Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound

Validation of differences in backscatter coefficients among four ultrasound scanners with different beamforming methods

PURPOSE

The backscatter coefficient (BSC) indicates the absolute scatterer property of a material, independently of clinicians and system settings. Our study verified that the BSC differed among the scanners, transducers, and beamforming methods used for quantitative ultrasound analyses of biological tissues.

METHODS

Measurements were performed on four tissue-mimicking homogeneous phantoms containing spherical scatterers with mean diameters of 20 and 30 µm prepared at concentrations of 0.5 and 2.0 wt%, respectively. The BSCs in the different systems were compared using ultrasound scanners with two single-element transducers and five linear high- or low-frequency probes. The beamforming methods were line-by-line formation using focused imaging (FI) and parallel beam formation using plane wave imaging (PWI). The BSC of each system was calculated by the reference phantom method. The mean deviation from the theoretical BSC computed by the Faran model was analyzed as the benchmark validation of the calculated BSC.

RESULTS

The BSCs calculated in systems with different properties and beamforming methods well concurred with the theoretical BSC. The mean deviation was below ± 2.8 dB on average, and within the approximate standard deviation (± 2.2 dB at most) in all cases. These variations agreed with a previous study in which the largest error among four different scanners with FI beamforming was 3.5 dB.

CONCLUSION

The BSC in PWI was equivalent to those in the other systems and to those of FI beamforming. This result indicates the possibility of ultra-high frame-rate BSC analysis using PWI.

Nonlinear bulk elasticity imaging using dual frequency ultrasound

The nonlinear acoustic bulk properties of tissue, e.g., the coefficient of nonlinearity, β_n, or the nonlinear bulk elasticity, β_p=β_nκ₀, have been shown to be promising parameters for tissue characterization due to their sensitivity to tissue structure. Previously developed methods for imaging these parameters using single frequency ultrasound have shown success in a laboratory setting using the transmission mode. In the pulse-echo mode, however, unknown absorption, diffraction, and speckle produce unreliable estimates and instability, causing these methods to have achieved no clinical relevance. In this paper, a pulse-echo method for measurement of the nonlinear bulk elasticity is presented using a dual frequency approach. The method is less sensitive to diffraction and absorption due to a separate low frequency manipulation wave. The technique is tested in both simulations and in vitro in a heterogeneous phantom with two regions of different nonlinear properties. Both in simulations and in vitro, a spatial β_p map is produced where the two regions are clearly distinguished. In addition, the quantitative estimates of β_p obtained are close to the expected values, making the method a promising first step toward in vivo imaging of nonlinear bulk properties.

Resolution of Murine Toxic Hepatic Injury Quantified With Ultrasound Entropy Metrics

Image-based classification of liver disease generally lacks specificity for distinguishing between acute, resolvable injury and chronic irreversible injury. We propose that ultrasound radiofrequency data acquired in vivo from livers subjected to toxic drug injury can be analyzed with information theoretic detectors to derive entropy metrics, which classify a statistical distribution of pathologic scatterers that dissipate over time as livers heal. Here we exposed 38 C57BL/6 mice to carbon tetrachloride to cause liver damage, and imaged livers in vivo 1, 4, 8, 12 and 18 d after exposure with a broadband 15-MHz probe. Selected entropy metrics manifested monotonic recovery to normal values over time as livers healed, and were correlated directly with progressive restoration of liver architecture by histologic assessment (r² ≥ 0.95, p < 0.004). Thus, recovery of normal liver microarchitecture after toxic exposure can be delineated sensitively with entropy metrics.

Diagnostic Accuracy of Echo Envelope Statistical Modeling Compared to B-Mode and Power Doppler Ultrasound Imaging in Patients With Clinically Diagnosed Lateral Epicondylosis of the Elbow

OBJECTIVES

To compare the accuracy of homodyned K quantitative ultrasound (QUS) with that of B-mode and Doppler ultrasound imaging for discriminating between lateral epicondylosis (LE) and asymptomatic elbows.

METHODS

This prospective study received Institutional Review Board approval, and participants provided written informed consent. Between February 2015 and March 2017, 30 LE elbows in 27 patients and 24 asymptomatic elbows in 13 volunteers underwent B-mode, Doppler, and radiofrequency ultrasound imaging of the common extensor tendon (CET) and radial collateral ligament (RCL). Two readers classified the elbows independently on the basis of a review of B-mode and Doppler images. The global and local estimates of QUS parameters (μ _n , 1/α, and k) were computed in the CET and CET-RCL regions, respectively, and the area of each region was calculated. A random-forest classifier identified the most discriminating 3-parameter combination: CET global estimate of 1/α, CET-RCL area, and local estimate of k.

