interest (ROI) with a step interval of 0.05 mm. Images based on the ultrasonic backscatter parameters (i.e., AIB, SCS, FSAB, and FIAB) were constructed to compare with photographic images of the specimens as well as two-dimensional (2D) μ-CT images from approximately the same depth and location of the specimen. Similar structures and trabecular alignments can be observed among these images. Statistical analyses demonstrated that the means and standard deviations of the ultrasonic backscatter parameters exhibited significant correlations with bone density (|R| = 0.45-0.78, p < 0.01) and bone microstructure (|R| = 0.44-0.87, p < 0.001). Some bovine trabecular bone microstructure parameters were independently associated with the ultrasonic backscatter parameters (ΔR² = 4.18%-44.45%, p < 0.05) after adjustment for bone apparent density (BAD). The results show that ultrasonic backscatter parametric imaging can provide a direct view of the trabecular microstructure and can reflect information about the density and microstructure of trabecular bone.

Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models

PURPOSE

The purpose of this study was to develop computational algorithms to best determine tumor responses early after the start of neoadjuvant chemotherapy, based on quantitative ultrasound (QUS) and textural analysis in patients with locally advanced breast cancer (LABC).

METHODS

A total of 100 LABC patients treated with neoadjuvant chemotherapy were included in this study. Breast tumors were scanned with a clinical ultrasound system prior to treatment, during the first, fourth and eighth weeks of treatment, and prior to surgery. QUS parameters were calculated from ultrasound radio frequency data within tumor regions. Texture features were extracted from each QUS parametric map. Patients were classified into two groups based on identified clinical/pathological response: responders and non-responders. In order to differentiate treatment responders, three multi-feature response classification algorithms, namely a linear discriminant, a k-nearest-neighbor and a nonlinear support vector machine classifier were compared.

RESULTS

All algorithms distinguished responders and non-responders with accuracies ranging between 68% and 92%. In particular, support vector machine performed the best in differentiating responders from non-responders with accuracies of 78%, 90% and 92% at weeks 1, 4 and 8 after the start of treatment, respectively. The most relevant features in separating the two response groups at early stages (weeks 1and 4) were texture features and at a later stage (week 8) were mean QUS parameters, particularly ultrasound backscatter intensity-based parameters.

CONCLUSION

An early stage treatment response prediction model developed by quantitative ultrasound and texture analysis combined with modern computational methods permits offering effective alternatives to standard treatment for refractory patients.

A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks

We demonstrate the clinical utility of combining quantitative ultrasound (QUS) imaging of the breast with an artificial neural network (ANN) classifier to predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) administration prior to the start of treatment.

Using a 6 MHz ultrasound system, radiofrequency (RF) ultrasound data were acquired from 100 patients with biopsy-confirmed locally advanced breast cancer prior to the start of NAC. Quantitative ultrasound mean parameter intensity and texture features were computed from the tumour core and margin, and were compared to the clinical/pathological response and 5-year recurrence-free survival (RFS) of patients. A multi-parametric QUS model in conjunction with an ANN classifier predicted patient response with 96 ± 6% accuracy, and a 0.96 ± 0.08 area under the receiver operating characteristic curve (AUC), compared to 65 ± 10 % accuracy and 0.67 ± 0.14 AUC achieved using a K-Nearest Neighbour (KNN) algorithm. A separate ANN model predicted patient RFS with 85 ± 7% accuracy, and a 0.89 ± 0.11 AUC, whereas the KNN methodology achieved a 58 ± 6 % accuracy and a 0.64 ± 0.09 AUC.

The application of ANN for classifying patient response based on tumour QUS features performs well in terms of predicting response to chemotherapy. The findings here provide a framework for developing personalized a priori chemotherapy selection for patients that are candidates for NAC, potentially resulting in improved patient treatment outcomes and prognosis.

Insights into photoacoustic speckle and applications in tumor characterization

In ultrasound imaging, fully-developed speckle arises from the spatiotemporal superposition of pressure waves backscattered by randomly distributed scatterers. Speckle appearance is affected by the imaging system characteristics (lateral and axial resolution) and the random-like nature of the underlying tissue structure. In this work, we examine speckle formation in acoustic-resolution photoacoustic (PA) imaging using simulations and experiments. Numerical and physical phantoms were constructed to demonstrate that PA speckle carries information related to unresolved absorber structure in a manner similar to ultrasound speckle and unresolved scattering structures. A fractal-based model of the tumor vasculature was used to study PA speckle from unresolved cylindrical vessels. We show that speckle characteristics and the frequency content of PA signals can be used to monitor changes in average vessel size, linked to tumor growth. Experimental validation on murine tumors demonstrates that PA speckle can be utilized to characterize the unresolved vasculature in acoustic-resolution photoacoustic imaging.

