Sensitive ultrasonic delineation of steroid treatment in living dystrophic mice with energy-based and entropy-based radio frequency signal processing

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

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.

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 firearm discharge detection and classification in an enclosed environment

Two different signal processing algorithms are described for detection and classification of acoustic signals generated by firearm discharges in small enclosed spaces. The first is based on the logarithm of the signal energy. The second is a joint entropy. The current study indicates that a system using both signal energy and joint entropy would be able to both detect weapon discharges and classify weapon type, in small spaces, with high statistical certainty.

Imaging deep skeletal muscle structure using a high-sensitivity ultrathin side-viewing optical coherence tomography needle probe

We have developed an extremely miniaturized optical coherence tomography (OCT) needle probe (outer diameter 310 µm) with high sensitivity (108 dB) to enable minimally invasive imaging of cellular structure deep within skeletal muscle. Three-dimensional volumetric images were acquired from ex vivo mouse tissue, examining both healthy and pathological dystrophic muscle. Individual myofibers were visualized as striations in the images. Degradation of cellular structure in necrotic regions was seen as a loss of these striations. Tendon and connective tissue were also visualized. The observed structures were validated against co-registered hematoxylin and eosin (H&E) histology sections. These images of internal cellular structure of skeletal muscle acquired with an OCT needle probe demonstrate the potential of this technique to visualize structure at the microscopic level deep in biological tissue in situ.

Quantitative assessment of muscle damage in the mdx mouse model of Duchenne muscular dystrophy using polarization-sensitive optical coherence tomography

Minimally invasive, high-resolution imaging of muscle necrosis has the potential to aid in the assessment of diseases such as Duchenne muscular dystrophy. Undamaged muscle tissue possesses high levels of optical birefringence due to its anisotropic ultrastructure, and this birefringence decreases when the tissue undergoes necrosis. In this study, we present a novel technique to image muscle necrosis using polarization-sensitive optical coherence tomography (PS-OCT). From PS-OCT scans, our technique is able to quantify the birefringence in muscle tissue, generating an image indicative of the tissue ultrastructure, with areas of abnormally low birefringence indicating necrosis. The technique is demonstrated on excised skeletal muscles from exercised dystrophic mdx mice and control C57BL/10ScSn mice with the resulting images validated against colocated histological sections. The technique additionally gives a measure of the proportion (volume fraction) of necrotic tissue within the three-dimensional imaging field of view. The percentage necrosis assessed by this technique is compared against the percentage necrosis obtained from manual assessment of histological sections, and the difference between the two methods is found to be comparable to the interobserver variability of the histological assessment. This is the first published demonstration of PS-OCT to provide automated assessment of muscle necrosis.

Joint entropy of continuously differentiable ultrasonic waveforms

This study is based on an extension of the concept of joint entropy of two random variables to continuous functions, such as backscattered ultrasound. For two continuous random variables, X and Y, the joint probability density p(x,y) is ordinarily a continuous function of x and y that takes on values in a two dimensional region of the real plane. However, in the case where X=f(t) and Y=g(t) are both continuously differentiable functions, X and Y are concentrated exclusively on a curve, γ(t)=(f(t),g(t)), in the x,y plane. This concentration can only be represented using a mathematically "singular" object such as a (Schwartz) distribution. Its use for imaging requires a coarse-graining operation, which is described in this study. Subsequently, removal of the coarse-graining parameter is accomplished using the ergodic theorem. The resulting expression for joint entropy is applied to several data sets, showing the utility of the concept for both materials characterization and detection of targeted liquid nanoparticle ultrasonic contrast agents. In all cases, the sensitivity of these techniques matches or exceeds, sometimes by a factor of two, that demonstrated in previous studies that employed signal energy or alternate entropic quantities.

Quantitative ultrasound of skeletal muscle: reliable measurements of calibrated muscle backscatter from different ultrasound systems

Widespread implementation of quantitative muscle ultrasonography in assessing skeletal muscle pathology is limited by an inability to replicate results between different ultrasound systems. We have developed a measurement of skeletal muscle pathology, calibrated muscle backscatter (cMB), which should be reproducible between different ultrasound systems. We compared the reliability of grayscale and cMB measurements between different ultrasound systems, configurations and region-of-interest (ROI) sizes. cMB of skeletal muscle was reliably measured (intraclass correlation coefficient [ICC] ≤0.98) despite very dissimilar grayscale levels (ICC ≤0.54). cMB reliability was highest between systems using similar settings (ICC: 0.82-0.98) and was lowest when transducer type varied (ICC: 0.47-0.71). Reliability was better from ROIs spanning a narrow range of depths compared with larger ranges. cMB measurements are more reliable than grayscale between different ultrasound systems and configurations. Measuring cMB could improve widespread implementation of quantitative ultrasound in assessments of skeletal muscle pathology.

