Three-dimensional high-frequency backscatter and envelope quantification of cancerous human lymph nodes

Simulation of ultrasound backscatter coefficient measurement using the finite element method

Ultrasound backscatter coefficient (BSC) measurement is a method for assessing tissue morphology that can inform on pathologies such as cancer. The BSC measurement is, however, limited by the accuracy with which the investigator can normalise their results to account for frequency dependent effects of diffraction and attenuation whilst performing such measurements. We propose a simulation-based approach to investigate the potential sources of error in assessing the BSC. Presented is a tool for the 2D Finite Element (FE) simulation mimicking a BSC measurement using the planar reflector substitution method in reduced dimensionality. The results of this are verified against new derivations of BSC equations also in reduced dimensionality. These new derivations allow computation of BSC estimates based on the scattering from a 2D scattering area, a line reference reflector and a theoretical value for the BSC of a 2D distribution of scatterers. This 2D model was designed to generate lightweight simulations that allow rapid investigation of the factors associated with BSC measurement, allowing the investigator to generate large data sets in relatively short time scales. Under the conditions for an incoherent scattering medium, the simulations produced BSC estimates within 6% of the theoretical value calculated from the simulation domain, a result reproduced across a range of source f-numbers. This value of error compares well to both estimated errors from other simulation based approaches and to physical experiments. The mathematical and simulation models described here provide a theoretical and experimental framework for continued investigation into factors affecting the accuracy of BSC measurements.

Evaluation of Scatterer Parameters From Ultrasound Scattering Models Taking Into Account Scattering From Nuclei and Cells of Cell-Pellet Biophantoms and Ex Vivo Tumors

The Quantitative Ultrasound backscatter coefficient provides the capability to evaluate tissue microstructure parameters. Tissue-based scatterer parameters are extracted using ultrasound scattering models. It is challenging to correlate ultrasound scatterer parameters of tissue structures from optical-measured histology, possibly because of inappropriate scattering models or the presence of multiple scatterers. The objective of this study is to pursue the quantification of pertinent scatterer parameters with scattering models that consider ultrasound scattering from nuclei and cells. The concentric sphere model (CSM) and the structure factor model adapted for two types of scatterers (SFM2) are evaluated for cell-pellet biophantoms and ex vivo tumors of four cell lines: 4T1, JC, LMTK, and MAT. The structure factor model (SFM) was used for comparison. CSM and SFM2 provided scatterer parameters closer to histology (lower relative errors) for nucleus and cell radii and volume fractions than SFM but were not always accompanied by lower dispersion of the scatterer distribution (lower coefficient of variation). CSM and SFM2 quantified cell and nucleus radius and volume fraction parameters with lower relative error compared to SFM. For tumors, CSM provided better results than SFM2.

A magnetic phantom technique for investigating structural effects on quantitative ultrasound parameters

Media that contain ultrasound scatterers arranged in a regular spatial distribution can be considered as structured. Structural effects affect quantitative ultrasound parameters that reflect the microstructure properties. Prior studies examined structural effects using simulations or phantoms with fixed microarchitecture, focusing on a limited set of ultrasound parameters, with limited attention given to their underlying physical significance. This study aims to investigate the concordance of the physical interpretations of multiple quantitative ultrasound parameters experimentally by introducing a phantom type with an adjustable microarchitecture. The phantom consists of an aqueous solution containing superparamagnetic microspheres, acting as scatterers. The spatial arrangement of the magnetic particles is modified by applying an external magnetic field, therefore changing the degree of structure of the phantom. Quantitative ultrasound parameters are estimated in three different configurations: the magnetic field intensity is varied over time, strength, and orientation. In each experiment, the backscatter coefficient and the envelope quantitative ultrasound parameters are successfully extracted (R2 ≈ 0.94). Their physical interpretations are supported by microphotographs and geometrical considerations through concordant hypotheses. This study paves the way for the use of magnetic phantoms. This methodology could be followed to validate theoretical scattering models and the physical meanings of quantitative ultrasound parameters.

Breast Glandular and Ductal Volume Changes during the Menstrual Cycle: A Study in 48 Breasts Using Ultralow-Frequency Transmitted Ultrasound Tomography/Volography

The aim of this study was to show for the first time that low-frequency 3D-transmitted ultrasound tomography (3D UT, volography) can differentiate breast tissue types using tissue properties, accurately measure glandular and ductal volumes in vivo, and measure variation over time. Data were collected for 400 QT breast scans on 24 women (ages 18-71), including four (4) postmenopausal subjects, 6-10 times over 2+ months of observation. The date of onset of menopause was noted, and the cases were further subdivided into three (3) classes: pre-, post-, and peri-menopausal. The ducts and glands were segmented using breast speed of sound, attenuation, and reflectivity images and followed over several menstrual cycles. The coefficient of variation (CoV) for glandular tissue in premenopausal women was significantly larger than for postmenopausal women, whereas this is not true for the ductal CoV. The glandular standard deviation (SD) is significantly larger in premenopausal women vs. postmenopausal women, whereas this is not true for ductal tissue. We conclude that ducts do not appreciably change over the menstrual cycle in either pre- or post-menopausal subjects, whereas glands change significantly over the cycle in pre-menopausal women, and 3D UT can differentiate ducts from glands in vivo.

Improved cancer risk stratification of isoechoic thyroid nodules to reduce unnecessary biopsies using quantitative ultrasound

Objective

Gray-scale ultrasound (US) is the standard-of-care for evaluating thyroid nodules (TNs). However, the performance is better for the identification of hypoechoic malignant TNs (such as classic papillary thyroid cancer) than isoechoic malignant TNs. Quantitative ultrasound (QUS) utilizes information from raw ultrasonic radiofrequency (RF) echo signal to assess properties of tissue microarchitecture. The purpose of this study is to determine if QUS can improve the cancer risk stratification of isoechoic TNs.

Methods

Patients scheduled for TN fine needle biopsy (FNB) were recruited from the Thyroid Health Clinic at Boston Medical Center. B-mode US and RF data (to generate QUS parameters) were collected in 274 TNs (163 isoechoic, 111 hypoechoic). A linear combination of QUS parameters (CQP) was trained and tested for isoechoic [CQP(i)] and hypoechoic [CQP(h)] TNs separately and compared with the performance of conventional B-mode US risk stratification systems.

Results

CQP(i) produced an ROC AUC value of 0.937+/- 0.043 compared to a value of 0.717 +/- 0.145 (p >0.05) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.589 +/- 0.173 (p >0.05) for the American Thyroid Association (ATA) risk stratification system. In this study, CQP(i) avoids unnecessary FNBs in 73% of TNs compared to 55.8% and 11.8% when using ACR TI-RADS and ATA classification system.

Conclusion

This data supports that a unique QUS-based classifier may be superior to conventional US stratification systems to evaluate isoechoic TNs for cancer and should be explored further in larger studies.