A method to locate spatial distribution of scattering centers from ultrasonic backscatter signal

The purpose of this work is to find a method to locate the scattering centers in spatial domain; by using this information, the mean scatter spacing (MSS) can be estimated, and the spatial information is the one-dimensional imaging of scattering centers. This paper presents a method that can locate the scattering centers in spatial domain robustly and automatically. By incorporating it with fast Fourier transformation, the MSS can be estimated. The three foremost processes, matched filtering, envelope extraction, and peak reconstruction, are incorporated in the authors' algorithm. Monte Carlo simulations demonstrate that the proposed method is a robust one to locate scattering centers in spatial domain, and has a better performance than spectrum-based MSS estimation techniques. Especially exploited in estimating MSS which varies from 0.6 to 1.2 mm in the range of human mean trabecular bone spacing, the proposed method shows great potential in medical use. Simple but widely used phantom experiments demonstrate that the proposed algorithm has the capacity to locate scattering centers in spatial domain.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

Background

Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer.

Methods

The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times.

Results

Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology.

Conclusions

This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

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.

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis.

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.

High frequency ultrasound imaging and simulations of sea urchin oocytes

High frequency ultrasound backscatter signals from sea urchin oocytes were measured using a 40 MHz transducer and compared to numerical simulations. The Faran scattering model was used to calculate the ultrasound scattered from single oocytes in suspension. The urchin oocytes are non-nucleated with uniform size and biomechanical properties; the backscatter from each cell is similar and easy to simulate, unlike typical nucleated mammalian cells. The time domain signal measured from single oocytes in suspension showed two distinct peaks, and the power spectrum was periodic with minima spaced approximately 10 MHz apart. Good agreement to the Faran scattering model was observed. Measurements from tightly packed oocyte cell pellets showed similar periodic features in the power spectra, which was a result of the uniform size and consistent biomechanical properties of the cells. Numerical simulations that calculated the ultrasound scattered from individual oocytes within a three dimensional volume showed good agreement to the measured signals and B-scan images. A cepstral analysis of the signal was used to calculate the size of the cells, which was 78.7 μm (measured) and 81.4 μm (simulated). This work supports the single scattering approximation, where ultrasound is discretely scattered from single cells within a bulk homogeneous sample, and that multiple scattering has a negligible effect. This technique can be applied towards understanding the complex scattering behaviour from heterogeneous tissues.

Regular chondrocyte spacing is a potential cause for coherent ultrasound backscatter in human articular cartilage

The potential of quantitative ultrasound (QUS) to assess the regular cellular spacing in the superficial cartilage zones was investigated experimentally and numerically. Nine osteochondral samples, extracted from two human cadaver knee joints, were measured using a 50-MHz ultrasound scanning device and evaluated using Mankin score. Simulated backscattered power spectra from models with an idealized cell alignment exhibited a pronounced frequency peak. From the peak, cell spacing in the range between 15 and 40 μm between cell layers was detected with an average error of 0.2 μm. The mean QUS-based cell spacing was 28.3 ± 5.3 μm. Strong correlation (R²= 0.59, p ≤ 0.001) between spacing estimates from light microscopy (LM) and QUS was found for samples with Mankin score ≤3. For higher scores, QUS-based spacing was significantly higher (p ≤ 0.05) compared to LM-based spacing. QUS-based spacing estimates together with other QUS parameters may serve as future biomarkers for detecting early signs of osteoarthrosis.

Speckle tracking and speckle content based composite strain imaging for solid and fluid filled lesions

Strain imaging though for solid lesions provides an effective way for determining their pathologic condition by displaying the tissue stiffness contrast, for fluid filled lesions such an imaging is yet an open problem. In this paper, we propose a novel speckle content based strain imaging technique for visualization and classification of fluid filled lesions in elastography after automatic identification of the presence of fluid filled lesions. Speckle content based strain, defined as a function of speckle density based on the relationship between strain and speckle density, gives an indirect strain value for fluid filled lesions. To measure the speckle density of the fluid filled lesions, two new criteria based on oscillation count of the windowed radio frequency signal and local variance of the normalized B-mode image are used. An improved speckle tracking technique is also proposed for strain imaging of the solid lesions and background. A wavelet-based integration technique is then proposed for combining the strain images from these two techniques for visualizing both the solid and fluid filled lesions from a common framework. The final output of our algorithm is a high quality composite strain image which can effectively visualize both solid and fluid filled breast lesions in addition to the speckle content of the fluid filled lesions for their discrimination. The performance of our algorithm is evaluated using the in vivo patient data and compared with recently reported techniques. The results show that both the solid and fluid filled lesions can be better visualized using our technique and the fluid filled lesions can be classified with good accuracy.

Analysis of Coherent and Diffuse Scattering Using a Reference Phantom

The estimation of many spectral-based quantitative ultrasound parameters assumes that backscattered echo signals are from a stationary, incoherent scattering process. The accuracy of these assumptions in real tissue can limit the diagnostic value of these parameters and the physical insight about tissue microstructure they can convey. This work presents an empirical decision test to determine the presence of significant coherent contributions to echo signals and whether they are caused by low scatterer number densities or the presence of specular reflectors or scatterers with periodic spacing. This is achieved by computing parameters from echo signals that quantify stationary or nonstationary features related to coherent scattering, and then comparing their values to thresholds determined from a reference material providing diffuse scattering. The paper first presents a number of parameters with demonstrated sensitivity to coherent scattering and describes criteria to select those with the highest sensitivity using simulated and phantom-based echo data. Results showed that the echo amplitude signal-to-noise ratio and the multitaper-generalized spectrum were the parameters with the highest sensitivity to coherent scattering with stationary and nonstationary features, respectively. These parameters were incorporated into the reference-based decision test, which successfully identified regions in simulated and tissue-mimicking phantoms with different incoherent and coherent scattering conditions. When scatterers with periodic organization were detected, the combination of stationary and nonstationary analysis permitted the estimation of the mean spacing below and above the resolution limit imposed by the pulse size. Preliminary applications of this algorithm to human cervical tissue ex vivo showed correspondence between regions of B-mode images showing bright reflectors, tissue interfaces, and hypoechoic regions with regions classified as specular reflectors and low scatterer number density. These results encourage further application of the algorithm to more structurally complex phantoms and tissue.

