A Systematized Review of Quantitative Ultrasound Based on First-Order Speckle Statistics.

Since the late 1970s, the speckle interference patterns ubiquitous in pulse-echo ultrasound images have been used to characterize sub-resolution tissue structure. During this time, new models, estimation methods, and processing techniques have proliferated, offering a wealth of recommendations for the task of tissue characterization. A literature review was performed to draw attention to these various methods and to critically track assumptions and gaps in knowledge. A total of 388 articles were collected from a systematic search for first-order speckle statistics in diagnostic ultrasound in the NIH PubMed database and Elsevier's Scopus database. Articles were grouped by basic characteristics and evaluated for addressing fundamental assumptions. A sampling of models and methods is presented in order to reveal the state of the art in speckle statistics as well as sources of measurement error and other important considerations. While this body of literature emphasizes the value of speckle analysis in diagnostic ultrasound, it is shown that relatively little attention is devoted to basic assumptions such as the linearity of system response and scatterer geometry. Additionally, several areas of investigation are available to improve upon speckle statistics analysis, potentially leading to the advancement of this unique tool.

Simulation of ultrasonic scattering from scatterer size distributions using Field II.

Quantitative analysis of radio frequency (RF) signals obtained from ultrasound scanners can yield objective parameters that are gaining clinical relevance as imaging biomarkers. These include the backscatter coefficient (BSC) and the effective scatterer diameter (ESD). Biomarker validation is typically performed in phantoms which do not provide the flexibility of systematic variation of scattering properties. Computer simulations, such as those from the ultrasound simulator Field II, can allow more flexibility. However, Field II does not allow simulation of RF data from a distribution of scatterers with finite size. In this work, a simulation method is presented which builds upon previous work by including Faran theory models representative of distributions of scatterer size. These are systematically applied to RF data simulated in Field II. The method is validated by measuring the root mean square error of the estimated BSC and percent bias of the ESD and comparing to experimental results. The results indicate the method accurately simulates distributions of scatterer sizes and provides scattering similar to that seen in data from clinical scanners. Because Field II is widely used by the ultrasound community, this method can be adopted to aid in validation of quantitative ultrasound imaging biomarkers.

Equilibrium Mechanical Properties of the Nonhuman Primate Cervix.

Cervical remodeling is critical for a healthy pregnancy. Premature tissue changes can lead to preterm birth (PTB), and the absence of remodeling can lead to post-term birth, causing significant morbidity. Comprehensive characterization of cervical material properties is necessary to uncover the mechanisms behind abnormal cervical softening. Quantifying cervical material properties during gestation is challenging in humans. Thus, a nonhuman primate (NHP) model is employed for this study. In this study, cervical tissue samples were collected from Rhesus macaques before pregnancy and at three gestational time points. Indentation and tension mechanical tests were conducted, coupled with digital image correlation (DIC), constitutive material modeling, and inverse finite element analysis (IFEA) to characterize the equilibrium material response of the macaque cervix during pregnancy. Results show, as gestation progresses: (1) the cervical fiber network becomes more extensible (nonpregnant versus pregnant locking stretch: 2.03 ± 1.09 versus 2.99 ± 1.39) and less stiff (nonpregnant versus pregnant initial stiffness: 272 ± 252 kPa versus 43 ± 43 kPa); (2) the ground substance compressibility does not change much (nonpregnant versus pregnant bulk modulus: 1.37 ± 0.82 kPa versus 2.81 ± 2.81 kPa); (3) fiber network dispersion increases, moving from aligned to randomly oriented (nonpregnant versus pregnant concentration coefficient: 1.03 ± 0.46 versus 0.50 ± 0.20); and (4) the largest change in fiber stiffness and dispersion happen during the second trimester. These results, for the first time, reveal the remodeling process of a nonhuman primate cervix and its distinct regimes throughout the entire pregnancy.

A Force-Matched Approach to Large-Strain Nonlinearity in Elasticity Imaging for Breast Lesion Characterization.