Quantitative evaluation of rat sciatic nerve degeneration using high-frequency ultrasound

In this study, we evaluated the utility of using high-frequency ultrasound to non-invasively track the degenerative process in a rat model of peripheral nerve injury. Primary analyses explored spatial and temporal changes in quantitative backscatter coefficient (BSC) spectrum-based outcomes and B-mode textural outcomes, using gray level co-occurrence matrices (GLCMs), during the progressive transition from acute to chronic injury. As secondary analyses, correlations among GLCM and BSC spectrum-based parameters were evaluated, and immunohistochemistry were used to suggest a structural basis for ultrasound outcomes. Both mean BSC spectrum-based and mean GLCM-based measures exhibited significant spatial differences across presurgical and 1-month/2-month time points, distal stumps enclosed proximity to the injury site being particularly affected. The two sets of parameters sensitively detected peripheral nerve degeneration at 1-month and 2-month post-injury, with area under the receiver operating charactersitic curve > 0.8 for most parameters. The results also indicated that the many BSC spectrum-based and GLCM-based parameters significantly correlate with each other, and suggested a common structural basis for a diverse set of quantitative ultrasound parameters. The findings of this study suggest that BSC spectrum-based and GLCM-based analysis are promising non-invasive techniques for diagnosing peripheral nerve degeneration.

Cancer characterization using light backscattering spectroscopy and quantitative ultrasound: an ex vivo study on sarcoma subtypes

Histological analysis is the gold standard method for cancer diagnosis. However, it is prone to subjectivity and sampling bias. In response to these limitations, we introduce a quantitative bimodal approach that aims to provide non-invasive guidance towards suspicious regions. Light backscattering spectroscopy and quantitative ultrasound techniques were combined to characterize two different bone tumor types from animal models: chondrosarcomas and osteosarcomas. Two different cell lines were used to induce osteosarcoma growth. Histological analyses were conducted to serve as references. Three ultrasound parameters and intensities of the light reflectance profiles showed significant differences between chondrosarcomas and osteosarcomas at the 5% level. Likewise, variations in the same biomarkers were reported for the two types of osteosarcoma, despite their similar morphology observed in the histological examinations. These observations show the sensitivity of our techniques in probing fine tissue properties. Secondly, the ultrasound spectral-based technique identified the mean size of chondrosarcoma cells and nuclei with relative errors of about 22% and 9% respectively. The optical equivalent technique correctly extracted scatterer size distributions that encompass nuclei and cells for chondrosarcomas and osteosarcomas ([Formula: see text] and [Formula: see text] respectively). The optical scattering contributions of nuclei were estimated at 52% for the chondrosarcomas and 69% for the osteosarcomas, probably indicating the abundant and the absent extracellular matrix respectively. Thus, the ultrasound and the optical methods brought complementary parameters. They successfully estimated morphological parameters at the cellular and the nuclear scales, making our bimodal technique promising for tumor characterization.

A Computer Program for Assessing Histoanatomical Morphometrics in Ultra-High-Frequency Ultrasound Images of the Bowel Wall in Children: Development and Inter-Observer Variability

Ultra-high-frequency ultrasound (UHFUS) has a reported potential to differentiate between aganglionic and ganglionic bowel wall, referred to as histoanatomical differences. A good correlation between histoanatomy and UHFUS of the bowel wall has been proven. In order to perform more precise and objective histoanatomical morphometrics, the main research objective of this study was to develop a computer program for the assessment and automatic calculation of the histoanatomical morphometrics of the bowel wall in UHFUS images. A computer program for UHFUS diagnostics was developed and presented. A user interface was developed in close collaboration between pediatric surgeons and biomedical engineers, to enable interaction with UHFUS images. Images from ex vivo bowel wall samples of 23 children with recto-sigmoid Hirschsprung's disease were inserted. The program calculated both thickness and amplitudes (image whiteness) within different histoanatomical bowel wall layers. Two observers assessed the images using the program and the inter-observer variability was evaluated. There was an excellent agreement between observers, with an intraclass correlation coefficient range of 0.970-0.998. Bland-Altman plots showed flat and narrow distributions. The mean differences ranged from 0.005 to 0.016 mm in thickness and 0 to 0.7 in amplitude units, corresponding to 1.1-3.6% and 0.0-0.8% from the overall mean. The computer program enables and ensures objective, accurate and time-efficient measurements of histoanatomical thicknesses and amplitudes in UHFUS images of the bowel wall. The program can potentially be used for several bowel wall conditions, accelerating research within UHFUS diagnostics.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.

High-frequency ultrasound point-of-care device to quantify myopia-induced microstructural changes in the anterior sclera

PURPOSE

To develop a point-of-care (POC) device using high-frequency ultrasound (US) for evaluating microstructural changes in the anterior sclera associated with myopia.

METHODS

The proposed POC device must satisfy four primary requirements for effective clinical use: the measurement component is handheld; the software must be simple and provide real-time feedback; patient safety and health data security requirements set forth by relevant governing bodies must be satisfied and the measurement data must have sufficient signal-to-noise ratio (SNR) and repeatability. Radiofrequency (RF) echo data acquired by the POC device will be processed using our quantitative US methods to characterise tissue microstructure and biomechanical properties.

RESULTS

All stated requirements have been met in the developed POC device. The high-frequency transducer is housed in a custom, 3D-printed, pen-like holder that allows for easy measurements of the anterior sclera. Custom software provides a simple interface for data acquisition, real-time data display and secure data storage. Exposimetry measurements of the US pressure field indicate device compliance with United States Food and Drug Administration limits for ophthalmic US. In vivo measurements on a volunteer suggest the RF data SNR and acquisition consistency are suitable for quantitative analysis.

CONCLUSIONS

A fully functioning POC device using high-frequency US has been created for evaluating the microstructure of the anterior sclera. Planned studies using the POC device to scan the eyes of myopia patients will help clarify how the anterior sclera microstructure may be affected by myopia. If effective, this portable, inexpensive and user-friendly system could be an important part of routine eye examinations.

Classification of Metastatic Lymph Nodes In Vivo Using Quantitative Ultrasound at Clinical Frequencies

Quantitative ultrasound (QUS) methods characterizing the backscattered echo signal have been of use in assessing tissue microstructure. High-frequency (30 MHz) QUS methods have been successful in detecting metastases in surgically excised lymph nodes (LNs), but limited evidence exists regarding the efficacy of QUS for evaluating LNs in vivo at clinical frequencies (2-10 MHz). In this study, a clinical scanner and 10-MHz linear probe were used to collect radiofrequency (RF) echo data of LNs in vivo from 19 cancer patients. QUS methods were applied to estimate parameters derived from the backscatter coefficient (BSC) and statistics of the envelope-detected RF signal. QUS parameters were used to train classifiers based on linear discriminant analysis (LDA) and support vector machines (SVMs). Two BSC-based parameters, scatterer diameter and acoustic concentration, were the most effective for accurately detecting metastatic LNs, with both LDA and SVMs achieving areas under the receiver operating characteristic (AUROC) curve ≥0.94. A strategy of classifying LNs based on the echo frame with the highest cancer probability improved performance to 88% specificity at 100% sensitivity (AUROC = 0.99). These results provide encouraging evidence that QUS applied at clinical frequencies may be effective at accurately identifying metastatic LNs in vivo, helping in diagnosis while reducing unnecessary biopsies and surgical treatments.

Assessing engineered tissues and biomaterials using ultrasound imaging: In vitro and in vivo applications

Quantitative assessment of the structural, functional, and mechanical properties of engineered tissues and biomaterials is fundamental to their development for regenerative medicine applications. Ultrasound (US) imaging is a non-invasive, non-destructive, and cost-effective technique capable of longitudinal and quantitative monitoring of tissue structure and function across centimeter to sub-micron length scales. Here we present the fundamentals of US to contextualize its application for the assessment of biomaterials and engineered tissues, both in vivo and in vitro. We review key studies that demonstrate the versatility and broad capabilities of US for clinical and pre-clinical biomaterials research. Finally, we highlight emerging techniques that further extend the applications of US, including for ultrafast imaging of biomaterials and engineered tissues in vivo and functional monitoring of stem cells, organoids, and organ-on-a-chip systems in vitro.