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach

PURPOSE

This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).

METHODS

Using a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival.

RESULTS

Multi-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively).

CONCLUSION

This study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.

Noninvasive characterization of locally advanced breast cancer using textural analysis of quantitative ultrasound parametric images

PURPOSE: The identification of tumor pathologic characteristics is an important part of breast cancer diagnosis, prognosis, and treatment planning but currently requires biopsy as its standard. Here, we investigated a noninvasive quantitative ultrasound method for the characterization of breast tumors in terms of their histologic grade, which can be used with clinical diagnostic ultrasound data. METHODS: Tumors of 57 locally advanced breast cancer patients were analyzed as part of this study. Seven quantitative ultrasound parameters were determined from each tumor region from the radiofrequency data, including mid-band fit, spectral slope, 0-MHz intercept, scatterer spacing, attenuation coefficient estimate, average scatterer diameter, and average acoustic concentration. Parametric maps were generated corresponding to the region of interest, from which four textural features, including contrast, energy, homogeneity, and correlation, were determined as further tumor characterization parameters. Data were examined on the basis of tumor subtypes based on histologic grade (grade I versus grade II to III). RESULTS: Linear discriminant analysis of the means of the parametric maps resulted in classification accuracy of 79%. On the other hand, the linear combination of the texture features of the parametric maps resulted in classification accuracy of 82%. Finally, when both the means and textures of the parametric maps were combined, the best classification accuracy was obtained (86%). CONCLUSIONS: Textural characteristics of quantitative ultrasound spectral parametric maps provided discriminant information about different types of breast tumors. The use of texture features significantly improved the results of ultrasonic tumor characterization compared to conventional mean values. Thus, this study suggests that texture-based quantitative ultrasound analysis of in vivo breast tumors can provide complementary diagnostic information about tumor histologic characteristics.

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

PURPOSE

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

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.

Estimation of PSD shifts for high-resolution metrology of thickness micro-changes with possible applications in vessel walls and biological membrane characterization

Achieving accurate measurements of inflammation levels in tissues or thickness changes in biological membranes (e.g., amniotic sac, parietal pleura) and thin biological walls (e.g., blood vessels) from outside the human body, is a promising research line in the medical area. It would provide a technical basis to study the options for early diagnosis of some serious diseases such as hypertension, atherosclerosis or tuberculosis. Nevertheless, achieving the aim of non-invasive measurement of those scarcely-accessible parameters on patient internal tissues, currently presents many difficulties. The use of high-frequency ultrasonic transducer systems appears to offer a possible solution. Previous studies using conventional ultrasonic imaging have shown this, but the spatial resolution was not sufficient so as to permit a thickness evaluation with clinical significance, which requires an accuracy of a few microns. In this paper a broadband ultrasonic technique, that was recently developed by the authors to address other non-invasive medical detection problems (by integrating a piezoelectric transducer into a spectral measuring system), is extended to our new objective; the aim is its application to the thickness measurement of sub-millimeter membranes or layers made of materials similar to some biological tissues (phantoms). The modeling and design rules of such a transducer system are described, and various methods of estimating overtones location in the power spectral density (PSD) are quantitatively assessed with transducer signals acquired using piezoelectric systems and also generated from a multi-echo model. Their effects on the potential resolution of the proposed thickness measuring tool, and their capability to provide accuracies around the micron are studied in detail. Comparisons are made with typical tools for extracting spatial parameters in laminar samples from echo-waveforms acquired with ultrasonic transducers. Results of this advanced measurement spectral tool are found to improve the performance of typical cross-correlation methods and provide reliable and high-resolution estimations.

Tissue classification using ultrasound-induced variations in acoustic backscattering features

Ultrasound (US) radio-frequency (RF) time series is an effective tissue classification method that enables accurate cancer diagnosis, but the mechanisms underlying this method are not completely understood. This paper presents a model to describe the variations in tissue temperature and sound speed that take place during the RF time series scanning procedures and relate these variations to US backscattering. The model was used to derive four novel characterization features. These features were used to classify three animal tissues, and they obtained accuracies as high as 88.01%. The performance of the proposed features was compared with RF time series features proposed in a previous study. The results indicated that the US-induced variations in tissue temperature and sound speed, which were used to derive the proposed features, were important contributors to the tissue typing capabilities of the RF time series. Simulations carried out to estimate the heating induced during the scanning procedure employed in this study showed temperature rises lower than 2 °C. The model and results presented in this paper can be used to improve the RF time series.