RESULTS

The patients with LE had a mean age of 50 years (range, 31-66 years), and the volunteers had a mean age of 50 years (range, 37-57 years). The area under the receiver operating characteristic curve, sensitivity, and specificity of reader 1, reader 2, and the QUS-based model were 0.80 (95% confidence interval [CI], 0.66-0.95), 0.72 (95% CI, 0.56-0.89), and 0.88 (95% CI, 0.72-1.04); 0.79 (95% CI, 0.66-0.93), 0.65 (95% CI, 0.47-0.82), and 0.84 (95% CI, 0.67-1.01); and 0.82 (95% CI, 0.80-0.85), 0.73, and 0.79, respectively.

CONCLUSIONS

An automated, computer-based QUS technique diagnosed LE with accuracy of 0.82. This technique could provide quantitative biomarkers for the characterization of LE disease.

Numerical and experimental investigation of impacts of nonlinear scattering encapsulated microbubbles on Nakagami distribution

PURPOSE

The Nakagami statistical model and Nakagami shape parameter m have been widely used in linear tissue characterization and preliminarily characterized the envelope distributions of nonlinear encapsulated microbubbles (EMBs). However, the Nakagami distribution of nonlinear scattering EMBs lacked a systematical investigation. Thus, this study aimed to investigate the Nakagami distribution of EMBs and illustrate the impact of EMBs' nonlinearity on the Nakagami model.

METHOD

A group of simulated EMB phantoms and in vitro EMB dilutions with an increasing concentration distribution under various EMB nonlinearities, as regulated by acoustic parameters, were characterized by using the window-modulated compounding Greenwood-Durand estimator.

RESULTS

Raw envelope histograms of simulated and in vitro EMBs were well matched with the Nakagami distribution with a high correlation coefficient of 0.965 ± 0.021 (P < 0.005). The mean values and gradients of m parameters of simulated and in vitro EMBs were smaller than those of linear scatterers due to the stronger nonlinearity. These m values exhibited a quasi-linear improvement with the increase in second harmonic nonlinear-to-linear component ratio regulated by pulse lengths and excitation frequencies at low- and high-concentration conditions.

CONCLUSIONS

The Nakagami distribution was suitable for the EMBs characterization but the corresponding m parameter was affected by the EMBs' nonlinearity. These validations provided support and nonlinear impact assessment for the EMBs' characterization using the Nakagami statistical model in the future.

Quantitative Ultrasound in Ex Vivo Fibrotic Rabbit Livers

Liver fibrosis is the common result of chronic liver disease. Diagnosis and grading liver fibrosis for patient management is mainly based on blood tests and hepatic puncture-biopsy, which is particularly invasive. Quantitative ultrasound (QUS) techniques provide insight into tissue microstructure and are based on the frequency-based analysis of the signals from biologic tissues. This study aims to quantify how spectral-based QUS parameters change with fibrosis grade. The changes in QUS parameters of healthy and fibrotic rabbit liver samples were investigated and were compared with the changes in liver stiffness, using shear wave elastography. Overall, the acoustic concentration was found to decrease with increasing fibrosis grade, and the effective scatterer size was found to be higher in fibrotic livers when compared with normal liver. The result of this study indicates that the combination of three QUS parameters (stiffness, effective scatterer size and acoustic concentration) provides the best classification performance, especially for classifying healthy and fibrotic livers.

Quantitative Ultrasound and B-Mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (-24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = -0.68), entropy (R = -0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles.

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

OBJECTIVES

To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool.

METHODS

We compared, ranked, and validated the robustness of 5 texture-based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non-US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot-Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non-US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images.

RESULTS

Three of the 5 methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) had favorably robust performance when using the non-US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification).

CONCLUSIONS

Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.

Quantitative ultrasound imaging of cell-laden hydrogels and printed constructs

In the present work we have revisited the application of quantitative ultrasound imaging (QUI) to cellular hydrogels, by using the reference phantom method (RPM) in combination with a local attenuation compensation algorithm. The investigated biological samples consisted of cell-laden collagen hydrogels with PC12 neural cells. These cell-laden hydrogels were used to calibrate the integrated backscattering coefficient (IBC) as a function of cell density, which was then used to generate parametric images of local cell density. The image resolution used for QUI and its impact on the relative IBC error was also investigated. Another important contribution of our work was the monitoring of PC12 cell proliferation. The cell number estimates obtained via the calibrated IBC compared well with data obtained using a conventional quantitative method, the MTS assay. Evaluation of spectral changes as a function of culture time also provided additional information on the cell cluster size, which was found to be in close agreement with that observed by microscopy. Last but not least, we also applied QUI on a 3D printed cellular construct in order to illustrate its capabilities for the evaluation of bioprinted structures. STATEMENT OF SIGNIFICANCE: While there is intensive research in the areas of polymer science, biology, and 3D bio-printing, there exists a gap in available characterisation tools for the non-destructive inspection of biological constructs in the three-dimensional domain, on the macroscopic scale, and with fast data acquisition times. Quantitative ultrasound imaging is a suitable characterization technique for providing essential information on the development of tissue engineered constructs. These results provide a detailed and comprehensive guide on the capabilities and limitations of the technique.