Repeatability, Reproducibility and Sources of Variability in the Assessment of Backscatter Coefficient and Texture Parameters from High-Frequency Ultrasound Acquisitions in Human Median Nerve

Ultrasound (US) is an increasingly prevalent and effective diagnostic modality for neuromuscular imaging. Gray-scale B-mode imaging has been the dominant US approach to evaluating nerves qualitatively or making morphometric measurements of nerves, providing important insights into pathological changes for conditions such as carpal tunnel syndrome. Among more recent ultrasound strategies, high-frequency ultrasound (often defined as >15 MHz for clinical applications), quantitative ultrasound and image textural analysis offer promising enhancements for improved and more objective approaches to nerve imaging. In this study, we evaluated the repeatability and reproducibility of backscatter coefficient (BSC) and imaging texture features extracted by gray-level co-occurrence matrices (GLCMs) in homogeneous tissue-mimicking reference phantoms and in median nerves in the wrists of healthy participants. We also investigated several practical sources of variability in the assessment of quantitative parameters, including influences of operators, and participant-to-participant variability. Overall, BSC- and GLCM-based outcomes are highly repeatable and reproducible after operator training, based on measurement of descriptive statistics, repeatability coefficient (RC) and reproducibility coefficient recommended by Quantitative Imaging Biomarker Alliance (QIBA RDC). GLCM parameters appear more reproducible and repeatable than BSC-based parameters in healthy participants in vivo. However, such variability noted here must be compared with the value ranges and variability of the results in pathological nerves, including median nerves afflicted by trauma, overuse syndromes such as carpal tunnel syndrome and after surgical repair.

Review of Envelope Statistics Models for Quantitative Ultrasound Imaging and Tissue Characterization

The homodyned K-distribution and the K-distribution, viewed as a special case, as well as the Rayleigh and the Rice distributions, viewed as limit cases, are discussed in the context of quantitative ultrasound (QUS) imaging. The Nakagami distribution is presented as an approximation of the homodyned K-distribution. The main assumptions made are (1) the absence of log-compression or application of nonlinear filtering on the echo envelope of the radiofrequency signal and (2) the randomness and independence of the diffuse scatterers. We explain why other available models are less amenable to a physical interpretation of their parameters. We also present the main methods for the estimation of the statistical parameters of these distributions. We explain why we advocate the methods based on the X-statistics for the Rice and the Nakagami distributions and the K-distribution. The limitations of the proposed models are presented. Several new results are included in the discussion sections, with proofs in the appendix.

Quantitative Ultrasound for Evaluation of Tumour Response to Ultrasound-Microbubbles and Hyperthermia

Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology.

Extracting Quantitative Ultrasonic Parameters from the Backscatter Coefficient

The ultrasonic backscatter coefficient (BSC) is a fundamental quantitative ultrasound (QUS) parameter that contains rich information about the underlying tissue. Deriving parameters from the BSC is essential for fully utilizing the information contained in BSC for tissue characterization. In this chapter, we review two primary approaches for extracting parameters from the BSC versus frequency curve: the model-based approach and the model-free approach, focusing on the model-based approach, where a scattering model is fit to the observed BSC to yield model parameters. For this approach, we will attempt to unite commonly used models under a coherent theoretical framework. We will focus on the underlying assumptions and conditions for various BSC models. Computer code is provided to facilitate the use of some of the models. The strengths and weaknesses of various models are also discussed.

Implementation of Non-Invasive Quantitative Ultrasound in Clinical Cancer Imaging

Quantitative ultrasound (QUS) is a non-invasive novel technique that allows treatment response monitoring. Studies have shown that QUS backscatter variables strongly correlate with changes observed microscopically. Increases in cell death result in significant alterations in ultrasound backscatter parameters. In particular, the parameters related to scatterer size and scatterer concentration tend to increase in relation to cell death. The use of QUS in monitoring tumor response has been discussed in several preclinical and clinical studies. Most of the preclinical studies have utilized QUS for evaluating cell death response by differentiating between viable cells and dead cells. In addition, clinical studies have incorporated QUS mostly for tissue characterization, including classifying benign versus malignant breast lesions, as well as responder versus non-responder patients. In this review, we highlight some of the important findings of previous preclinical and clinical studies and expand the applicability and therapeutic benefits of QUS in clinical settings. We summarized some recent clinical research advances in ultrasound-based radiomics analysis for monitoring and predicting treatment response and characterizing benign and malignant breast lesions. We also discuss current challenges, limitations, and future prospects of QUS-radiomics.

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator

The homodyned-K distribution is an important ultrasound backscatter envelope statistics model of physical meaning, and the parametric imaging of the model parameters has been explored for quantitative ultrasound tissue characterization. In this paper, we proposed a new method for liver fibrosis characterization by using radiomics of ultrasound backscatter homodyned-K imaging based on an improved artificial neural network (iANN) estimator. The iANN estimator was used to estimate the ultrasound homodyned-K distribution parameters k and α from the backscattered radiofrequency (RF) signals of clinical liver fibrosis (n = 237), collected with a 3-MHz convex array transducer. The RF data were divided into two groups: Group I corresponded to liver fibrosis with no hepatic steatosis (n = 94), and Group II corresponded to liver fibrosis with mild to severe hepatic steatosis (n = 143). The estimated homodyned-K parameter values were then used to construct k and α parametric images using the sliding window technique. Radiomics features of k and α parametric images were extracted, and feature selection was conducted. Logistic regression classification models based on the selected radiomics features were built for staging liver fibrosis. Experimental results showed that the proposed method is overall superior to the radiomics method of uncompressed envelope images when assessing liver fibrosis. Regardless of hepatic steatosis, the proposed method achieved the best performance in staging liver fibrosis ≥F1, ≥F4, and the area under the receiver operating characteristic curve was 0.88, 0.85 (Group I), and 0.85, 0.86 (Group II), respectively. Radiomics has improved the ability of ultrasound backscatter statistical parametric imaging to assess liver fibrosis, and is expected to become a new quantitative ultrasound method for liver fibrosis characterization.

Assessment of the frequency dependence of acoustic properties on material, composition, and scatterer size of the medium

PURPOSE

The aim of this study was to elucidate the frequency dependence of the speed of sound (SoS) and attenuation coefficients in phantoms with controlled attenuation properties (scatterer density, scatterer size, absorption control material) and rat livers.

METHODS

The frequency dependence of SoS and attenuation coefficients were evaluated with ultrasound (1-15 MHz) by observing multiple phantoms with different scatterer sizes, densities, and presence or absence of evaporated milk as absorbing media. Normal and fatty model rat livers were examined with the same protocol.

RESULTS

The phantom results revealed that the scatterer density and SoS of the base media were the dominant factors causing the changes in SoS. Frequency dependence was not observed in SoS. Assessment of the attenuation coefficient showed that the frequency dependence was mainly affected by absorption attenuation when the scatterer was as small as a hepatocyte (i.e. ≤ 10 µm). Scattering attenuation was also observed to affect frequency dependence when the scatterer was as large as lipid droplets (i.e. ≤ 40 µm).