Possible patient early diagnosis by ultrasonic noninvasive estimation of thermal gradients into tissues based on spectral changes modeling

To achieve a precise noninvasive temperature estimation, inside patient tissues, would open promising research fields, because its clinic results would provide early-diagnosis tools. In fact, detecting changes of thermal origin in ultrasonic echo spectra could be useful as an early complementary indicator of infections, inflammations, or cancer. But the effective clinic applications to diagnosis of thermometry ultrasonic techniques, proposed previously, require additional research. Before their implementations with ultrasonic probes and real-time electronic and processing systems, rigorous analyses must be still made over transient echotraces acquired from well-controlled biological and computational phantoms, to improve resolutions and evaluate clinic limitations. It must be based on computing improved signal-processing algorithms emulating tissues responses. Some related parameters in echo-traces reflected by semiregular scattering tissues must be carefully quantified to get a precise processing protocols definition. In this paper, approaches for non-invasive spectral ultrasonic detection are analyzed. Extensions of author's innovations for ultrasonic thermometry are shown and applied to computationally modeled echotraces from scattered biological phantoms, attaining high resolution (better than 0.1°C). Computer methods are provided for viability evaluation of thermal estimation from echoes with distinct noise levels, difficult to be interpreted, and its effectiveness is evaluated as possible diagnosis tool in scattered tissues like liver.

Improving the statistics of quantitative ultrasound techniques with deformation compounding: an experimental study

Many quantitative ultrasound (QUS) techniques are based on estimates of the radio-frequency (RF) echo signal power spectrum. Historically, reliable spectral estimates required spatial averaging over large regions-of-interest (ROIs). Spatial compounding techniques have been used to obtain robust spectral estimates for data acquired over small regions of interest. A new technique referred to as "deformation compounding" is another method for providing robust spectral estimates over smaller regions of interest. Motion tracking software is used to follow an ROI while the tissue is deformed (typically by pressing with the transducer). The deformation spatially reorganizes the scatterers so that the resulting echo signal is decorrelated. The RF echo signal power spectrum for the ROI is then averaged over several frames of RF echo data as the tissue is deformed, thus, undergoing deformation compounding. More specifically, averaging spectral estimates among the uncorrelated RF data acquired following small deformations allows reduction in the variance of the power spectral density estimates and, thereby, improves accuracy of spectrum-based tissue property estimation. The viability of deformation compounding has been studied using phantoms with known attenuation and backscatter coefficients. Data from these phantoms demonstrates that a deformation of about 2% frame-to-frame average strain is sufficient to obtain statistically-independent echo signals (with correlations of less than 0.2). Averaging five such frames, where local scatterer reorganization has taken place due to mechanical deformations, reduces the average percent standard deviation among power spectra by 26% and averaging 10 frames reduces the average percent standard deviation by 49%. Deformation compounding is used in this study to improve measurements of backscatter coefficients. These tests show deformation compounding is a promising method to improve the accuracy of spectrum-based quantitative ultrasound for tissue characterization.

High-frequency ultrasound for intraoperative margin assessments in breast conservation surgery: a feasibility study

Background

In addition to breast imaging, ultrasound offers the potential for characterizing and distinguishing between benign and malignant breast tissues due to their different microstructures and material properties. The aim of this study was to determine if high-frequency ultrasound (20-80 MHz) can provide pathology sensitive measurements for the ex vivo detection of cancer in margins during breast conservation surgery.

Methods

Ultrasonic tests were performed on resected margins and other tissues obtained from 17 patients, resulting in 34 specimens that were classified into 15 pathology categories. Pulse-echo and through-transmission measurements were acquired from a total of 57 sites on the specimens using two single-element 50-MHz transducers. Ultrasonic attenuation and sound speed were obtained from time-domain waveforms. The waveforms were further processed with fast Fourier transforms to provide ultrasonic spectra and cepstra. The ultrasonic measurements and pathology types were analyzed for correlations. The specimens were additionally re-classified into five pathology types to determine specificity and sensitivity values.

Results

The density of peaks in the ultrasonic spectra, a measure of spectral structure, showed significantly higher values for carcinomas and precancerous pathologies such as atypical ductal hyperplasia than for normal tissue. The slopes of the cepstra for non-malignant pathologies displayed significantly greater values that differentiated them from the normal and malignant tissues. The attenuation coefficients were sensitive to fat necrosis, fibroadenoma, and invasive lobular carcinoma. Specificities and sensitivities for differentiating pathologies from normal tissue were 100% and 86% for lobular carcinomas, 100% and 74% for ductal carcinomas, 80% and 82% for benign pathologies, and 80% and 100% for fat necrosis and adenomas. Specificities and sensitivities were also determined for differentiating each pathology type from the other four using a multivariate analysis. The results yielded specificities and sensitivities of 85% and 86% for lobular carcinomas, 85% and 74% for ductal carcinomas, 100% and 61% for benign pathologies, 84% and 100% for fat necrosis and adenomas, and 98% and 80% for normal tissue.

Conclusions

Results from high-frequency ultrasonic measurements of human breast tissue specimens indicate that characteristics in the ultrasonic attenuation, spectra, and cepstra can be used to differentiate between normal, benign, and malignant breast pathologies.