Quantitative ultrasound visualization of cell death: emerging clinical applications for detection of cancer treatment response

Differentiable echogeneities exhibited by living and dead cells enables the monitoring of cell death response via quantitative ultrasound techniques at high-frequencies and recently at clinical range frequencies. Such capability can be potentially employed to provide rapid and quantitative functional information in real time, and at the patient bedside for evaluating therapy response early following treatment. This paper summarizes backgrounds on quantitative ultrasound visualization of cell death and highlights its potential capabilities for monitoring cancer treatment response, where favorable results have been reported, according to a recent pilot clinical study.

Use of smoothing splines for analysis of backscattered ultrasonic waveforms: application to monitoring of steroid treatment of dystrophic mice

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease characterized by progressive weakness and wasting of skeletal and cardiac muscle; boys present with weakness by the age of 5 years and, if left untreated, are unable to walk without assistance by the age of 10 years. Therapy for DMD has been primarily palliative, with oral steroids emerging as a first-line approach even though this treatment has serious side-effects. Consequently, low-cost imaging technology suitable for improved diagnosis and treatment monitoring of DMD would be of great value, especially in remote and underserved areas. Previously, we reported use of the logarithm of the signal energy, log [E(f)], and a new method for ultrasound signal characterization using entropy, H(f), to monitor prednisolone treatment of skeletal muscle in a dystrophin-deficient mouse model. Three groups were studied: mdx mice treated with prednisolone, a control group of mdx mice treated with saline, and a control group of wild-type mice treated with saline. It was found that both log [E(f)] and H(f) were required to statistically differentiate the three groups. In the current study, we show that preprocessing of the raw ultrasound using optimal smoothing splines before computation of either log [E(f)] or a rapidly computable variant of Hf, denoted I(f,∞), permits delineation of all three groups by either metric alone. This opens the way to the ultimate goal of this study, which is identification and implementation of new diagnostically sensitive algorithms on the new generation of low-cost hand-held clinical ultrasonic imaging systems.

Identification of muscle necrosis in the mdx mouse model of Duchenne muscular dystrophy using three-dimensional optical coherence tomography

Three-dimensional optical coherence tomography (3D-OCT) was used to image the structure and pathology of skeletal muscle tissue from the treadmill-exercised mdx mouse model of human Duchenne muscular dystrophy. Optical coherence tomography (OCT) images of excised muscle samples were compared with co-registered hematoxylin and eosin-stained and Evans blue dye fluorescence histology. We show, for the first time, structural 3D-OCT images of skeletal muscle dystropathology well correlated with co-located histology. OCT could identify morphological features of interest and necrotic lesions within the muscle tissue samples based on intrinsic optical contrast. These findings demonstrate the utility of 3D-OCT for the evaluation of small-animal skeletal muscle morphology and pathology, particularly for studies of mouse models of muscular dystrophy.

Quantitative ultrasound using backscatter analysis in Duchenne and Becker muscular dystrophy

Evaluation of ultrasound images of muscle with calibrated muscle backscatter (cMB) provides reproducible quantitative measurements of muscle pathology. Increased cMB is associated with greater muscle pathology. We used cMB to evaluate the severity of muscle pathology in 55 patients with Duchenne and Becker Muscular Dystrophy (D/BMD) compared to 77 controls. cMB was also compared to measurements of strength and function. cMB in DMD and BMD increased linearly with age and was higher than in controls when groups are compared. cMB increased twice as fast with age in DMD than in BMD. In DMD, cMB was higher with reduced function and strength. Ultrasound measurement of muscle pathology using cMB is a sensitive and objective quantitative technique for determining the severity of muscle pathology in dystrophinopathies. Longitudinal studies are required to determine the sensitivity of this measure to changes in pathology over time.

Quantitative muscle ultrasound in neuromuscular disorders using the parameters 'intensity', 'entropy', and 'fractal dimension'

BACKGROUND AND PURPOSE

Ultrasound is a useful non-invasive instrument in visualizing physiological and pathological morphology in skeletal muscle. Here, we evaluate the possibility that quantitative muscle ultrasound using the parameters 'intensity', 'entropy', and 'fractal dimension' is a feasible method to distinguish between dystrophic myopathies (DM), inflammatory myopathies (IM), and motor neuron disorders.

METHODS

Seven patients with IM, 12 patients with DM, nine patients with motor neuron diseases, and 24 healthy subjects underwent an identical ultrasound examination protocol, applied on gastrocnemius and tibialis anterior muscle. Analysis parameters were applied on grey scale images as well as on gradient images.

RESULTS

Statistical evaluation revealed no significant differences in the evaluated parameters for differentiation of the distinct disease groups. Compared with healthy controls however we found statistically significant differences between almost of all the investigated parameters, even in disease cases with clinically unaffected distal musculature.

CONCLUSION

The parameters are able to distinguish between healthy and affected musculature but not between distinct disease entities. Studies are needed to establish whether or not the parameters are helpful to monitor muscle involvement and disease progression in neuromuscular diseases.