System-Independent Ultrasound Attenuation Coefficient Estimation Using Spectra Normalization

Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as differentiating tumors and quantifying fat content in the liver. The two commonly used ACE methods in the ultrasound array imaging system are the spectral shift method and the reference-phantom-based methods. The spectra shift method estimates the central frequency downshift along depth, whereas the reference-phantom-based methods use a well-calibrated phantom to cancel system dependent effects in attenuation estimation. In this study, we propose a novel system-independent ACE technique based on spectra normalization of different frequencies. This technique does not require a reference phantom for normalization. The power of each frequency component is normalized by the power of an adjacent frequency component in the spectrum to cancel system-dependent effects, such as focusing and time gain compensation (TGC). This method is referred to as the reference frequency method (RFM), and its performance has been evaluated in phantoms and in vivo liver studies. The RFM technique can be applied to various transducer geometries (e.g., linear or curved arrays) with different beam patterns (e.g., focused or unfocused).

Exploiting Ballou's rule for better tissue classification

Ultrasound tissue characterization based on the coefficient of nonlinearity, β_n = 1 + B/2A, has been demonstrated to produce added diagnostic value due to its large variation and sensitivity to tissue structure. However, the parameter has been observed to be significantly correlated to the speed of sound and density. These relationships are analyzed empirically as well as theoretically by developing a pressure-density relation based on a thermodynamic model and the Mie intermolecular potential. The results indicate that for many soft tissues, the coefficient of nonlinearity is largely determined by the isentropic compressibility, κ_s. Consequently, for tissue characterization, estimating the nonlinear response of the medium, given by β_p = β_nκ_s, appears to be beneficial due to correlated quantities.

Sizing biological cells using a microfluidic acoustic flow cytometer

We describe a new technique that combines ultrasound and microfluidics to rapidly size and count cells in a high-throughput and label-free fashion. Using 3D hydrodynamic flow focusing, cells are streamed single file through an ultrasound beam where ultrasound scattering events from each individual cell are acquired. The ultrasound operates at a center frequency of 375 MHz with a wavelength of 4 μm; when the ultrasound wavelength is similar to the size of a scatterer, the power spectra of the backscattered ultrasound waves have distinct features at specific frequencies that are directly related to the cell size. Our approach determines cell sizes through a comparison of these distinct spectral features with established theoretical models. We perform an analysis of two types of cells: acute myeloid leukemia cells, where 2,390 measurements resulted in a mean size of 10.0 ± 1.7 μm, and HT29 colorectal cancer cells, where 1,955 measurements resulted in a mean size of 15.0 ± 2.3 μm. These results and histogram distributions agree very well with those measured from a Coulter Counter Multisizer 4. Our technique is the first to combine ultrasound and microfluidics to determine the cell size with the potential for multi-parameter cellular characterization using fluorescence, light scattering and quantitative photoacoustic techniques.

Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter

Background

Neoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects.

Methods

The B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses.

Results

Statistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91.

Conclusions

Quantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

Low fat but not soy protein isolate was an effective intervention to reduce nonalcoholic fatty liver disease progression in C57BL/6J mice: monitored by a novel quantitative ultrasound (QUS) method

Untreated nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) lead to irreversible liver damage. We hypothesized that a low-fat diet (LFD) or a high-fat diet (HFD) with soy protein isolate (SPI) would be an effective intervention to halt or reverse NAFLD progression. To test these hypotheses, we conducted 2 studies. In the first study, we fed an HFD to 7-week-old C57BL/6J mice to induce NAFLD compared to an LFD (control). Hepatic steatosis was monitored by quantitative ultrasound (QUS) scans (in vivo and ex vivo). Animals were euthanized after 0, 2, 4, and 6 weeks of feeding. In the second study, 7-week-old mice were randomized onto an LFD or HFD with SPI intervention after 4 weeks of feeding HFD. Animals from each group were scanned with QUS and euthanized after 4, 9, and 12 weeks of feeding. Animals fed the HFD developed NAFLD (100%) and NASH (80%) characterized by increased liver weight, lipid accumulation, and histological scores for inflammation by 4 weeks in the first study. In the second study, the LFD ameliorated this NAFLD phenotype after 5 weeks of feeding; however, the SPI intervention failed to significantly attenuate NAFLD. QUS parameters were significantly increased with the HFDs (P < .05) and steatosis grade (P < .05) and were positively correlated with hepatic lipid concentrations. In conclusion, dietary modification may be effective at reversing NAFLD and NASH at early stages. Furthermore, QUS may become a valuable tool to track hepatic steatosis. Additional studies are needed to further evaluate the effectiveness of these interventions.