An adaptive spectral estimation technique to detect cavitation in HIFU with high spatial resolution

In ultrasound-guided high-intensity focused ultrasound (HIFU) therapy, the changes observed on tissue are subtle during treatment; some ultrasound-guided HIFU protocols rely on the observation of significant brightness changes as the indicator of tissue lesions. The occurrence of a distinct hyperechogenic region ("bright-up") around the focus is often associated with acoustic cavitation resulting in microbubble formation, but it may indicate different physical events such as larger bubbles from boiling (known to alter acoustic impedance) or sometimes lesion formation. A reliable method to distinguish and spatially localize these causes within the tissue would assist the control of HIFU delivery, which is the subject of this paper. Spectral analysis of the radio frequency (RF) signal underlying the B-mode image provides more information on the physical cause, but the usual techniques that are methods on the Fourier transform require a long series for good spectral resolution and so they give poor spatial resolution. This paper introduces an active spectral cavitation detection method to attain high spatial resolution (0.15 × 0.15 mm per pixel) through a parametric statistical method (ARMA modeling) used on finite-length data sets, which enables local changes to be identified more easily. This technique uses the characteristics of the signal itself to optimize the model parameters and structure. Its performance is assessed using synthesized cavitation RF data, and it is then demonstrated in ex vivo bovine liver during and after HIFU exposure. The results suggest that good spatial and spectral resolution can be obtained by the design of suitable algorithms. In ultrasound-guided HIFU, the technique provides a useful supplement to B-mode analysis, with no additional time penalty in data acquisition.

An efficient temperature estimation using optical-flow in ultrasound B-mode digital images

A two-dimensional temperature estimation method has been proposed based on the detection of shifts in speckle pattern location of ultrasound B-Mode digital images from a region of tissue undergoing thermal therapy. The speckle pattern shifts were due to the local temperature dependence of sound speed and thermal expansion in the heated region. Here in this paper we made use of optical-flow in order to achieve a better accuracy and lower computational burden. The accuracy of measurement of the temperature has been tested on simulated images and it is experimentally validated using tissue mimicking phantom. Good agreement was obtained from the simulated sequences (mean difference=0.1 and pick error was 0.6). The proposed method was found computationally faster compared to our previous work which was based on the cross correlation algorithm.

A performance analysis of echographic ultrasonic techniques for non-invasive temperature estimation in hyperthermia range using phantoms with scatterers

Optimization of efficiency in hyperthermia requires a precise and non-invasive estimation of internal distribution of temperature. Although there are several research trends for ultrasonic temperature estimation, efficient equipments for its use in the clinical practice are not still available. The main objective of this work was to research about the limitations and potential improvements of previously reported signal processing options in order to identify research efforts to facilitate their future clinical use as a thermal estimator. In this document, we have a critical analysis of potential performance of previous ultrasonic research trends for temperature estimation inside materials, using different processing techniques proposed in frequency, time and phase domains. It was carried out in phantom with scatterers, assessing at their specific applicability, linearity and limitations in hyperthermia range. Three complementary evaluation indexes: technique robustness, Mat-lab processing time and temperature resolution, with specific application protocols, were defined and employed for a comparative quantification of the behavior of the techniques. The average increment per degrees C and mm was identified for each technique (3 KHz/ degrees C in the frequency analysis, 0.02 rad/ degrees C in the phase domain, while increments in the time domain of only 1.6 ns/ degrees C were found). Their linearity with temperature rising was measured using linear and quadratic regressions and they were correlated with the obtained data. New improvements in time and frequency signal processing in order to reveal the potential thermal and spatial resolutions of these techniques are proposed and their subsequent improved estimation results are shown for simulated and measured A-scans registers. As an example of these processing novelties, an excellent potential resolution of 0.12 degrees C into hyperthermia range, with near-to-linear frequency dependence, could be achieved. Specifically defined "numerical" and physical multi-scatter phantoms are described, which mimic ultrasound velocity in tissues of about 1560 m/s @ 35 degrees C and have a quasi-uniform internal scattering structure designed to assure standard signal patterns adequate for processing comparisons in the same time and sound velocity conditions for all the techniques analyzed, and to obtain easily repeatable multi-pulse echo-patterns. A perfect lineal dependence (100% of correlation coefficient) between the unitary average increment measured by each technique and temperature rising was observed while working with simulated A-scan registers, where all the parameters are under an accurate control. Nevertheless a very small quadratic tendency appeared in the results obtained from experimental echo registers, which are more similar to a real tissues case. It would be an interesting future work to analyze the behavior of these techniques in real tissues in order to confirm or reject this light quadratic tendency. Finally, new methods were detailed and applied in order to precisely quantify the advantages of each estimation technique; their respective intrinsic limitations were also underlined.

Tissue characterization using multiscale products of wavelet transform of ultrasound radio frequency echoes

This paper presents a novel method for tissue characterization using wavelet transform of ultrasound radio frequency (RF) echo signals. We propose the use of multiscale products of wavelet transform sequences of RF echoes to estimate the scatterer distribution in the tissue. The proposed method is based on the fact that when emitted ultrasound beams interact with scatterers in the tissue, backscattered beams contain singularities corresponding to the location of the scatterers. The singularities will exist in multiple scales of wavelet sequences of the echo signals. Therefore, peaks of wavelet transform multiscale products correspond to the location of scatterers. Estimation of scatterer spacing can be used for tissue characterization. The efficacy of the proposed method was validated in RF echo signals of in-vitro human prostate to characterize normal and cancerous tissue. The results confirm that wavelet transform multiscale products of RF echo signals contain tissue typing information that can be used as an effective tool to differentiate normal and cancerous prostate tissue.

