A statistical model for the ultrasonic backscattered echo from tissue containing microcalcifications

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.

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis

Acoustic structure quantification (ASQ) is a recently developed technique widely used for detecting liver fibrosis. Ultrasound Nakagami parametric imaging based on the Nakagami distribution has been widely used to model echo amplitude distribution for tissue characterization. We explored the feasibility of using ultrasound Nakagami imaging as a model-based ASQ technique for assessing liver fibrosis. Standard ultrasound examinations were performed on 19 healthy volunteers and 91 patients with chronic hepatitis B and C (n = 110). Liver biopsy and ultrasound Nakagami imaging analysis were conducted to compare the METAVIR score and Nakagami parameter. The diagnostic value of ultrasound Nakagami imaging was evaluated using receiver operating characteristic (ROC) curves. The Nakagami parameter obtained through ultrasound Nakagami imaging decreased with an increase in the METAVIR score (p < 0.0001), representing an increase in the extent of pre-Rayleigh statistics for echo amplitude distribution. The area under the ROC curve (AUROC) was 0.88 for the diagnosis of any degree of fibrosis (≥F1), whereas it was 0.84, 0.69, and 0.67 for ≥F2, ≥F3, and ≥F4, respectively. Ultrasound Nakagami imaging is a model-based ASQ technique that can be beneficial for the clinical diagnosis of early liver fibrosis.

Effects of Estimators on Ultrasound Nakagami Imaging in Visualizing the Change in the Backscattered Statistics from a Rayleigh Distribution to a Pre-Rayleigh Distribution

Ultrasound Nakagami imaging has recently attracted interest as an imaging technique for analyzing envelope statistics. Because the presence of structures has a strong effect on estimation of the Nakagami parameter, previous studies have indicated that Nakagami imaging should be used specifically for characterization of soft tissues with fewer structures, such as liver tissues. Typically, changes in the properties of the liver parenchyma cause the backscattered statistics to transform from a Rayleigh distribution to a pre-Rayleigh distribution, and this transformation can be visualized using a Nakagami imaging technique. However, different estimators result in different estimated values; thus, the performance of a Nakagami image may depend on the type of estimator used. This study explored the effects of various estimators on ultrasound Nakagami imaging to describe the backscattered statistics as they change from a Rayleigh distribution to a pre-Rayleigh distribution. Simulations and clinical measurements involving patients with liver fibrosis (n = 85) yielded image data that were used to construct B-mode and conventional Nakagami images based on the moment estimator (denoted as mINV images) and maximum-likelihood estimator (denoted as mML images). In addition, novel window-modulated compounding Nakagami images based on the moment estimator (denoted as mWMC images) were also obtained. The means and standard deviations of the Nakagami parameters were examined as a function of the backscattered statistics. The experimental results indicate that the mINV, mML and mWMC images enabled quantitative visualization of the change in backscattered statistics from a Rayleigh distribution to a pre-Rayleigh distribution. Importantly, the mWMC image is superior to both mINV and mML images because it simultaneously realizes sensitive detection of the backscattered statistics and a reduction of estimation variance for image smoothness improvement. We therefore recommend using mWMC image as a novel strategy in Nakagami imaging technique for liver tissue characterization.

Effects of fatty infiltration in human livers on the backscattered statistics of ultrasound imaging

Ultrasound imaging has been widely applied to screen fatty liver disease. Fatty liver disease is a condition where large vacuoles of triglyceride fat accumulate in liver cells, thereby altering the arrangement of scatterers and the corresponding backscattered statistics. In this study, we used ultrasound Nakagami imaging to explore the effects of fatty infiltration in human livers on the statistical distribution of backscattered signals. A total of 107 patients volunteered to participate in the experiments. The livers were scanned using a clinical ultrasound scanner to obtain the raw backscattered signals for ultrasound B-mode and Nakagami imaging. Clinical scores of fatty liver disease for each patient were determined according to a well-accepted sonographic scoring system. The results showed that the Nakagami image can visualize the local backscattering properties of liver tissues. The Nakagami parameter increased from 0.62 ± 0.11 to 1.02 ± 0.07 as the fatty liver disease stage increased from normal to severe, indicating that the backscattered statistics vary from pre-Rayleigh to Rayleigh distributions. A significant positive correlation (correlation coefficient ρ = 0.84; probability value (p value) < 0.0001) exists between the degree of fatty infiltration and the Nakagami parameter, suggesting that ultrasound Nakagami imaging has potentials in future applications in fatty liver disease diagnosis.