Inter-platform reproducibility of ultrasonic attenuation and backscatter coefficients in assessing NAFLD

OBJECTIVES

To assess inter-platform reproducibility of ultrasonic attenuation coefficient (AC) and backscatter coefficient (BSC) estimates in adults with known/suspected nonalcoholic fatty liver disease (NAFLD).

METHODS

This HIPAA-compliant prospective study was approved by an institutional review board; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with clinical ultrasound scanner platforms from two manufacturers. Each participant was scanned by the same trained sonographer who performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. Each data acquisition recorded a B-mode image and the underlying radio frequency (RF) data. AC and BSC were calculated using the reference phantom method. Inter-platform reproducibility was evaluated for AC and log-transformed BSC (logBSC = 10log₁₀BSC) by intraclass correlation coefficient (ICC), Pearson's correlation, Bland-Altman analysis with computation of limits of agreement (LOAs), and within-subject coefficient of variation (wCV; applicable to AC).

RESULTS

Sixty-four participants were enrolled. Mean AC values measured using the two platforms were 0.90 ± 0.13 and 0.94 ± 0.15 dB/cm/MHz while mean logBSC values were - 30.6 ± 5.0 and - 27.9 ± 5.6 dB, respectively. Inter-platform ICC was 0.77 for AC and 0.70 for log-transformed BSC in terms of absolute agreement. Pearson's correlation coefficient was 0.81 for AC and 0.80 for logBSC. Ninety-five percent LOAs were - 0.21 to 0.13 dB/cm/MHz for AC, and - 9.48 to 3.98 dB for logBSC. The wCV was 7% for AC.

CONCLUSIONS

Hepatic AC and BSC are reproducible across two different ultrasound platforms in adults with known or suspected NAFLD.

KEY POINTS

• Ultrasonic attenuation coefficient and backscatter coefficient are reproducible between two different ultrasound platforms in adults with NAFLD. • This inter-platform reproducibility may qualify quantitative ultrasound biomarkers for generalized clinical application in patients with suspected/known NAFLD.

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

Echo statistics associated with discrete scatterers: A tutorial on physics-based methods

When a beam emitted from an active monostatic sensor system sweeps across a volume, the echoes from scatterers present will fluctuate from ping to ping due to various interference phenomena and statistical processes. Observations of these fluctuations can be used, in combination with models, to infer properties of the scatterers such as numerical density. Modeling the fluctuations can also help predict system performance and associated uncertainties in expected echoes. This tutorial focuses on "physics-based statistics," which is a predictive form of modeling the fluctuations. The modeling is based principally on the physics of the scattering by individual scatterers, addition of echoes from randomized multiple scatterers, system effects involving the beampattern and signal type, and signal theory including matched filter processing. Some consideration is also given to environment-specific effects such as the presence of boundaries and heterogeneities in the medium. Although the modeling was inspired by applications of sonar in the field of underwater acoustics, the material is presented in a general form, and involving only scalar fields. Therefore, it is broadly applicable to other areas such as medical ultrasound, non-destructive acoustic testing, in-air acoustics, as well as radar and lasers.

Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound

Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.

Multiparametric QUS Analysis for Placental Tissue Characterization

Multiparametric Quantitative Ultrasound (QUS) holds promise for characterizing placental tissue and detecting placental disorders. In this study, we simultaneously extract two qualitatively different QUS parameters, namely attenuation coefficient estimate (ACE) and shear wave speed from ultrasound radio frequency data acquired using a shear wave vibro elastography (SWAVE) method. The study comprised data from 59 post-delivery clinically normal placentas. The shear wave speed was found to be equal to 1.74 ± 0.13 m/s whereas the attenuation coefficient estimate was 0.57 ± 0.48 dB/cm-MHz. This provides a baseline for future studies of placental disorders.

High-Frequency Ultrasound Imaging for Examination of Early Dental Caries

The extent of dental tissue destruction during the treatment of white spot lesions (WSLs) increases with the severity of the lesion. If the depth and shape of WSLs can be predicted with a noninvasive diagnostic method before dental caries treatment, more conservative interventions can be planned. Given the superiority of high-frequency ultrasound (HFUS) imaging in observing the internal structures of the body, the present study aimed to verify the possibility of HFUS imaging to examine the depth and shape of WSLs. We prepared tooth samples and developed a biomicroscopic system with a HFUS transducer to obtain images of normal and WSL regions. HFUS images were compared with conventional ultrasound images and micro-computed tomography images. HFUS distinctly differentiated demineralization within WSL and normal regions. WSL depth calculated in the micro-computed tomography image was similar to that in HFUS. This study revealed that HFUS imaging has the potential to detect early dental caries and offer information on the invasion depth of early dental caries quantitatively.