Simplified inverse filter tracking algorithm for estimating the mean trabecular bone spacing

Ultrasonic backscatter signals provide useful information relevant to bone tissue characterization. Trabecular bone microstructures have been considered as quasi-periodic tissues with a collection of regular and diffuse scatterers. This paper investigates the potential of a novel technique using a simplified inverse filter tracking (SIFT) algorithm to estimate mean trabecular bone spacing (MTBS) from ultrasonic backscatter signals. In contrast to other frequency-based methods, the SIFT algorithm is a time-based method and utilizes the amplitude and phase information of backscatter echoes, thus retaining the advantages of both the autocorrelation and the cepstral analysis techniques. The SIFT algorithm was applied to backscatter signals from simulations, phantoms, and bovine trabeculae in vitro. The estimated MTBS results were compared with those of the autoregressive (AR) cepstrum and quadratic transformation (QT) . The SIFT estimates are better than the AR cepstrum estimates and are comparable with the QT values. The study demonstrates that the SIFT algorithm has the potential to be a reliable and robust method for the estimation of MTBS in the presence of a small signal-to-noise ratio, a large spacing variation between regular scatterers, and a large scattering strength ratio of diffuse scatterers to regular ones.

Improving the contrast of breast cancer masses in ultrasound using an autoregressive model based filter

The assessment and diagnosis of breast cancer with ultrasound is a challenging problem due to the low contrast between cancer masses and benign tissue. Due to this low contrast it has proven to be difficult to achieve reliable segmentation results on breast cancer masses. An autoregressive model has been employed to filter out of the backscattered RF-signal from a tissue harmonic image which is not degraded by harmonic leakage. Measurements on the filtered image have shown a significant (up to 45%) increase in contrast between cancer masses and benign tissue.

Classification of simulated hyperplastic stages in the breast ducts based on ultrasound RF echo

Visual inspection of ultrasound is diagnostically limited for characterizing breast tissue, in particular when it comes to visually detecting hyperplasia that forms in the ducts at its early formation (at submillimeter resolution) stages. It can, of course, be seen using biopsies. But this will not be done unless the areas have been flagged using noninvasive modalities. The aim of this paper is to draw to the attention of the medical community (albeit through simulations) that the continuous wavelet transform decomposition (CWTD) that was proven in vivo for tissue characterization before has the potential to flag out simulated hyperplasia data at submillimeter resolutions. And it might be an excellent candidate for detecting in vivo hyperplastic changes in the breast. To the best of our knowledge, this is the first attempt at studying the potential of detecting cell growth in breast ducts using ultrasound. The stochastic decomposition model (the CWTD) of the RF echo with its coherent and diffuse components, yields image parameters that correlate closely with the structural parameters of the (simulated) hyperplastic stages of the breast tissue. The discrimination power of the various parameters is studied under a host of conditions, such as varying resolution, depth, and coherent to diffuse energy ratio (CDR) values using a point-scatterer model simulator that mimics epithelium hyperplastic growth in the breast ducts. These are shown to be useful for detecting the various types of simulated hyperplastic data. Careful analysis shows that three parameters, in particular the number of coherent scatterers, the Rayleigh scattering degree, and the energy of the diffuse scatterers, are most sensitive to variations in the hyperplastic simulated data. And they show very high ability to discriminate between various stages of simulated hyperplasia, even in cases of low resolution and low CDR values. Using the area under the receiver operating characteristics (ROC) curve (A(z)) as the performance metric, values of A(z) > 0.942 are obtained when discriminating between stages for resolution <or= 0.4 mm, even for low CDR values. Then it drops below the 0.9 range as the resolution exceeds the 0.4-mm range. A nonparametric segmentation method to extract ductal areas from breast scans is presented to be used as a pre-step before classification of hyperplastic stages in breast ducts. This is a necessary step for segmenting the RF scan into ductal versus nonductal areas from breast scans. This is tested using breast tissue mimicking phantom data resulting values of A(z) > 0.948 for different duct densities.

A full vectorial contrast source inversion scheme for three-dimensional acoustic imaging of both compressibility and density profiles

Imaging the two acoustic medium parameters density and compressibility requires the use of both the acoustic pressure and velocity wave fields, described via integral equations. Imaging is based on solving for the unknown medium parameters using known measured scattered wave fields, and it is difficult to solve this ill-posed inverse problem directly using a conjugate gradient inversion scheme. Here, a contrast source inversion method is used in which the contrast sources, defined via the product of changes in compressibility and density with the pressure and velocity wave fields, respectively, are computed iteratively. After each update of the contrast sources, an update of the medium parameters is obtained. Total variation as multiplicative regularization is used to minimize blurring in the reconstructed contrasts. The method successfully reconstructed three-dimensional contrast profiles based on changes in both density and compressibility, using synthetic data both with and without 50% white noise. The results were compared with imaging based only on the pressure wave field, where speed of sound profiles were solely based on changes in compressibility. It was found that the results improved significantly by using the full vectorial method when changes in speed of sound depended on changes in both compressibility and density.