CONCLUSION

Assuming a consistent size of the main scatterers in the evaluation medium, the frequency dependence of the SoS and attenuation coefficients may provide insight into the scatterer density and the contribution of absorption and scattering attenuation. Further studies in the higher frequency band (up to about 50 MHz) are expected to advance the clinical application of high-frequency ultrasound.

Quantitative Ultrasound Assessment of Early Osteoarthritis in Human Articular Cartilage Using a High-Frequency Linear Array Transducer

Quantitative ultrasound (QUS) assessment of osteoarthritis (OA) using high-frequency, research-grade single-element ultrasound systems has been reported. The objective of this ex vivo study was to assess the performance of QUS in detecting early OA using a high-frequency linear array transducer. Osteochondral plugs (n = 26) of human articular cartilage were scanned with ExactVu Micro-Ultrasound using an EV29L side-fire transducer. For comparison, the samples were also imaged with SAM200Ex, a custom 40-MHz scanning acoustic microscope with a single-element, focused transducer. Thirteen QUS parameters were derived from the ultrasound data. Magnetic resonance imaging (MRI) data, with T1 and T2 extracted as the quantitative parameters, were also acquired for comparison. Cartilage degeneration was graded from histology and correlated to all quantitative parameters. A maximum Spearman rank correlation coefficient (ρ) of 0.75 was achieved using a combination of ExactVu QUS parameters, while a maximum ρ of 0.62 was achieved using a combination of parameters from SAM200Ex. A maximum ρ of 0.75 was achieved using the T1 and T2 MRI parameters. This study illustrates the potential of a high-frequency linear array transducer to provide a convenient method for early OA screening with results comparable to those of research-grade single-element ultrasound and MRI.

In vivo assessment of pulmonary fibrosis and edema in rodents using the backscatter coefficient and envelope statistics

Quantitative ultrasound methods based on the backscatter coefficient (BSC) and envelope statistics have been used to quantify disease in a wide variety of tissues, such as prostate, lymph nodes, breast, and thyroid. However, to date, these methods have not been investigated in the lung. In this study, lung properties were quantified by BSC and envelope statistical parameters in normal, fibrotic, and edematous rat lungs in vivo. The average and standard deviation of each parameter were calculated for each lung as well as the evolution of each parameter with acoustic propagation time within the lung. The transport mean free path and backscattered frequency shift, two parameters that have been successfully used to assess pulmonary fibrosis and edema in prior work, were evaluated in combination with the BSC and envelope statistical parameters. Multiple BSC and envelope statistical parameters were found to provide contrast between control and diseased lungs. BSC and envelope statistical parameters were also significantly correlated with fibrosis severity using the modified Ashcroft fibrosis score as the histological gold standard. These results demonstrate the potential for BSC and envelope statistical parameters to improve the diagnosis of pulmonary fibrosis and edema as well as monitor pulmonary fibrosis.

Total attenuation compensation for backscatter coefficient estimation using full angular spatial compounding

The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC is only possible with the knowledge of the attenuation coefficient slope (ACS) of the tissues under examination. In this study, the use of attenuation maps constructed using full angular spatial compounding (FASC) is proposed for attenuation compensation when imaging integrated BSCs. Experimental validation of the proposed approach was obtained using two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background, and in a phantom containing an ex vivo chicken breast sample embedded in an agar matrix. With the phantom data, three different ACS maps were employed for attenuation compensation: (1) a ground truth ACS map constructed using insertion loss techniques, (2) the estimated ACS map using FASC attenuation imaging, and (3) a uniform ACS map with a value of 0.5 dBcm^{\protect \relax \special {t4ht=-}1}MHz^{\protect \relax \special {t4ht=-}1}, which is commonly used to represent attenuation in soft tissues. Comparable results were obtained when using the ground truth and FASC-estimated ACS maps in term of inclusion detectability and estimation accuracy, with averaged fractional error below 2.8 dB in both phantoms. Conversely, the use of the homogeneous ACS map resulted in higher levels of fractional error (>10 dB), which demonstrates the importance of an accurate attenuation compensation. The results with the ex vivo tissue sample were consistent with the observations using the physical phantoms, with the FASC-derived ACS map providing comparable BSC images to those formed using the ground truth ACS map and more accurate than those BSC images formed using a uniform ACS. These results suggest that BSCs can be reliably estimated using FASC when a self-consistent attenuation compensation stemming from prior estimation of an accurate ACS map is used.

A study on understanding the physical mechanism of change in ultrasonic envelope statistical property during temperature elevation

PURPOSE

Our previous studies demonstrate that the variation in ultrasonic envelope statistics is correlated with the temperature change inside scattering media. This variation is identified as the change in the scatterer structure during thermal expansion or contraction. However, no specific evidence has been verified to date. This study numerically reproduces the change in the scatterer distribution during thermal expansion or contraction using finite element simulations and also investigates how the situation is altered by different material properties.

METHODS

The material properties of a linear elastic solid depend on the thermal expansion coefficient, thermal conductivity, specific heat, and initial scatterer number density. Three-dimensional displacements, calculated in the simulation, were sequentially used to update the positions of the randomly distributed scatterers. Ultrasound signals from the scatterer distribution were generated by simulating a 7.5-MHz linear array transducer whose specifications were the same as those in the experimental measurements of several phantoms and excised porcine livers. To represent the change in the envelope statistical feature, the absolute value of the ratio change in the logarithmic Nakagami (NA) parameter, Δ m , at each time was calculated as a value normalized with the initial NA parameter.

RESULTS

The change in the scatterer number density relates to the volume change during temperature elevation. The magnitude of the Δ m shift against the temperature change increases depending on the higher thermal expansion coefficient. In contrast, the relationship between Δ m and the scatterer number density is similar with any material property. Additionally, the changes in Δ m obtained by several experimental phantoms with low to high scatterer number densities are comparable with the numerical simulation results.

CONCLUSIONS

The change in Δ m is indirectly related to the change in the scatterer number density owing to the volume change during thermal expansion or contraction.

Fatty liver evaluation with double-Nakagami model under low-resolution conditions

In previous studies, the double-Nakagami (DN) model has been proposed for fatty liver assessment and applied to in vivo rat livers and clinical data sets. The healthy liver structure filter (HLSF) method, which extracts non-healthy areas using two DN parameters, has also been proposed. In this paper, we first verify the accuracy of the DN model and the HLSF method for acoustic fields at 15 and 5 MHz, which were reproduced using numerical simulation. We then apply the method to clinical data sets of livers observed using a frequency of 3 MHz and investigate the method’s clinical usefulness. A positive correlation ( r = 0.28 ) was found between the ratio of the non-healthy area and fat mass. Although the results were inferior to the results produced using 15 MHz ultrasound ( r = 0.96 ), we found that it was possible to detect the difference between a normal liver and a fatty liver even at a lower frequency.

Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization

The homodyned K (HK) distribution allows a general description of ultrasound backscatter envelope statistics with specific physical meanings. In this study, we proposed a new artificial neural network (ANN) based parameter estimation method of the HK distribution. The proposed ANN estimator took advantages of ANNs in learning and function approximation and inherited the strengths of conventional estimators through extracting five feature parameters from backscatter envelope signals as the input of the ANN: the signal-to-noise ratio (SNR), skewness, kurtosis, as well as X- and U-statistics. Computer simulations and clinical data of hepatic steatosis were used for validations of the proposed ANN estimator. The ANN estimator was compared with the RSK (the level-curve method that uses SNR, skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on X- and U-statistics) estimators. Computer simulation results showed that the relative bias was best for the XU estimator, whilst the normalized standard deviation was overall best for the ANN estimator. The ANN estimator was almost one order of magnitude faster than the RSK and XU estimators. The ANN estimator also yielded comparable diagnostic performance to state-of-the-art HK estimators in the assessment of hepatic steatosis. The proposed ANN estimator has great potential in ultrasound tissue characterization based on the HK distribution.