Power Spectrum Consistency among Systems and Transducers

Use of the reference phantom method for computing acoustic attenuation and backscatter is widespread. However, clinical application of these methods has been limited by the need to acquire reference phantom data. We determined that the data acquired from 11 transducers of the same model and five clinical ultrasound systems of the same model produce equivalent estimates of reference phantom power spectra. We describe that the contribution to power spectral density variance among systems and transducers equals that from speckle variance with 59 uncorrelated echo signals. Thus, when the number of uncorrelated lines of data is small, speckle variance will dominate the power spectral density estimate variance introduced by different systems and transducers. These results suggest that, at least for this particular transducer and imaging system combination, one set of reference phantom calibration data is highly representative of the average among equivalent transducers and systems that are in good working order.

Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming

One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties, such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissue attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. To overcome these limitations, in this paper, we incorporate the prior information of piecewise continuity of QUS parameters as a regularization term into our cost function. We further propose to calculate this cost function using dynamic programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a published least squares method in terms of both estimation bias and variance.

Quantification of Muscle Tissue Properties by Modeling the Statistics of Ultrasound Image Intensities Using a Mixture of Gamma Distributions in Children With and Without Cerebral Palsy

OBJECTIVES

To investigate whether quantitative ultrasound (US) imaging, based on the envelope statistics of the backscattered US signal, can describe muscle properties in typically developing children and those with cerebral palsy (CP).

METHODS

Radiofrequency US data were acquired from the rectus femoris muscle of children with CP (n = 22) and an age-matched cohort without CP (n = 14) at rest and during maximal voluntary isometric contraction. A mixture of gamma distributions was used to model the histogram of the echo intensities within a region of interest in the muscle.

RESULTS

Muscle in CP had a heterogeneous echo texture that was significantly different from that in healthy controls (P < .001), with larger deviations from Rayleigh scattering. A mixture of 2 gamma distributions showed an excellent fit to the US intensity, and the shape and rate parameters were significantly different between CP and control groups (P < .05). The rate parameters for both the single gamma distribution and mixture of gamma distributions were significantly higher for contracted muscles compared to resting muscles, but there was no significant interaction between these factors (CP and muscle contraction) for a mixed-model analysis of variance.

CONCLUSIONS

Ultrasound tissue characterization indicates a more disorganized architecture and increased echogenicity in muscles in CP, consistent with previously documented increases in fibrous infiltration and connective tissue changes in this population. Our results indicate that quantitative US can be used to objectively differentiate muscle architecture and tissue properties.

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

PURPOSE

The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.

METHODS

We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level.

RESULTS

The area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively.

CONCLUSIONS

The proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.

Computer-Aided Theragnosis Based on Tumor Volumetric Information in Breast Cancer

OBJECTIVE

A computer-assisted technology has recently been proposed for the assessment of therapeutic responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC). The system, however, extracted features from individual scans in a tumor irrespective of its relation to the other scans of the same patient, ignoring the volumetric information. This study addresses this problem by introducing a novel engineered texton-based method in order to account for volumetric information in the design of textural descriptors to represent tumor scans.

METHODS

A noninvasive computer-aided-theragnosis (CAT) system was developed by employing multiparametric QUS spectral and backscatter coefficient maps. The proceeding was composed of two subdictionaries: one built on the "pretreatment" and another on "week " scans, where was 1, 4, or 8. The learned dictionary of each patient was subsequently used to compute the model (histogram of textons) for each scan of the patient. Advanced machine learning techniques including a kernel-based dissimilarity measure to estimate the distances between "pretreatment" and "mid-treatment" scans as an indication of treatment effectiveness, learning from imbalanced data, and supervised learning were subsequently employed on the texton-based features.

RESULTS

The performance of the CAT system was tested using statistical tests of significance and leave-one-subject-out (LOSO) classification on 56 LABC patients. The proposed texton-based CAT system indicated significant differences in changes between the responding and nonresponding patient populations and achieved high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. Specifically, the CAT system achieved the area under curve of 0.81, 0.83, and 0.85 on weeks 1, 4, and 8, respectively.

CONCLUSION

The proposed texton-based CAT system accounted for the volumetric information in "pretreatment" and "mid-treatment" scans of each patient. It was demonstrated that this attribute of the CAT system could boost its performance compared to the cases that the features were extracted from solely individual scans.

