Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors

Speckle statistics of biological tissues in optical coherence tomography

The speckle statistics of optical coherence tomography images of biological tissue have been studied using several historical probability density functions. Here, we propose a new theoretical framework based on power-law functions, where we hypothesize that an underlying power-law distribution governs scattering from tissues. Thus, multi-scale scattering sites including the fractal branching vasculature will contribute to power-law probability distributions of speckle statistics. Specifically, these are the Burr type XII distribution for speckle amplitude, the Lomax distribution for intensity, and the generalized logistic distribution for log amplitude. Experimentally, these three distributions are fitted to histogram data from nine optical coherence tomography scans of various samples and biological tissues, in vivo and ex vivo. The distributions are also compared with classical models such as the Rayleigh, K, and gamma distributions. The results indicate that across OCT datasets of various tissue types, the proposed power-law distributions are more appropriate models yielding novel parameters for characterizing the physics of scattering from biological tissue. Thus, the overall framework brings to the field new biomarkers from OCT measures of speckle in tissues, grounded in basic biophysics and with wide applications to diagnostic imaging in clinical use.

A new CNN architecture for efficient classification of ultrasound breast tumor images with activation map clustering based prediction validation

Effective ultrasound (US) analysis for preliminary breast tumor diagnosis is constrained due to the presence of complex echogenic patterns. Implementing pretrained models of convolutional neural networks (CNNs) which mostly focuses on natural images and using transfer learning seldom gives good results in medical domain. In this work, a CNN architecture, StepNet, with step-wise incremental convolution layers for each downsampled block was developed for classification of breast tumors as benign/malignant. To increase noise robustness and as an improvement over existing methodologies, neutrosophic preprocessing was performed, and the enhanced images were appended to the original image during training and data augmentation. The final layers' activation maps are clustered using fuzzy c-means clustering which qualify as a validation method for the prediction of StepNet. Using neutrosophic preprocessing alone had increased the validation accuracy from 0.84 to 0.93, while using neutrosophic preprocessing and augmentation had increased the accuracy to 0.98. StepNet has comparably less training and validation time than other state of the art architectures and methods and shows an increase in prediction accuracy even for challenging isoechoic and hypoechoic tumors.

Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

OBJECTIVES

Quantitative image analysis is one of the methods to overcome the lack of objectivity of ultrasound (US). The aim of this study was to clarify the correlation between the features from a US image analysis and the histologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and differentiation of HCC smaller than 2 cm from borderline lesions.

METHODS

We retrospectively analyzed grayscale US images with histopathologic evidence of HCC or a precancerous lesion using ImageJ version 1.47 software (National Institutes of Health, Bethesda, MD).

RESULTS

A total of 148 nodules were included (borderline lesion, n = 31; early HCC [eHCC], n = 3; well-differentiated HCC [wHCC], n = 16; moderately differentiated HCC [mHCC], n = 79; and poorly differentiated HCC [pHCC], n = 19). A multivariate analysis selected lower minimum gray values (odds ratio [OR], 0.431; P = .003) and a higher standard deviation (OR, 1.880; P = .019) as predictors of HCC smaller than 2 cm. Median (range) minimum gray values of borderline lesions, eHCC, wHCC, mHCC, and pHCC were 29 (0-103), 7 (0-47), 6 (0-60), 10 (0-53), and 2 (0-38), respectively, and gradually decreased from borderline lesions to pHCC (P < 0.001). The multivariate analysis showed a higher aspect ratio (OR, 2.170; P = .001) and lower minimum gray value (OR, 0.475; P = .043) as predictors of MVI. An anechoic area diagnosed by a subjective evaluation was correlated with the minimum gray value (P < .0001). The proportion of the anechoic area gradually increased from eHCC to pHCC (P = .031).

CONCLUSIONS

In a US image analysis, HCC smaller than 2 cm had features of greater heterogeneity and a lower minimum gray value than borderline lesions. Moderately differentiated HCC was smoother than borderline lesions, and the anechoic area correlated with histologic grading. Microvascular invasion was correlated with a slender shape and a lower minimum gray value.

Scattering Signatures of Normal versus Abnormal Livers with Support Vector Machine Classification

Fifty years of research on the nature of backscatter from tissues has resulted in a number of promising diagnostic parameters. We recently introduced two analyses tied directly to the biophysics of ultrasound scattering: the H-scan, based on a matched filter approach to distinguishing scattering transfer functions, and the Burr distribution for quantification of speckle patterns. Together, these analyses can produce at least five parameters that are directly linked to the mathematics of ultrasound in tissue. These have been measured in vivo in 35 rat livers under normal conditions and after exposure to compounds that induce inflammation, fibrosis, and steatosis in varying combinations. A classification technique, the support vector machine, is employed to determine clusters of the five parameters that are signatures of the different liver conditions. With the multiparametric measurement approach and determination of clusters, the different types of liver pathology can be discriminated with 94.6% accuracy.