Singular spectrum analysis applied to ultrasonic detection and imaging of brachytherapy seeds

Ultrasound-guided brachytherapy using titanium-shelled radioactive seeds is a popular, effective means of treating prostate cancer. Unfortunately, implantation using needles inserted transperitoneally causes gland movement and distortion, which often results in seed misplacement and dosimetry errors. If actual seed locations could be determined in the operating room, then corrections to dosimetry errors could be made immediately. However, seed specularity, shadowing, and tissue clutter make imaging seeds difficult using conventional ultrasound. Singular spectrum analysis (SSA) shows promise for reliably imaging radioactive seeds implanted in the prostate and enabling additional corrective implantations to be made in the operating room. SSA utilizes eigenvalues derived from the diagonalized correlation matrix of envelope-detected radio-frequency echo signals to yield a P value indicative of the likelihood of a seed-specific repetitive signal. We demonstrated the potential of SSA for seed detection and imaging and illustrated the trade-off considerations for optimization of SSA in clinical applications using simulations assessing performance as a function of different levels of noise and the presence of repetitive signals with various repetition periods; experiments in an ideal scattering environment; and experiments using seeds implanted in beef.

Noninvasive temperature estimation using sonographic digital images

OBJECTIVE

The purpose of this study was to develop and evaluate a speckle-tracking method for tissue temperature estimation due to heating fields using digital sonographic images.

METHODS

The temperature change estimation method is based on the thermal dependence of the ultrasound speed and the thermal expansion of the medium. Local changes in the speed of sound due to changes in the temperature produce apparent displacement of the scatterers, and the expansion introduces physical displacement. In our study, a new technique has been introduced in which the axial physical displacements were obtained from digital sonographic images. The axial speckle pattern displacement was determined with a cross-correlation algorithm. The displacement data were then used for computing the temperature changes. To monitor the temperature in real time, the computational time was decreased by restricting the search region in the cross-correlation algorithm and carrying out the cross-correlation function in the frequency domain via a fast Fourier transform algorithm.

RESULTS

Experiments were performed on tissue-mimicking phantoms. The imaging probe was a commercial linear array working at 10 MHz. In addition, the temperature changes during heating were measured invasively by negative temperature coefficient thermistors. There was good agreement between ultrasonic temperature estimations and invasive temperature measurements.

CONCLUSIONS

The proposed method verifies the capability of the speckle-tracking algorithm for determining both the magnitude and direction of displacement. The average error was 0.2 degrees C; the maximum error was 0.53 degrees C; and the SD was 0.19 degrees C. Therefore, the proposed algorithm is capable of extracting the temperature information from sonographic digital images.

A forward model and conjugate gradient inversion technique for low-frequency ultrasonic imaging

Emerging methods of hyperthermia cancer treatment require noninvasive temperature monitoring, and ultrasonic techniques show promise in this regard. Various tomographic algorithms are available that reconstruct sound speed or contrast profiles, which can be related to temperature distribution. The requirement of a high enough frequency for adequate spatial resolution and a low enough frequency for adequate tissue penetration is a difficult compromise. In this study, the feasibility of using low frequency ultrasound for imaging and temperature monitoring was investigated. The transient probing wave field had a bandwidth spanning the frequency range 2.5-320.5 kHz. The results from a forward model which computed the propagation and scattering of low-frequency acoustic pressure and velocity wave fields were used to compare three imaging methods formulated within the Born approximation, representing two main types of reconstruction. The first uses Fourier techniques to reconstruct sound-speed profiles from projection or Radon data based on optical ray theory, seen as an asymptotical limit for comparison. The second uses backpropagation and conjugate gradient inversion methods based on acoustical wave theory. The results show that the accuracy in localization was 2.5 mm or better when using low frequencies and the conjugate gradient inversion scheme, which could be used for temperature monitoring.

Analysis of microstructural alterations of normal and pathological breast tissue in vivo using the AR cepstrum

The mean scatterer spacing is considered to be an important parameter for describing ultrasonic scattering and characterization of biological tissue. Autoregressive models are widely used in parametric techniques for spectral estimation. In this paper, we describe the results of a careful examination of the mean scatterer spacing parameter in normal and pathological breast tissues in vivo using the autoregressive cepstrum. Our experimental results carried out at 4.5 MHz using weakly focused pulse-echo single element transducer show that the mean scatterer spacing in normal breast tissues in vivo is 1.25+/-0.21 mm whereas in several pathological breast tissues, it is between 0.82+/-0.10 and 1.09+/-0.07 mm. These results indicate good correlation with microstructure of breast tissue characterization, and hence the AR cepstrum holds promise that it could be used as an effective method for signal analysis of ultrasonic scattering and characterization of breast tissues scatterers.