Nonlocal Coherent Denoising of RF Data for Ultrasound Elastography

Ultrasound elastography infers mechanical properties of living tissues from ultrasound radiofrequency (RF) data recorded while the tissues are undergoing deformation. A challenging yet critical step in ultrasound elastography is to estimate the tissue displacement (or, equivalently the time delay estimate) fields from pairs of RF data. The RF data are often corrupted with noise, which causes the displacement estimator to fail in many in vivo experiments. To address this problem, we present a nonlocal, coherent denoising approach based on Bayesian estimation to reduce the impact of noise. Despite incoherent denoising algorithms that smooth the B-mode images, the proposed denoising algorithm is used to suppress noise while maintaining useful information such as speckle patterns. We refer to the proposed approach as COherent Denoising for Elastography (CODE) and evaluate its performance when CODE is used in conjunction with the two state-of-art elastography algorithms, namely: (i) GLobal Ultrasound Elastography (GLUE) and (ii) Dynamic Programming Analytic Minimization elastography (DPAM). Our results show that CODE substantially improves the strain result of both GLUE and DPAM.

Quantitative Measurement of Erythrocyte Aggregation as a Systemic Inflammatory Marker by Ultrasound Imaging: A Systematic Review

This systematic review is aimed at answering two questions: (i) Is erythrocyte aggregation a useful biomarker in assessing systemic inflammation? (ii) Does quantitative ultrasound imaging provide the non-invasive option to measure erythrocyte aggregation in real time? The search was executed through bibliographic electronic databases CINAHL, EMB Review, EMBASE, MEDLINE, PubMed and the grey literature. The majority of studies correlated elevated erythrocyte aggregation with inflammatory blood markers for several pathologic states. Some studies used "erythrocyte aggregation" as an established marker of systemic inflammation. There were limited but promising articles regarding the use of quantitative ultrasound spectroscopy to monitor erythrocyte aggregation. Similarly, there were limited studies that used other ultrasound techniques to measure systemic inflammation. The quantitative measurement of erythrocyte aggregation has the potential to be a routine clinical marker of inflammation as it can reflect the cumulative inflammatory dynamics in vivo, is relatively simple to measure, is cost-effective and has a rapid turnaround time. Technologies like quantitative ultrasound spectroscopy that can measure erythrocyte aggregation non-invasively and in real time may offer the advantage of continuous monitoring of the inflammation state and, thus, may help in rapid decision making in a critical care setup.

Three-dimensional Visualization of Ultrasound Backscatter Statistics by Window-modulated Compounding Nakagami Imaging

In this study, the window-modulated compounding (WMC) technique was integrated into three-dimensional (3D) ultrasound Nakagami imaging for improving the spatial visualization of backscatter statistics. A 3D WMC Nakagami image was produced by summing and averaging a number of 3D Nakagami images (number of frames denoted as N) formed using sliding cubes with varying side lengths ranging from 1 to N times the transducer pulse. To evaluate the performance of the proposed 3D WMC Nakagami imaging method, agar phantoms with scatterer concentrations ranging from 2 to 64 scatterers/mm³ were made, and six stages of fatty liver (zero, one, two, four, six, and eight weeks) were induced in rats by methionine-choline-deficient diets (three rats for each stage, total n = 18). A mechanical scanning system with a 5-MHz focused single-element transducer was used for ultrasound radiofrequency data acquisition. The experimental results showed that 3D WMC Nakagami imaging was able to characterize different scatterer concentrations. Backscatter statistics were visualized with various numbers of frames; N = 5 reduced the estimation error of 3D WMC Nakagami imaging in visualizing the backscatter statistics. Compared with conventional 3D Nakagami imaging, 3D WMC Nakagami imaging improved the image smoothness without significant image resolution degradation, and it can thus be used for describing different stages of fatty liver in rats.

Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations.

Performance Evaluations on Using Entropy of Ultrasound Log-Compressed Envelope Images for Hepatic Steatosis Assessment: An In Vivo Animal Study