Quantitative characterization of the colorectal cancer in a rabbit model using high-frequency endoscopic ultrasound

PURPOSE

Colonoscopy accompanied with biopsy works as the routine endoscopic strategy for the diagnosis of colorectal cancer (CRC) in clinic; however, the colonoscopy is limited to the tissue surface. During the last decades, enabling technologies are emerging to complement with the colonoscopy for better administration of CRC. The conventional low-frequency (<12 MHz) endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA) has been widely used to assess the lesion penetration. With the high-frequency ultrasound transducer (>20 MHz), EUS allows more precise visualization of the colorectal abnormalities. In order to achieve the accurate detection or in situ characterization of the colorectal lesions, the EUS diagnosis needs more patho-physiological related information in the micro-structural or molecular level. Quantitative ultrasound (QUS) technique, which could extract the micro-structural information from the ultrasound radio-frequency (RF) signal, is promising for the non-invasive tissue characterization. To date, the knowledge of the high-frequency endoscopic QUS for the CRC characterization has not been fully determined.

METHODS

In this work, to our best knowledge, it is the first application of the QUS technique based on a customized high-frequency EUS system (30.5 MHz center frequency) to characterize the colorectal malignancies in a VX2 rabbit CRC model. To eliminate the response from the ultrasound electronic system and transducer, the ultrasound signals from colon tissue were calibrated. And, the resulting quasi-liner ultrasound spectra were fit by the linear regression test. As a result, three spectral parameters, including the slope (k), intercept (I) and Midband Fit (M), were obtained from the best-fit line. The three spectral parameters were compared between the malignant tissue regions and adjacent normal tissue regions of the colon tissue specimen ex vivo. The independent t-test was conducted between the three parameters from the normal and malignant group. The statistical method of Fisher Linear Discriminant (FLD) was used to explore the linear combinations of the three parameters, so as to provide more tissue micro-structural features than the single parameter alone. The three FLD values were derived from three different combinations among k, I and M. The threshold was selected from the statistical analysis to optimize the differentiation criteria between the malignant and the normal tissues. The color-coded images were used to display the local FLD values and combined with the EUS B-mode image.

RESULTS AND CONCLUSIONS

The mean Midband Fit (M) and intercept (I) showed significant differences between the malignant and normal tissue regions. The statistical analysis showed that there were significant differences in all the mean FLD values of the spectral parameter combinations (kI, kM and IM) (t test, P < 0.05). And, the combined image result from the B-mode image and color-coded image could visually correlate with the histology result. In conclusion, the high-frequency endoscopic QUS technique was potential to be used as a complementary method to distinguish the colorectal malignancies by leveraging its morphological and micro-structural ultrasound information.

A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods

PURPOSE

We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.

MATERIALS AND METHODS

QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

RESULTS

A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.

CONCLUSIONS

A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.

A Preliminary Study of Quantitative Ultrasound for Cancer-Risk Assessment of Thyroid Nodules

BACKGROUND

Gray-scale, B-mode ultrasound (US) imaging is part of the standard clinical procedure for evaluating thyroid nodules (TNs). It is limited by its instrument- and operator-dependence and inter-observer variability. In addition, the accepted high-risk B-mode US TN features are more specific for detecting classic papillary thyroid cancer rather than the follicular variant of papillary thyroid cancer or follicular thyroid cancer. Quantitative ultrasound (QUS) is a technique that can non-invasively assess properties of tissue microarchitecture by exploiting information contained in raw ultrasonic radiofrequency (RF) echo signals that is discarded in conventional B-mode imaging. QUS provides quantitative parameter-value estimates that are a function of the properties of US scatterers and microarchitecture of the tissue. The purpose of this preliminary study was to assess the performance of QUS parameters in evaluating benign and malignant thyroid nodules.

METHODS

Patients from the Thyroid Health Center at the Boston Medical Center were recruited to participate. B-mode and RF data were acquired and analyzed in 225 TNs (24 malignant and 201 benign) from 208 patients. These data were acquired either before (167 nodules) or after (58 nodules) subjects underwent fine-needle biopsy (FNB). The performance of a combination of QUS parameters (CQP) was assessed and compared with the performance of B-mode risk-stratification systems.

RESULTS

CQP produced an ROC AUC value of 0.857 ± 0.033 compared to a value of 0.887 ± 0.033 (p=0.327) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.880 ± 0.041 (p=0.367) for the American Thyroid Association (ATA) risk-stratification system. Furthermore, using a CQP threshold of 0.263 would further reduce the number of unnecessary FNBs in 44% of TNs without missing any malignant TNs. When CQP used in combination with ACR TI-RADS, a potential additional reduction of 49 to 66% in unnecessary FNBs was demonstrated.

CONCLUSION

This preliminary study suggests that QUS may provide a method to classify TNs when used by itself or when combined with a conventional gray-scale US risk-stratification system and can potentially reduce the need to biopsy TNs.

Basic concept and clinical applications of quantitative ultrasound (QUS) technologies

In the field of clinical ultrasound, the full digitalization of diagnostic equipment in the 2000s enabled the technological development of quantitative ultrasound (QUS), followed by multiple diagnostic technologies that have been put into practical use in recent years. In QUS, tissue characteristics are quantified and parameters are calculated by analyzing the radiofrequency (RF) echo signals returning to the transducer. However, the physical properties (and pathological level structure) of the biological tissues responsible for the imaging features and QUS parameters have not been sufficiently verified as there are various conditions for observing living tissue with ultrasound and inevitable discrepancies between theoretical and actual measurements. A major issue of QUS in clinical application is that the evaluation results depend on the acquisition conditions of the RF echo signal as the source of the image information, and also vary according to the model of the diagnostic device. In this paper, typical examples of QUS techniques for evaluating attenuation, speed of sound, amplitude envelope characteristics, and backscatter coefficient in living tissues are introduced. Exemplary basic research and clinical applications related to these technologies, and initiatives currently being undertaken to establish the QUS method as a true tissue characterization technology, are also discussed.

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions

PURPOSE

Accurate and timely diagnosis of breast carcinoma is very crucial because of its high incidence and high morbidity. Screening can improve overall prognosis by detecting the disease early. Biopsy remains as the gold standard for pathological confirmation of malignancy and tumour grading. The development of diagnostic imaging techniques as an alternative for the rapid and accurate characterization of breast masses is necessitated. Quantitative ultrasound (QUS) spectroscopy is a modality well suited for this purpose. This study was carried out to evaluate different texture analysis methods applied on QUS spectral parametric images for the characterization of breast lesions.

METHODS

Parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined using QUS spectroscopy from 193 patients with breast lesions. Texture methods were used to quantify heterogeneities of the parametric images. Three statistical-based approaches for texture analysis that include Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GRLM), and Gray Level Size Zone Matrix (GLSZM) methods were evaluated. QUS and texture-parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis in order to classify breast lesions as either benign or malignant. We developed a diagnostic model using different classification algorithms including linear discriminant analysis (LDA), k-nearest neighbours (KNN), support vector machine with radial basis function kernel (SVM-RBF), and an artificial neural network (ANN). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation.