3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation

H-Scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3-D) H-scan US imaging approach for scatterer size estimation in volume space. Using a programmable research scanner (Vantage 256, Verasonics Inc, Kirkland, WA, USA) equipped with a custom volumetric imaging transducer (4 DL7, Vermon, Tours, France), raw radiofrequency (RF) data was collected for offline processing to generate H-scan US volumes. A deep convolutional neural network (CNN) was modified and used to achieve voxel mapping from the input H-scan US image to underlying scatterer size. Preliminary studies were conducted using homogeneous gelatin-based tissue-mimicking phantom materials embedded with acoustic scatterers of varying size (15 to 250 μm) and concentrations (0.1 to 1%). Two additional phantoms were embedded with 63 or 125 µm-sized microspheres and used to test CNN estimation accuracy. In vitro results indicate that 3-D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R² > 0.85, p < 0.03). The result of scatterer size estimation reveals that a CNN can achieve an average mapping accuracy of 93.3%. Overall, our preliminary in vitro findings reveal that 3-D H-scan US imaging allows the visualization of tissue scatterer patterns and incorporation of a CNN can be used to help estimate size of the acoustic scattering objects.

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.

Radiofrequency ablation with four electrodes as a building block for matrix radiofrequency ablation: Ex vivo liver experiments and finite element method modelling. Influence of electric and activation mode on coagulation size and geometry

PURPOSE

Radiofrequency ablation (RFA) is increasingly being used to treat unresectable liver tumors. Complete ablation of the tumor and a safety margin is necessary to prevent local recurrence. With current electrodes, size and shape of the ablation zone are highly variable leading to unsatisfactory local recurrence rates, especially for tumors >3 cm. In order to improve predictability, we recently developed a system with four simple electrodes with complete ablation in between the electrodes. This rather small but reliable ablation zone is considered as a building block for matrix radiofrequency ablation (MRFA). In the current study we explored the influence of the electric mode (monopolar or bipolar) and the activation mode (consecutive, simultaneous or switching) on the size and geometry of the ablation zone.

MATERIALS AND METHODS

The four electrode system was applied in ex vivo bovine liver. The electric and the activation mode were changed one by one, using constant power of 50 W in all experiments. Size and geometry of the ablation zone were measured. Finite element method (FEM) modelling of the experiment was performed.

RESULTS

In ex vivo liver, a complete and predictable coagulation zone of a 3 × 2 × 2 cm block was obtained most efficiently in the bipolar simultaneous mode due to the combination of the higher heating efficacy of the bipolar mode and the lower impedance by the simultaneous activation of four electrodes, as supported by the FEM simulation.

CONCLUSIONS

In ex vivo liver, the four electrode system used in a bipolar simultaneous mode offers the best perspectives as building block for MRFA. These results should be confirmed by in vivo experiments.

Spatio-Temporal Segmentation in 3-D Echocardiographic Sequences Using Fractional Brownian Motion

An important aspect for an improved cardiac functional analysis is the accurate segmentation of the left ventricle (LV). A novel approach for fully-automated segmentation of the LV endocardium and epicardium contours is presented. This is mainly based on the natural physical characteristics of the LV shape structure. Both sides of the LV boundaries exhibit natural elliptical curvatures by having details on various scales, i.e. exhibiting fractal-like characteristics. The fractional Brownian motion (fBm), which is a non-stationary stochastic process, integrates well with the stochastic nature of ultrasound echoes. It has the advantage of representing a wide range of non-stationary signals and can quantify statistical local self-similarity throughout the time-sequence ultrasound images. The locally characterized boundaries of the fBm segmented LV were further iteratively refined using global information by means of second-order moments. The method is benchmarked using synthetic 3D+time echocardiographic sequences for normal and different ischemic cardiomyopathy, and results compared with state-of-the-art LV segmentation. Furthermore, the framework was validated against real data from canine cases with expert-defined segmentations and demonstrated improved accuracy. The fBm-based segmentation algorithm is fully automatic and has the potential to be used clinically together with 3D echocardiography for improved cardiovascular disease diagnosis.

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.

Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data

Objectives

The purpose of this study was the automated generation and validation of parametric blood flow velocity maps, based on contrast-enhanced ultrasound (CEUS) scans.

Materials and Methods

Ethical approval for animal experiments was obtained. CEUS destruction-replenishment sequences were recorded in phantoms and three different tumor xenograft mouse models. Systematic pixel binning and intensity averaging was performed to generate parameter maps of blood flow velocities with different pixel resolution. The 95% confidence interval of the mean velocity, calculated on the basis of the whole tumor segmentation, served as ground truth for the different parameter maps.

Results

In flow phantoms the measured mean velocity values were only weakly influenced by the pixel resolution and correlated with real velocities (r² ≥ 0.94, p < 0.01). In tumor xenografts, however, calculated mean velocities varied significantly (p < 0.0001), depending on the parameter maps' resolution. Pixel binning was required for all in vivo measurements to obtain reliable parameter maps and its degree depended on the tumor model.

Conclusion

Systematic pixel binning allows the automated identification of optimal pixel resolutions for parametric maps, supporting textural analysis of CEUS data. This approach is independent from the ultrasound setup and can be implemented in the software of other (clinical) ultrasound devices.