Ultrasound B-mode imaging based on log-compressed envelope data has been widely applied to examine hepatic steatosis. Modeling raw backscattered signals returned from the liver parenchyma by using statistical distributions can provide additional information to assist in hepatic steatosis diagnosis. Since raw data are not always available in modern ultrasound systems, information entropy, which is a widely known nonmodel-based approach, may allow ultrasound backscattering analysis using B-scan for assessing hepatic steatosis. In this study, we explored the feasibility of using ultrasound entropy imaging constructed using log-compressed backscattered envelopes for assessing hepatic steatosis. Different stages of hepatic steatosis were induced in male Wistar rats fed with a methionine- and choline-deficient diet for 0 (i.e., normal control) and 1, 1.5, and 2 weeks (n = 48; 12 rats in each group). In vivo scanning of rat livers was performed using a commercial ultrasound machine (Model 3000, Terason, Burlington, MA, USA) equipped with a 7-MHz linear array transducer (Model 10L5, Terason) for ultrasound B-mode and entropy imaging based on uncompressed (H_E image) and log-compressed envelopes (H_B image), which were subsequently compared with histopathological examinations. Receiver operating characteristic (ROC) curve analysis and areas under the ROC curves (AUC) were used to assess diagnostic performance levels. The results showed that ultrasound entropy imaging can be used to assess hepatic steatosis. The AUCs obtained from H_E imaging for diagnosing different steatosis stages were 0.93 (≥mild), 0.89 (≥moderate), and 0.89 (≥severe), respectively. H_B imaging produced AUCs ranging from 0.74 (≥mild) to 0.84 (≥severe) as long as a higher number of bins was used to reconstruct the signal histogram for estimating entropy. The results indicated that entropy use enables ultrasound parametric imaging based on log-compressed envelope signals with great potential for diagnosing hepatic steatosis.

Spatial Angular Compounding Technique for H-Scan Ultrasound Imaging

H-Scan is a new ultrasound imaging technique that relies on matching a model of pulse-echo formation to the mathematics of a class of Gaussian-weighted Hermite polynomials. This technique may be beneficial in the measurement of relative scatterer sizes and in cancer therapy, particularly for early response to drug treatment. Because current H-scan techniques use focused ultrasound data acquisitions, spatial resolution degrades away from the focal region and inherently affects relative scatterer size estimation. Although the resolution of ultrasound plane wave imaging can be inferior to that of traditional focused ultrasound approaches, the former exhibits a homogeneous spatial resolution throughout the image plane. The purpose of this study was to implement H-scan using plane wave imaging and investigate the impact of spatial angular compounding on H-scan image quality. Parallel convolution filters using two different Gaussian-weighted Hermite polynomials that describe ultrasound scattering events are applied to the radiofrequency data. The H-scan processing is done on each radiofrequency image plane before averaging to get the angular compounded image. The relative strength from each convolution is color-coded to represent relative scatterer size. Given results from a series of phantom materials, H-scan imaging with spatial angular compounding more accurately reflects the true scatterer size caused by reductions in the system point spread function and improved signal-to-noise ratio. Preliminary in vivo H-scan imaging of tumor-bearing animals suggests this modality may be useful for monitoring early response to chemotherapeutic treatment. Overall, H-scan imaging using ultrasound plane waves and spatial angular compounding is a promising approach for visualizing the relative size and distribution of acoustic scattering sources.

Tumour Vascular Shutdown and Cell Death Following Ultrasound-Microbubble Enhanced Radiation Therapy

High-dose radiotherapy effects are regulated by acute tumour endothelial cell death followed by rapid tumour cell death instead of canonical DNA break damage. Pre-treatment with ultrasound-stimulated microbubbles (USMB) has enabled higher-dose radiation effects with conventional radiation doses. This study aimed to confirm acute and longitudinal relationships between vascular shutdown and tumour cell death following radiation and USMB in a wild type murine fibrosarcoma model using in vivo imaging.

Methods: Tumour xenografts were treated with single radiation doses of 2 or 8 Gy alone, or in combination with low-/high-concentration USMB. Vascular changes and tumour cell death were evaluated at 3, 24 and 72 h following therapy, using high-frequency 3D power Doppler and quantitative ultrasound spectroscopy (QUS) methods, respectively. Staining using in situ end labelling (ISEL) and cluster of differentiation 31 (CD31) of tumour sections were used to assess cell death and vascular distributions, respectively, as gold standard histological methods.

Results: Results indicated a decrease in the power Doppler signal of up to 50%, and an increase of more than 5 dBr in cell-death linked QUS parameters at 24 h for tumours treated with combined USMB and radiotherapy. Power Doppler and quantitative ultrasound results were significantly correlated with CD31 and ISEL staining results (p < 0.05), respectively. Moreover, a relationship was found between ultrasound power Doppler and QUS results, as well as between micro-vascular densities (CD31) and the percentage of cell death (ISEL) (R² 0.5-0.9).

Conclusions: This study demonstrated, for the first time, the link between acute vascular shutdown and acute tumour cell death using in vivo longitudinal imaging, contributing to the development of theoretical models that incorporate vascular effects in radiation therapy. Overall, this study paves the way for theranostic use of ultrasound in radiation oncology as a diagnostic modality to characterize vascular and tumour response effects simultaneously, as well as a therapeutic modality to complement radiation therapy.

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.

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.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

OBJECTIVE

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis.

METHODS

Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.