RESULTS

Classifier performances ranged from 73% to 91% in terms of accuracy dependent on tumour margin inclusion and classifier methodology. Utilizing information from tumour core alone, the ANN achieved the best classification performance of 93% sensitivity, 88% specificity, 91% accuracy, 0.95 AUC using QUS parameters and their GLSZM texture features.

CONCLUSIONS

A QUS-based framework and texture analysis methods enabled classification of breast lesions with >90% accuracy. The results suggest that optimizing method for extracting discriminative textural features from QUS spectral parametric images can improve classification performance. Evaluation of the proposed technique on a larger cohort of patients with proper validation technique demonstrated the robustness and generalization of the approach.

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

PURPOSE

Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.

METHODS

QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

RESULTS

Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.

CONCLUSIONS

QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

Quantifying scattering from dense media using two-dimensional impedance maps

A better understanding of ultrasound scattering in a three-dimensional (3D) medium can provide more accurate methods for ultrasound tissue characterization. The possibility of using two-dimensional impedance maps (2DZMs) based on correlation coefficients has shown promise in the case of isotropic and sparse medium [Luchies and Oelze, J. Acoust. Soc. Am. 139, 1557-1564 (2016)]. The present study investigates the use of 2DZMs in order to quantify 3D scatterer properties of dense media from two-dimensional (2D) histological slices. Two 2DZM approaches were studied: one based on the correlation coefficient and the other based on the 2D Fourier transform of 2DZMs. Both 2DZM approaches consist in estimating the backscatter coefficient (BSC) from several 2DZMs, and then the resulting BSC was fit to the theoretical polydisperse structure factor model to yield 3D scatterer properties. Simulation studies were performed to evaluate the ability of both 2DZM approaches to quantify scattering of a 3D medium containing randomly distributed polydisperse spheres or monodisperse ellipsoids. Experimental studies were also performed using the histology photomicrographs obtained from HT29 cell pellet phantoms. Results demonstrate that the 2DZM Fourier transform-based approach was more suitable than the correlation coefficient-based approach for estimating scatterer properties when using a small number of 2DZMs.

High-Frequency Ultrasonic Forceps for the In Vivo Detection of Cancer During Breast-Conserving Surgery

A major aim in the surgical management of soft tissue cancers is to detect and remove all cancerous tissues while ensuring noncancerous tissue remains intact. Breast-conserving surgery provides a prime illustration of this aim, since remaining cancer in breast margins results in multiple surgeries, while removal of too much unaffected tissue often has undesirable cosmetic effects. Similarly, resection of benign lymph nodes during sentinel lymph node biopsy can cause deleterious health outcomes. The objective of this study was to create an intraoperative, in vivo device to address these challenges. Instant diagnostic information generated by this device could allow surgeons to precisely and completely remove all malignant tissue during the first surgery. Surgical forceps based on Martin forceps were instrumented at the tips with high-frequency ultrasonic transducers composed of polyvinylidene difluoride, a thickness-sensing rotary potentiometer at the base, and a spring to provide the appropriate restoring force. Transducer wires within the forceps were connected to an external high-frequency pulser-receiver, activating the forceps' transmitting transducer at 50 MHz and amplifying through-transmission signals from the receiving transducer. The forceps were tested with tissue-mimicking agarose phantoms embedded with 58–550 μm polyethylene microspheres to simulate various stages of cancer progression and to provide a range of measurement values. Results were compared with measurements from standard 50 MHz immersion transducers. The results showed that the forceps displayed similar sensitivity for attenuation and increased accuracy for wave speed. The forceps could also be extended to endoscopes and laparoscopes.

Quantitative assessment of media concentration using the Homodyned K distribution

The Homodyned K distribution has been used successfully as a tool in the ultrasound characterization of sparse media, where the scatterer clustering parameter α accurately discriminates between media with different numbers of scatterers per resolution cell. However, as the number of scatterers increases and the corresponding amplitude statistics become Rician, the reliability of the α estimates decreases rapidly. In the present study, we assess the usefulness of α for the characterization of both sparse and concentrated media, using simulated independent and identically distributed (i.i.d.) samples from Homodyned K distributions, ultrasound images of media with up to 68 scatterers per resolution cell and ultrasound signals acquired from particle phantoms with up to 101 scatterers per resolution cell. All parameter estimates are obtained using the XU estimator (Destrempes et al., 2013). Results suggest that the parameter α can be used to distinguish between media with up to 40 scatterers per resolution cell at 22 MHz, provided that parameter estimation can be performed on very large sample sizes (i.e., >10,000 i.i.d. samples).

Estimation of Backscatter Coefficients Using an In Situ Calibration Source

The objective of this article is to demonstrate the feasibility of estimating the backscatter coefficient (BSC) using an in situ calibration source. Traditional methods of estimating the BSC in vivo using a reference phantom technique do not account for transmission losses due to intervening layers between the ultrasonic source and the tissue region to be interrogated, leading to increases in bias and variance of BSC-based estimates. To account for transmission losses, an in situ calibration approach is proposed. The in situ calibration technique employs a titanium sphere that is well-characterized ultrasonically, biocompatible, and embedded inside the sample. A set of experiments was conducted to evaluate the embedded titanium spheres as in situ calibration targets for BSC estimation. The first experiment quantified the backscattered signal strength from titanium spheres of three sizes: 0.5, 1, and 2 mm in diameter. The second set of experiments assessed the repeatability of BSC estimates from the titanium spheres and compared these BSCs to theory. The third set of experiments quantified the ability of the titanium bead to provide an in situ reference spectrum in the presence of a lossy layer on top of the sample. The final set of experiments quantified the ability of the bead to provide a calibration spectrum over multiple depths in the sample. All experiments were conducted using an L9-4/38 linear array connected to a SonixOne system. The strongest signal was observed from the 2-mm titanium bead with the signal-to-noise ratio (SNR) of 11.6 dB with respect to the background speckle. Using an analysis bandwidth of 2.5-5.5 MHz, the mean differences between the experimentally derived BSCs and BSCs derived from the Faran theory were 0.54 and 0.76 dB using the array and a single-element transducer, respectively. The BSCs estimated using the in situ calibration approach without the layer and with the layer and using the reference phantom approach with the layer were compared to the reference phantom approach without the layer present. The mean differences in BSCs were 0.15, 0.73, and -9.69 dB, respectively. The mean differences of the BSCs calculated from data blocks located at depths that were either 30 pulse lengths above or below the actual bead depth compared to the BSC calculated at bead depth were -1.55 and -1.48 dB, respectively. The results indicate that an in situ calibration target can account for overlaying tissue losses, thereby improving the robustness of BSC-based estimates.

Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: An animal study

Ultrasound is the first-line tool for screening hepatic steatosis. Statistical distributions can be used to model the backscattered signals for liver characterization. The Nakagami distribution is the most frequently adopted model; however, the homodyned K (HK) distribution has received attention due to its link to physical meaning and improved parameter estimation through X- and U-statistics (termed "XU"). To assess hepatic steatosis, we proposed HK parametric imaging based on the α parameter (a measure of the number of scatterers per resolution cell) calculated using the XU estimator. Using a commercial system equipped with a 7-MHz linear array transducer, phantom experiments were performed to suggest an appropriate window size for α imaging using the sliding window technique, which was further applied to measuring the livers of rats (n = 66) with hepatic steatosis induced by feeding the rats a methionine- and choline-deficient diet. The relationships between the α parameter, the stage of hepatic steatosis, and histological features were verified by the correlation coefficient r, one-way analysis of variance, and regression analysis. The phantom results showed that the window side length corresponding to five times the pulse length supported a reliable α imaging. The α parameter showed a promising performance for grading hepatic steatosis (p < 0.05; r² = 0.68). Compared with conventional Nakagami imaging, α parametric imaging provided significant information associated with fat droplet size (p < 0.05; r² = 0.53), enabling further analysis and evaluation of severe hepatic steatosis.

Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators

BACKGROUND

The homodyned-K (HK) distribution is an important statistical model for describing ultrasound backscatter envelope statistics. HK parametric imaging has shown potential for characterizing hepatic steatosis. However, the feasibility of HK parametric imaging in assessing human hepatic steatosis in vivo remains unclear.

METHODS

In this paper, ultrasound HK μ parametric imaging was proposed for assessing human hepatic steatosis in vivo. Two recent estimators for the HK model, RSK (the level-curve method that uses the signal-to-noise ratio (SNR), skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on the first moment of the intensity and two log-moments, namely X- and U-statistics), were investigated. Liver donors (n=72) and patients (n=204) were recruited to evaluate hepatic fat fractions (HFFs) using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate, and severe) using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct μ _RSK and μ _XU images to correlate with HFF analyses and fatty liver stages. The μ _RSK and μ _XU parametric images were constructed using the sliding window technique with the window side length (WSL) =1-9 pulse lengths (PLs). The diagnostic values of the μ _RSK and μ _XU parametric imaging methods were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

For the 72 participants in Group A, the μ _RSK parametric imaging with WSL =2-9 PLs exhibited similar correlation with log₁₀(HFF), and the μ _RSK parametric imaging with WSL = 3 PLs had the highest correlation with log₁₀(HFF) (r=0.592); the μ _XU parametric imaging with WSL =1-9 PLs exhibited similar correlation with log₁₀(HFF), and the μ _XU parametric imaging with WSL =1 PL had the highest correlation with log₁₀(HFF) (r=0.628). For the 204 patients in Group B, the areas under the ROC (AUROCs) obtained using μ _RSK for fatty stages ≥ mild (AUROC₁), ≥ moderate (AUROC₂), and ≥ severe (AUROC₃) were (AUROC₁, AUROC₂, AUROC₃) = (0.56, 0.57, 0.53), (0.68, 0.72, 0.75), (0.73, 0.78, 0.80), (0.74, 0.77, 0.79), (0.74, 0.78, 0.79), (0.75, 0.80, 0.82), (0.74, 0.77, 0.83), (0.74, 0.78, 0.84) and (0.73, 0.76, 0.83) for WSL =1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively. The AUROCs obtained using μ _XU for fatty stages ≥ mild, ≥ moderate, and ≥ severe were (AUROC₁, AUROC₂, AUROC₃) = (0.75, 0.83, 0.81), (0.74, 0.80, 0.80), (0.76, 0.82, 0.82), (0.74, 0.80, 0.84), (0.76, 0.80, 0.83), (0.75, 0.80, 0.84), (0.75, 0.79, 0.85), (0.75, 0.80, 0.85) and (0.73, 0.77, 0.83) for WSL = 1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively.

CONCLUSIONS

Both the μ _RSK and μ _XU parametric images are feasible for evaluating human hepatic steatosis. The WSL exhibits little impact on the diagnosing performance of the μ _RSK and μ _XU parametric imaging. The μ _XU parametric imaging provided improved performance compared to the μ _RSK parametric imaging in characterizing human hepatic steatosis in vivo.

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

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

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

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

Insights into photoacoustic speckle and applications in tumor characterization

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

Transbronchial evaluation of peripheral pulmonary lesions using ultrasonic spectrum analysis in lung cancer patients

BACKGROUND AND OBJECTIVE

Analysis of the endobronchial ultrasound (EBUS) radiofrequency spectrum has been used for convex-probe EBUS technology. Quantitative imaging analysis is also warranted for guided bronchoscopy using radial-probe EBUS (RP-EBUS) targeting peripheral pulmonary lesions (PPL). This study aimed to determine the feasibility of radiofrequency spectrum analysis for distinguishing malignant and benign PPL during diagnostic bronchoscopy.

METHODS

Raw RP-EBUS images with radiofrequency data, including backscatter signals, were prospectively recorded. The ultrasonic spectral parameters, such as intercept, midband-fit and slope within the region of interest, were retrospectively computed by linear regression analysis and compared with the final diagnosis.

RESULTS

A total of 71 PPL, including 45 malignant and 26 benign lesions, were analysed. Malignant PPL showed a significantly lower intercept (P < 0.0001), lower midband-fit (P < 0.0001) and higher slope (P = 0.014) than benign PPL. Analyses of the area under the curve of receiver operating characteristic plots demonstrated that the intercept showed the best diagnostic performance among three parameters (0.87, 0.77 and 0.69 for intercept, midband-fit and slope, respectively). The sensitivity, specificity, accuracy, positive likelihood and negative likelihood were 75.6%, 96.2%, 83.1%, 19.6 and 0.25 for the intercept; 88.9%, 57.7%, 77.5%, 2.1 and 0.19 for the midband-fit; and 68.9%, 73.1%, 70.4%, 2.6 and 0.43 for the slope.

CONCLUSION

Spectrum analysis of EBUS radiofrequency can be used as a novel non-invasive predictor of malignant or benign PPL. Analysis of the 'intercept' of the targeted lesion may provide useful supporting data for real-time sampling from PPL during diagnostic bronchoscopy.

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.

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.

Autoregressive Signal Processing Applied to High-Frequency Acoustic Microscopy of Soft Tissues

Quantitative acoustic microscopy (QAM) at frequencies exceeding 100 MHz has become an established imaging tool to depict acoustical and mechanical properties of soft biological tissues at microscopic resolutions. In this study, we investigate a novel autoregressive (AR) model to improve signal processing and parameter estimation and to test its applicability to QAM. The performance of the AR model for estimating acoustical parameters of soft tissues (i.e., acoustic impedance, speed of sound, and attenuation) was compared to the performance of the Hozumi model using simulated ultrasonic QAM signals and using experimentally measured signals from thin (i.e., 12 and ) sections of human lymph-node and pig-cornea tissue specimens. Results showed that the AR and Hozumi methods performed equally well (i.e., produced an estimation error of 0) in signals with low, linear attenuation in the tissue and high impedance contrast between the tissue and the coupling medium. However, the AR model outperformed the Hozumi model in estimation accuracy and stability (i.e., parameter error variation and number of outliers) in cases of 1) thin tissue-sample thickness and high tissue-sample speed of sound, 2) small impedance contrast between the tissue sample and the coupling medium, 3) high attenuation in the tissue sample, and 4) nonlinear attenuation in the tissue sample. Furthermore, the AR model allows estimating the exponent of nonlinear attenuation. The results of this study suggest that the AR model approach can improve current QAM by providing more reliable, quantitative, tissue-property estimates and also provides additional values of parameters related to nonlinear attenuation.