OBJECTIVE: Ultrasound elasticity imaging is a class of ultrasound techniques with applications that include the detection of malignancy in breast lesions. Although elasticity imaging traditionally assumes linear elasticity, the large strain elastic response of soft tissue is known to be nonlinear. This study evaluates the nonlinear response of breast lesions for the characterization of malignancy using force measurement and force-controlled compression during ultrasound imaging. METHODS: 54 patients were recruited for this study. A custom force-instrumented compression device was used to apply a controlled force during ultrasound imaging. Motion tracking derived strain was averaged over lesion or background ROIs and matched with compression force. The resulting force-matched strain was used for subsequent analysis and curve fitting. RESULTS: Greater median differences between malignant and benign lesions were observed at higher compressional forces (p-value < 0.05 for compressional forces of 2-6N). Of three candidate functions, a power law function produced the best fit to the force-matched strain. A statistically significant difference in the scaling parameter of the power function between malignant and benign lesions was observed (p-value = 0.025). CONCLUSIONS: We observed a greater separation in average lesion strain between malignant and benign lesions at large compression forces and demonstrated the characterization of this nonlinear effect using a power law model. Using this model, we were able to differentiate between malignant and benign breast lesions. SIGNIFICANCE: With further development, the proposed method to utilize the nonlinear elastic response of breast tissue has the potential for improving non-invasive lesion characterization for potential malignancy.

Comparison of four commercial immunomagnetic separation kits for the detection of Cryptosporidium.

Cryptosporidium spp. are protozoan parasites of significant health importance found in environmental waters globally. Four commercially available Cryptosporidium-specific immunomagnetic separation (IMS) kits used in various water sample matrices were analysed and compared. Beads were characterised by flow cytometry and tested for the recovery efficiencies for oocysts spiked into different matrices: river water sediment, clay sample, and filter backwash sample. Results showed that Dynabeads™ Cryptosporidium and Waterborne Crypto-Grab™ kits contained immunoglobulin IgM antibody-coated beads. In contrast, the BioPoint CryptoBead and the TCS Isolate kits contained immunoglobulin IgG antibody-coated beads. BioPoint CryptoBead was significantly coated with more antibodies and were able to capture oocysts more rapidly compared to the other beads. Recovery efficiencies of Dynabeads™, TCS Isolate® beads, and BioPoint CryptoBead ranged from 55 to 93% when tested against different sample matrices, with BioPoint CryptoBead resulting in the highest at 93% in reagent-grade water and Dynabeads™ at 55%, the lowest against clay samples. The Waterborne beads did not perform well on any samples, with recovery efficiencies ranging from 0 to 8%. Fluorescence microscopy analyses showed that both the IMS method and the sample matrix processed affect the quality of the membranes, with the cleanest samples for microscopy examination observed from BioPoint CryptoBead.

Delivering a Quantitative Imaging Agenda.

In a digital image, each voxel contains quantitative information dependent on the technique used to generate the image [...].

Optical coherence tomography of human fetal membrane sub-layers during loading.

Fetal membranes have important mechanical and antimicrobial roles in maintaining pregnancy. However, the small thickness (<800 µm) of fetal membranes places them outside the resolution limits of most ultrasound and magnetic resonance systems. Optical imaging methods like optical coherence tomography (OCT) have the potential to fill this resolution gap. Here, OCT and machine learning methods were developed to characterize the ex vivo properties of human fetal membranes under dynamic loading. A saline inflation test was incorporated into an OCT system, and tests were performed on n = 33 and n = 32 human samples obtained from labored and C-section donors, respectively. Fetal membranes were collected in near-cervical and near-placental locations. Histology, endogenous two photon fluorescence microscopy, and second harmonic generation microscopy were used to identify sources of contrast in OCT images of fetal membranes. A convolutional neural network was trained to automatically segment fetal membrane sub-layers with high accuracy (Dice coefficients >0.8). Intact amniochorion bilayer and separated amnion and chorion were individually loaded, and the amnion layer was identified as the load-bearing layer within intact fetal membranes for both labored and C-section samples, consistent with prior work. Additionally, the rupture pressure and thickness of the amniochorion bilayer from the near-placental region were greater than those of the near-cervical region for labored samples. This location-dependent change in fetal membrane thickness was not attributable to the load-bearing amnion layer. Finally, the initial phase of the loading curve indicates that amniochorion bilayer from the near-cervical region is strain-hardened compared to the near-placental region in labored samples. Overall, these studies fill a gap in our understanding of the structural and mechanical properties of human fetal membranes at high resolution under dynamic loading events.

US Quantification of Liver Fat: Past, Present, and Future.

Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical costs, and may lead to liver-related morbidity and mortality. There is an urgent need for accurate, reproducible, accessible, and noninvasive techniques appropriate for detecting and quantifying liver fat in the general population and for monitoring treatment response in at-risk patients. CT may play a potential role in opportunistic screening, and MRI proton-density fat fraction provides high accuracy for liver fat quantification; however, these imaging modalities may not be suited for widespread screening and surveillance, given the high global prevalence. US, a safe and widely available modality, is well positioned as a screening and surveillance tool. Although well-established qualitative signs of liver fat perform well in moderate and severe steatosis, these signs are less reliable for grading mild steatosis and are likely unreliable for detecting subtle changes over time. New and emerging quantitative biomarkers of liver fat, such as those based on standardized measurements of attenuation, backscatter, and speed of sound, hold promise. Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. For each US-based technique, they describe the concept, measurement method, advantages, and limitations. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

A Geometric Model of Ultrasound Backscatter to Describe Microstructural Anisotropy of Tissue.

Methods to assess ultrasound backscatter anisotropy from clinical array transducers have recently been developed. However, they do not provide information about the anisotropy of microstructural features of the specimens. This work develops a simple geometric model, referred to as the secant model, of backscatter coefficient anisotropy. Specifically, we evaluate anisotropy of the frequency dependence of the backscatter coefficient parameterized in terms of effective scatterer size. We assess the model in phantoms with known scattering sources and in a skeletal muscle, a well-known anisotropic tissue. We demonstrate that the secant model can determine the orientation of the anisotropic scatterers, as well as accurately determining effective scatterer sizes, and it may classify isotropic versus anisotropic scatterers. The secant model may find utility in monitoring disease progression as well as characterizing normal tissue architectures.

A Multimodal Phantom for Visualization and Assessment of Histotripsy Treatments on Ultrasound and X-Ray Imaging.

OBJECTIVE: Histotripsy is an emerging non-invasive, non-ionizing and non-thermal focal tumor therapy. Although histotripsy targeting is currently based on ultrasound (US), other imaging modalities such as cone-beam computed tomography (CBCT) have recently been proposed to enable the treatment of tumors not visible on ultrasound. The objective of this study was to develop and evaluate a multi-modality phantom to facilitate the assessment of histotripsy treatment zones on both US and CBCT imaging. METHODS: Fifteen red blood cell phantoms composed of alternating layers with and without barium were manufactured. Spherical 25-mm histotripsy treatments were performed, and treatment zone size and location were measured on CBCT and ultrasound. Sound speed, impedance and attenuation were measured for each layer type. RESULTS: The average ± standard deviation signed difference between measured treatment diameters was 0.29 ± 1.25 mm. The Euclidean distance between measured treatment centers was 1.68 ± 0.63 mm. The sound speed in the different layers ranged from 1491 to 1514 m/s and was within typically reported soft tissue ranges (1480-1560 m/s). In all phantoms, histotripsy resulted in sharply delineated treatment zones, allowing segmentation in both modalities. CONCLUSION: These phantoms will aid in the development and validation of X-ray-based histotripsy targeting techniques, which promise to expand the scope of treatable lesions beyond only those visible on ultrasound.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs).

Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine.

Pulse-Echo Quantitative US Biomarkers for Liver Steatosis: Toward Technical Standardization.

Excessive liver fat (steatosis) is now the most common cause of chronic liver disease worldwide and is an independent risk factor for cirrhosis and associated complications. Accurate and clinically useful diagnosis, risk stratification, prognostication, and therapy monitoring require accurate and reliable biomarker measurement at acceptable cost. This article describes a joint effort by the American Institute of Ultrasound in Medicine (AIUM) and the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) to develop standards for clinical and technical validation of quantitative biomarkers for liver steatosis. The AIUM Liver Fat Quantification Task Force provides clinical guidance, while the RSNA QIBA Pulse-Echo Quantitative Ultrasound Biomarker Committee develops methods to measure biomarkers and reduce biomarker variability. In this article, the authors present the clinical need for quantitative imaging biomarkers of liver steatosis, review the current state of various imaging modalities, and describe the technical state of the art for three key liver steatosis pulse-echo quantitative US biomarkers: attenuation coefficient, backscatter coefficient, and speed of sound. Lastly, a perspective on current challenges and recommendations for clinical translation for each biomarker is offered.

Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography.