RESULTS

The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C).

CONCLUSIONS

This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Effect of ultrasound frequency on the Nakagami statistics of human liver tissues

The analysis of the backscattered statistics using the Nakagami parameter is an emerging ultrasound technique for assessing hepatic steatosis and fibrosis. Previous studies indicated that the echo amplitude distribution of a normal liver follows the Rayleigh distribution (the Nakagami parameter m is close to 1). However, using different frequencies may change the backscattered statistics of normal livers. This study explored the frequency dependence of the backscattered statistics in human livers and then discussed the sources of ultrasound scattering in the liver. A total of 30 healthy participants were enrolled to undergo a standard care ultrasound examination on the liver, which is a natural model containing diffuse and coherent scatterers. The liver of each volunteer was scanned from the right intercostal view to obtain image raw data at different central frequencies ranging from 2 to 3.5 MHz. Phantoms with diffuse scatterers only were also made to perform ultrasound scanning using the same protocol for comparisons with clinical data. The Nakagami parameter-frequency correlation was evaluated using Pearson correlation analysis. The median and interquartile range of the Nakagami parameter obtained from livers was 1.00 (0.98-1.05) for 2 MHz, 0.93 (0.89-0.98) for 2.3 MHz, 0.87 (0.84-0.92) for 2.5 MHz, 0.82 (0.77-0.88) for 3.3 MHz, and 0.81 (0.76-0.88) for 3.5 MHz. The Nakagami parameter decreased with the increasing central frequency (r = -0.67, p < 0.0001). However, the effect of ultrasound frequency on the statistical distribution of the backscattered envelopes was not found in the phantom results (r = -0.147, p = 0.0727). The current results demonstrated that the backscattered statistics of normal livers is frequency-dependent. Moreover, the coherent scatterers may be the primary factor to dominate the frequency dependence of the backscattered statistics in a liver.

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.

Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters

PURPOSE

Statistical modeling of an ultrasound backscattered echo envelope is used for tissue characterization. However, in the presence of complex structures within the analyzed area, estimation of parameters is disturbed and unreliable, e.g., in the case of breast tumor classification. In order to improve the differentiation of breast lesions, the authors proposed a method based on the segmentation of homodyned K distribution parameter maps. Regions within lesions of different scattering properties were extracted and analyzed. In order to improve the classification, the best-performing features were selected from various regions and then combined.

METHODS

A radio-frequency data set consisting of 103 breast lesions was used in the authors' analysis. Maps of homodyned K distribution parameters were created using an algorithm based on signal-to-noise ratio, kurtosis, and skewness of fractional-order envelope moments. A Markov random field model was used to segment parametric maps. Features of different segments were extracted and evaluated based on bootstrapping and the receiver operating characteristic curve. To determine the best-performing feature subset, the authors applied the joint mutual information criterion.

RESULTS

It was found that there were individual features which performed better than the ones commonly used for lesion characterization, like the parameter obtained through averaging of values over the whole lesion. The authors selected and discussed the best-performing features. Properties of different extracted regions were important and improved the distinction between benign and malignant tumors. The best performance was obtained by combining four features with the area under the receiver operating curve of 0.84.

CONCLUSIONS

The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions.

In vivo ultrasonic attenuation in cetacean soft tissues

In vivo ultrasonic attenuation was estimated for extracranial soft tissues of two Tursiops truncatus and one Delphinapterus leucas. Backscatter data was non-invasively collected as part of routine health-based ultrasound examinations using a transducer operating in the 1.6-3.7 MHz frequency range. Data collected over the proximal mandible and temporal regions was processed to yield attenuation estimates using a reference tissue phantom whose properties had been independently determined. The estimated attenuations were at the low end of the range of literature-reported values for mammalian fatty and connective tissues. A companion model-based analysis quantified errors arising from tissue composition and sound speed uncertainties.

Automatic Differential Diagnosis of Melanocytic Skin Tumors Using Ultrasound Data

We describe a novel automatic diagnostic system based on quantitative analysis of ultrasound data for differential diagnosis of melanocytic skin tumors. The proposed method has been tested on 160 ultrasound data sets (80 of malignant melanoma and 80 of benign melanocytic nevi). Acoustical, textural and shape features have been evaluated for each segmented lesion. Using parameters selected according to Mahalanobis distance and linear support vector machine classifier, we are able to differentiate malignant melanoma from benign melanocytic skin tumors with 82.4% accuracy (sensitivity = 85.8%, specificity = 79.6%). The results indicate that high-frequency ultrasound has the potential to be used for differential diagnosis of melanocytic skin tumors and to provide supplementary information on lesion penetration depth. The proposed system can be used as an additional tool for clinical decision support to improve the early-stage detection of malignant melanoma.