Ultrasound parametric imaging of hepatic steatosis using the homodyned-K distribution: An animal study

Hepatic steatosis is an abnormal state where excess lipid mass is accumulated in hepatocyte vesicles. Backscattered ultrasound signals received from the liver contain useful information regarding the degree of steatosis in the liver. The homodyned-K (HK) distribution has been demonstrated as a general model for ultrasound backscattering. The estimator based on the first three integer moments (denoted as "FTM") of the intensity has potential for practical applications because of its simplicity and low computational complexity. This study explored the diagnostic performance of HK parametric imaging based on the FTM method in the assessment of hepatic steatosis. Phantom experiments were initially conducted using the sliding window technique to determine an appropriate window size length (WSL) for HK parametric imaging. Subsequently, hepatic steatosis was induced in male Wistar rats fed a methionine- and choline-deficient (MCD) diet for 0 (i.e., normal control), 1, 2, 4, 6, and 8 weeks (n = 36; six rats in each group). After completing the scheduled MCD diet, ultrasound B-mode and HK imaging of the rat livers were performed in vivo and histopathological examinations were conducted to score the degree of hepatic steatosis. HK parameters μ (related to scatterer number density) and k (related to scatterer periodicity) were expressed as functions of the steatosis stage in terms of the median and interquartile range (IQR). Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance levels of the μ and k parameters. The results showed that an appropriate WSL for HK parametric imaging is seven times the pulse length of the transducer. The median value of the μ parameter increased monotonically from 0.194 (IQR: 0.18-0.23) to 0.893 (IQR: 0.64-1.04) as the steatosis stage increased. Concurrently, the median value of the k parameter increased from 0.279 (IQR: 0.26-0.31) to 0.5 (IQR: 0.41-0.54) in the early stages (normal to mild) and decreased to 0.39 (IQR: 0.29-0.45) in the advanced stages (moderate to severe). The areas under the ROC curves obtained using (μ, k) were (0.947, 0.804), (0.914, 0.575), and (0.813, 0.604) for the steatosis stages of ≥mild, ≥moderate, and ≥severe, respectively. The current findings suggest that ultrasound HK parametric imaging based on FTM estimation has great potential for future clinical diagnoses of hepatic steatosis.

High-Frequency Quantitative Ultrasound for Imaging Prostate Cancer Using a Novel Micro-Ultrasound Scanner

Currently, biopsies guided by transrectal ultrasound (TRUS) are the only method for definitive diagnosis of prostate cancer. Studies by our group suggest that quantitative ultrasound (QUS) could provide a more sensitive means of targeting biopsies and directing focal treatments to cancer-suspicious regions in the prostate. Previous studies have utilized ultrasound signals at typical clinical frequencies, i.e., in the 6-MHz range. In the present study, a 29-MHz, TRUS, micro-ultrasound system and transducer (ExactVu micro-ultrasound, Exact Imaging, Markham, Canada) was used to acquire radio frequency data from 163 patients immediately before 12-core biopsy procedures, comprising 1956 cores. These retrospective data are a subset of data acquired in an ongoing, multisite, 2000-patient, randomized, clinical trial (clinicaltrials.gov NCT02079025). Spectrum-based QUS estimates of effective scatter diameter (ESD), effective acoustic concentration (EAC), midband (M), intercept (I) and slope (S) as well as envelope statistics employing a Nakagami distribution were used to train linear discriminant classifiers (LDCs) and support vector machines (SVMs). Classifier performance was assessed using area-under-the-curve (AUC) values obtained from receiver operating characteristic (ROC) analyses with 10-fold cross validation. A combination of ESD and EAC parameters resulted in an AUC value of 0.77 using a LDC. When Nakagami-µ or prostate-specific antigen (PSA) values were added as features, the AUC value increased to 0.79. SVM produced an AUC value of 0.77, using a combination of envelope and spectral QUS estimates. The best classification produced an AUC value of 0.81 using an LDC when combining envelope statistics, PSA, ESD and EAC. In a previous study, B-mode-based scoring and evaluation using the PRI-MUS protocol produced a maximal AUC value of 0.74 for higher Gleason-score values (GS >7) when read by an expert. Our initial results with AUC values of 0.81 are very encouraging for developing a new, predominantly user-independent, prostate-cancer, risk-assessing tool.

A Method for Stereological Determination of the Structure Function From Histological Sections of Isotropic Scattering Media

The frequency-dependent ultrasonic backscatter coefficient (BSC) from tissues, a fundamental parameter estimated by quantitative ultrasound (QUS) techniques, contains microstructure information useful for tissue characterization. To extract the microstructure information from the BSC, the tissue under investigation is often modeled as a collection of discrete scatterers embedded in a homogeneous background. From a discrete scatterer point of view, the BSC is dependent on not only the properties of individual scatterers relative to the background but also the scatterer spatial arrangement [described by the structure function (SF)]. Recently, the 2-D SF was computed from histological tissue sections, and was shown to be related to the volumetric SF extracted from QUS measurements. In this paper, a stereological method is proposed to extract the volumetric (3-D) SF from 2-D histological tissue sections. Simulations and experimental cell pellet biophantom studies were conducted to evaluate the proposed method. Simulation results verified the proposed method. Experimental results showed that the volumetric SF extracted using the proposed method had a significantly better agreement with the QUS-extracted SF than did the 2-D SF extracted in the previous study. The proposed stereological approach provides a useful tool for predicting the SF from histology.

Effects of the container on structure function with impedance map analysis of dense scattering media

Quantitative ultrasound (QUS) can be used to estimate acoustic properties of tissue microstructure. In one approach to QUS, the backscatter coefficient (BSC) is utilized to quantify and classify tissue state. From the BSC, parametric models can be constructed to relate the frequency-dependent BSC to geometrical properties of the underlying tissue. However, most of these parametric models are based on analytic expressions (e.g., Gaussian function) and not on actual tissue morphology. Impedance map analysis has been proposed to help identify sources of ultrasonic scattering in tissues and to develop improved models of scattering. Previously, two-dimensional impedance maps (2DZMs) were demonstrated to provide tissue models of three-dimensional (3D) structures for sparse scattering media. In the current study, 2DZMs analysis of dense scatterer media combining the structure function with impedance map analysis was studied through a series of simulations. The simulation analysis demonstrated that the correlation coefficient and power spectrum could be estimated for a dense collection of spheres using 2DZMs. The current finding implies that 2DZMs can capture information about the 3D spatial positions of scatterers in addition to information about the size and shape of the scatterers for a dense scattering media, which is expected to be encountered in many tissues.

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.

Analysis of Two Quantitative Ultrasound Approaches

There are two well-known ultrasonic approaches to extract sets of quantitative parameters: Lizzi-Feleppa (LF) parameters: slope, intercept, and midband; and quantitative ultrasound (QUS)-derived parameters: effective scatterer diameter (ESD) and effective acoustic concentration (EAC). In this study, the relation between the LF and QUS-derived parameters is studied theoretically and experimentally on ex vivo mouse livers. As expected from the theory, LF slope is correlated to ESD ([Formula: see text]), and from experimental data, LF midband is correlated to EAC ([Formula: see text]). However, LF intercept is not correlated to ESD ([Formula: see text]) nor EAC ([Formula: see text]). The unexpected correlation observed between LF slope and EAC ([Formula: see text]) results likely from the high correlation between ESD and EAC due to the inversion process. For a liver fat percentage estimation, an important potential medical application, the parameters presenting the better correlation are EAC ([Formula: see text]) and LF midband ([Formula: see text]).

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.