Statistics of boundaries in ultrasonic B-scan images

The existence of edges and boundaries in regions of interest (ROIs) in B-scan images alters the statistics of the backscattered echo from the ROI. Boundaries are the result of at least two different types of scattering scenarios in tissue, and the Nakagami model, which is being used extensively in ultrasound, is unlikely to fit the statistics of the backscattered echo under these conditions. Furthermore, there are very few other statistical models exist that describe the statistics of the backscattered echo from regions containing boundaries. In this work, the gamma mixture density and the recently proposed McKay density are explored as two viable models to fill this void. Justifications of these models are presented along with methods for estimating their parameters. Random number simulations and studies on tissue-mimicking phantoms indicate that the McKay and gamma mixture densities are the best for the modeling of the backscattered echo intensity when boundaries are present in the regions of interest.

Early detection of liver fibrosis in rats using 3-D ultrasound Nakagami imaging: a feasibility evaluation

We investigated the feasibility of using 3-D ultrasound Nakagami imaging to detect the early stages of liver fibrosis in rats. Fibrosis was induced in livers of rats (n = 60) by intraperitoneal injection of 0.5% dimethylnitrosamine (DMN). Group 1 was the control group, and rats in groups 2-6 received DMN injections for 1-5 weeks, respectively. Each rat was sacrificed to perform 3-D ultrasound scanning of the liver in vitro using a single-element transducer of 6.5 MHz. The 3-D raw data acquired at a sampling rate of 50 MHz were used to construct 3-D Nakagami images. The liver specimen was further used for histologic analysis with hematoxylin and eosin and Masson staining to score the degree of liver fibrosis. The results indicate that the Metavir scores of the hematoxylin and eosin-stained sections in Groups 1-4 were 0 (defined as early liver fibrosis in this study), and those in groups 5 and 6 ranged from 1 to 2 and 2 to 3, respectively. To quantify the degree of early liver fibrosis, the histologic sections with Masson stain were analyzed to calculate the number of fiber-related blue pixels. The number of blue pixels increased from (2.36 ± 0.79) × 10(4) (group 1) to (7.68 ± 2.62) × 10(4) (group 4) after DMN injections for 3 weeks, indicating that early stages of liver fibrosis were successfully induced in rats. The Nakagami parameter increased from 0.36 ± 0.02 (group 1) to 0.55 ± 0.03 (group 4), with increasing numbers of blue pixels in the Masson-stained sections (p-value < 0.05, t-test). We concluded that 3-D Nakagami imaging has potential in the early detection of liver fibrosis in rats and may serve as an image-based pathologic model to visually track fibrosis formation and growth.

Discrimination of breast microcalcifications using a strain-compounding technique with ultrasound speckle factor imaging

The usefulness of breast ultrasound could be extended by improving the detection of microcalcifications by being able to detect and enhance microcalcifications while simultaneously eliminating hyperechoic spots (e.g., speckle noise and fibrocystic changes) that can be mistaken for microcalcifications (i.e., false microcalcifications). This study investigated the use of a strain-compounding technique with speckle factor (SF) imaging to analyze the degree of scatterer redistributions in breast tissues under strain conditions for identifying microcalcifications and false microcalcifications. The efficacy of the proposed method was tested by collecting raw data of ultrasound backscattered signals from 26 lesions at BI-RADS category 4 or 5 with suspicious microcalcifications. The different strain conditions were created by applying manual compression to deform the breast lesion. For each region in which microcalcifications were suspected, estimates of the SNR of the strain-compounding B-scan images and estimates of the mean SF (SFavg) in the strain-compounding SF images were calculated. Compared with microcalcifications, the severity of speckle of the false microcalcifications would be easily degraded under compressive strain conditions. The results demonstrated that the SNR estimates in the strain-compounding B-scan images for microcalcifications and false microcalcifications were 5.22 ± 1.04 (mean ± standard deviation) and 4.62 ± 1.09, respectively; the corresponding SFavg estimates in the strain-compounding SF images were 0.47 ± 0.10 and 0.22 ± 0.10 (p < 0.01). The mean area under the receiver operating characteristic curve using the SNR estimate was 0.71, whereas that using the SFavg estimate was 0.94. These findings indicate that the strain-compounding SF imaging method is more effective at discriminating between microcalcifications and false microcalcifications.