Energy-based ultrasound elastography techniques minimize a regularized cost function consisting of data and continuity terms to obtain local displacement estimates based on the local time-delay estimation (TDE) between radio frequency (RF) frames. The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration. This drawback creates noticeable artifacts in the strain image. To resolve this issue, we propose to formulate the data function as a linear combination of the amplitude and gradient similarity constraints. We estimate the adaptive weight concerning each similarity term following an iterative scheme. Finally, we optimize the nonlinear cost function in an efficient manner to convert the problem to a sparse system of linear equations which are solved for millions of variables. We call our technique rGLUE: robust data term in GLobal Ultrasound Elastography. rGLUE has been validated using simulation, phantom, in vivo liver, and breast datasets. In all our experiments, rGLUE substantially outperforms the recent elastography methods both visually and quantitatively. For simulated, phantom, and in vivo datasets, respectively, rGLUE achieves 107%, 18%, and 23% improvements of signal-to-noise ratio (SNR) and 61%, 19%, and 25% improvements of contrast-to-noise ratio (CNR) over global ultrasound elastography (GLUE), a recently published elastography algorithm.

Shear wave dispersion as a potential biomarker for cervical remodeling during pregnancy: evidence from a non-human primate model.

Shear wave dispersion (variation of phase velocity with frequency) occurs in tissues with layered and anisotropic microstructure and viscous components, such as the uterine cervix. This phenomenon, mostly overlooked in previous applications of cervical Shear Wave Elasticity Imaging (SWEI) for preterm birth risk assessment, is expected to change drastically during pregnancy due to cervical remodeling. Here we demonstrate the potential of SWEI-based descriptors of dispersion as potential biomarkers for cervical remodeling during pregnancy. First, we performed a simulation-based pre-selection of two SWEI-based dispersion descriptors: the ratio R of group velocities computed with particle-velocity and particle-displacement, and the slope S of the phase velocity vs. frequency. The pre-selection consisted of comparing the contrast-to-noise ratio (CNR) of dispersion descriptors in materials with different degrees of dispersion with respect to a low-dispersive medium. Shear waves induced in these media by SWEI were simulated with a finite-element model of Zener viscoelastic solids. The pre-selection also considered two denoising strategies to improve CNR: a low-pass filter with automatic frequency cutoff determination, and singular value decomposition of shear wave displacements. After pre-selection, the descriptor-denoising combination that produced the largest CNR was applied to SWEI cervix data from 18 pregnant Rhesus macaques acquired at weeks 10 (mid-pregnancy stage) and 23 (late pregnancy stage) of the 24.5-week full pregnancy. A maximum likelihood linear mixed-effects model (LME) was used to evaluate the dependence of the dispersion descriptor on pregnancy stage, maternal age, parity and other experimental factors. The pre-selection study showed that descriptor S combined with singular value decomposition produced a CNR 11.6 times larger than the other descriptor and denoising strategy combinations. In the Non-Human Primates (NHP) study, the LME model showed that descriptor S significantly decreased from mid to late pregnancy (-0.37 ± 0.07 m/s-kHz per week, p <0.00001) with respect to the base value of 15.5 ± 1.9 m/s-kHz. This change was more significant than changes in other SWEI features such as the group velocity previously reported. Also, S varied significantly between the anterior and posterior portions of the cervix (p =0.02) and with maternal age (p =0.008). Given the potential of shear wave dispersion to track cervical remodeling, we will extend its application to ongoing longitudinal human studies.

Ultrasound Scatterer Density Classification Using Convolutional Neural Networks and Patch Statistics.

Quantitative ultrasound (QUS) can reveal crucial information on tissue properties, such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or underdeveloped speckle (UDS), respectively. Conventionally, the scatterer density has been classified using estimated statistical parameters of the amplitude of backscattered echoes. However, if the patch size is small, the estimation is not accurate. These parameters are also highly dependent on imaging settings. In this article, we adapt convolutional neural network (CNN) architectures for QUS and train them using simulation data. We further improve the network's performance by utilizing patch statistics as additional input channels. Inspired by deep supervision and multitask learning, we propose a second method to exploit patch statistics. We evaluate the networks using simulation data and experimental phantoms. We also compare our proposed methods with different classic and deep learning models and demonstrate their superior performance in the classification of tissues with different scatterer density values. The results also show that we are able to classify scatterer density in different imaging parameters with no need for a reference phantom. This work demonstrates the potential of CNNs in classifying scatterer density in ultrasound images.