Investigation of parametric spectral estimation techniques for elasticity imaging

Several autoregressive (AR) and autoregressive moving average (ARMA) parametric spectral estimators were evaluated for use in tissue strain estimation. Using both 1-D simulations and in vitro phantom experiments, the performance of these parametric spectral strain estimators were compared against both a nonparametric discrete Fourier transform (DFT) spectral strain estimator and a coherent elastographic technique. Parametric spectral estimator model orders were selected based on a modified strain filter approach. This technique illustrated the trade-offs between different signal-processing parameters and a strain estimator performance measure, namely the area under the strain filter (using applied strain dynamic range of 0.1 to 50%). The Yule-Walker AR spectral strain estimator outperformed all other parametric methods evaluated, but failed to outperform the DFT-based approach. Furthermore, both these spectral strain-estimation techniques exhibit an elastographic signal-to-noise ratio (SNR(e)) and strain estimation dynamic range not achievable using conventional elastography without global stretching.

Noninvasive estimation of tissue temperature via high-resolution spectral analysis techniques

We address the noninvasive temperature estimation from pulse-echo radio frequency signals from standard diagnostic ultrasound imaging equipment. In particular, we investigate the use of a high-resolution spectral estimation method for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach, employing generalized second-order statistics, is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods Seip and Ebbini (1995). Furthermore, temperature estimates from the new algorithm are compared with results from the more commonly used echo shift method described in Simon et al. (1998).

Ultrasonic data compression via parameter estimation

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.

Improved scatterer property estimates from ultrasound backscatter for small gate lengths using a gate-edge correction factor

Backscattered rf signals used to construct conventional ultrasound B-mode images contain frequency-dependent information that can be examined through the backscattered power spectrum. The backscattered power spectrum is found by taking the magnitude squared of the Fourier transform of a gated time segment corresponding to a region in the scattering volume. When a time segment is gated, the edges of the gated regions change the frequency content of the backscattered power spectrum due to truncating of the waveform. Tapered windows, like the Hanning window, and longer gate lengths reduce the relative contribution of the gate-edge effects. A new gate-edge correction factor was developed that partially accounted for the edge effects. The gate-edge correction factor gave more accurate estimates of scatterer properties at small gate lengths compared to conventional windowing functions. The gate-edge correction factor gave estimates of scatterer properties within 5% of actual values at very small gate lengths (less than 5 spatial pulse lengths) in both simulations and from measurements on glass-bead phantoms. While the gate-edge correction factor gave higher accuracy of estimates at smaller gate lengths, the precision of estimates was not improved at small gate lengths over conventional windowing functions.

Defining optimal axial and lateral resolution for estimating scatterer properties from volumes using ultrasound backscatter

The rf signals used to construct conventional ultrasound B-mode images contain frequency-dependent information that can be examined through the backscattered power spectrum. Typically, the backscattered power spectrum is calculated from a region of interest (ROI) within some larger volume. The dimensions of the ROI are defined axially by the spatial length corresponding to the time gate and laterally by the number of scan lines included in the ROI. Averaging the backscattered power spectra from several independent scan lines can reduce the presence of noise caused by electronics and by the random scatterer spacings, but also decreases the lateral resolution of the interrogation region. Furthermore, larger axial gate lengths can be used to reduce the effects of noise and improve the precision and accuracy of scatterer property estimates but also decreases the axial resolution. A trade-off exists between the size of the ROI (the number of scan lines used, the separation distance between each scan line, the axial gate length) and the accuracy and precision of scatterer property estimates. A series of simulations and measurements from physical phantoms were employed to examine these trade-offs. The simulations and phantom measurements indicated the optimal lateral and axial sizes of the ROI, where estimate accuracy and precision were better than 10% and 5%, respectively, occurred at 4 to 5 beamwidths laterally and 15 to 20 spatial pulse lengths axially.

Singular spectrum analysis applied to backscattered ultrasound signals from in vitro human cancellous bone specimens

Mean scatterer spacing (MSS) holds particular promise for the detection of changes in quasiperiodic tissue microstructures such as may occur during development of disease in the liver, spleen, or bones. Many techniques that may be applied for MSS estimation (temporal and spectral autocorrelation, power spectrum and cepstrum, higher order statistics, and quadratic transformation) characterize signals that contain a mixture of periodic and nonperiodic contributions. In contrast, singular spectrum analysis (SSA), a method usually applied in nonlinear dynamics, first identifies components of signals corresponding to periodic structures and, second, identifies dominant periodicity. Thus, SSA may better separate periodic structures from nonperiodic structures and noise. Using an ultrasound echo simulation model, we previously demonstrated SSA's potential to identify MSS of structures in quasiperiodic scattering media. The current work aims to observe the behavior of MSS estimation by SSA using ultrasound measurements in phantom materials (two parallel, nylon-line phantoms and four foam phantoms of different densities). The SSA was able to estimate not only the nylon-line distances but also nylon-line thickness. The method also was sensitive to the average pore-size differences of the four sponges. The algorithms then were applied to characterize human cancellous bone microarchitectures. Using 1-MHz center-frequency, radio-frequency ultrasound signals, MSS was measured in 24 in vitro bone samples and ranged from 1.0 to 1.7 mm. The SSA MSS estimates correlate significantly to MSS measured independently from synchrotron microtomography, r2 = 0.68. Thus, application of SSA to backscattered ultrasound signals seems to be useful for providing information linked to tissue microarchitecture that is not evident from clinical images.