Quantitative assessment of in vivo breast masses using ultrasound attenuation and backscatter

Pulse-echo ultrasound attenuation tomography

Objective.We present the first fully two-dimensional attenuation imaging technique developed for pulse-echo ultrasound systems. Unlike state-of-the-art techniques, which use line-by-line acquisitions, our method uses steered emissions to constrain attenuation values at each location with multiple crossing wave paths, essential to resolve the spatial variations of this tissue property.Approach.At every location, we compute normalized cross-correlations between the beamformed images that are obtained from emissions at different steering angles. We demonstrate that their log-amplitudes provide the changes between attenuation-induced amplitude losses undergone by the different incident waves. This allows us to formulate a linear tomographic problem, which we efficiently solve via a Tikhonov-regularized least-squares approach.Main results.The performance of our tomography technique is first validated in numerical examples and then experimentally demonstrated in custom-made tissue-mimicking phantoms with inclusions of varying size, echogenicity, and attenuation. We show that this technique is particularly good at resolving lateral variations in tissue attenuation and remains accurate in media with varying echogenicity.Significance.Based on a similar principle, this method can be easily combined with computed ultrasound tomography in echo mode for speed-of-sound imaging, paving the way towards a multi-modal ultrasound tomography framework characterizing multiple acoustic tissue properties simultaneously.

Spectral-based Quantitative Ultrasound Imaging Processing Techniques: Comparisons of RF Versus IQ Approaches

Quantitative ultrasound (QUS) is an imaging technique which includes spectral-based parameterization. Typical spectral-based parameters include the backscatter coefficient (BSC) and attenuation coefficient slope (ACS). Traditionally, spectral-based QUS relies on the radio frequency (RF) signal to calculate the spectral-based parameters. Many clinical and research scanners only provide the in-phase and quadrature (IQ) signal. To acquire the RF data, the common approach is to convert IQ signal back into RF signal via mixing with a carrier frequency. In this study, we hypothesize that the performance, that is, accuracy and precision, of spectral-based parameters calculated directly from IQ data is as good as or better than using converted RF data. To test this hypothesis, estimation of the BSC and ACS using RF and IQ data from software, physical phantoms and in vivo rabbit data were analyzed and compared. The results indicated that there were only small differences in estimates of the BSC between when using the original RF, the IQ derived from the original RF and the RF reconverted from the IQ, that is, root mean square errors (RMSEs) were less than 0.04. Furthermore, the structural similarity index measure (SSIM) was calculated for ACS maps with a value greater than 0.96 for maps created using the original RF, IQ data and reconverted RF. On the other hand, the processing time using the IQ data compared to RF data were substantially less, that is, reduced by more than a factor of two. Therefore, this study confirms two things: (1) there is no need to convert IQ data back to RF data for conducting spectral-based QUS analysis, because the conversion from IQ back into RF data can introduce artifacts. (2) For the implementation of real-time QUS, there is an advantage to convert the original RF data into IQ data to conduct spectral-based QUS analysis because IQ data-based QUS can improve processing speed.

Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes

Traditional B-mode ultrasound has difficulties distinguishing benign from malignant breast lesions. It appears that Quantitative Ultrasound (QUS) may offer advantages. We examined the QUS imaging system's potential, utilizing parameters like Attenuation Coefficient (AC), Speed of Sound (SoS), Effective Scatterer Diameter (ESD), and Effective Scatterer Concentration (ESC) to enhance diagnostic accuracy. B-mode images and radiofrequency signals were gathered from breast lesions. These parameters were processed and analyzed by a QUS system trained on a simulated acoustic dataset and equipped with an encoder-decoder structure. Fifty-seven patients were enrolled over six months. Biopsies served as the diagnostic ground truth. AC, SoS, and ESD showed significant differences between benign and malignant lesions (p < 0.05), but ESC did not. A logistic regression model was developed, demonstrating an area under the receiver operating characteristic curve of 0.90 (95% CI: 0.78, 0.96) for distinguishing between benign and malignant lesions. In conclusion, the QUS system shows promise in enhancing diagnostic accuracy by leveraging AC, SoS, and ESD. Further studies are needed to validate these findings and optimize the system for clinical use.

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.

Confounders of Ultrasound Attenuation Imaging in a Linear Probe Using the Canon Aplio i800 System: A Phantom Study

There have been studies showing attenuation imaging (ATI) with ultrasound as an approach to diagnose liver diseases such as steatosis or cirrhosis. So far, this technique has only been used on a convex probe. The goal of the study was to investigate the feasibility of ATI measurements using the linear array on a canon Aplio i800 scanner on certified phantoms. Three certified liver tissue attenuation phantoms were measured in five different positions using a linear probe. The effects of positioning and depth were explored and compared. The values were compared to the certified expected value for each phantom as well as the different measurement values for each measurement position. The ATI measurements on phantoms showed significant effect for the different probe positions and region of interest (ROI) depths. Values taken in the center with the probe perpendicular to the phantom were closest to certified values. Median values at 2.5-4.5 cm depth for phantoms 1 and 2 and 0.5-2.5 cm for phantom 3 were comparable with certified values. Measurements taken at a depth greater than 6 cm in any position were the least representative of the certified values (p-value < 0.01) and had the widest range throughout the different sessions. ATI measurements can be performed with the linear probe in phantoms; however, careful consideration should be given to depth dependency, as it can significantly affect measurement values. Remaining measurements at various depths within the 0.5-6.0 cm range showed deviation from the certified values of approximately 25%.

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.

Inter- and Intra-Operator Variability of Regularized Backscatter Quantitative Ultrasound for the Characterization of Breast Masses

OBJECTIVES

Here we report on the intra- and inter-operator variability of the backscatter coefficient (BSC) estimated with a new low-variance quantitative ultrasound (QUS) approach applied to breast lesions in vivo.

METHODS

Radiofrequency (RF) echo signals were acquired from 29 BIRADS 4 and 5 breast lesions in 2 sequential cohorts following 2 imaging protocols: cohort 1) radial and antiradial views, and cohort 2) short- and long-axis views. Protocol 2 was implemented after retraining and discussion on how to improve reproducibility. Each patient was scanned by at least 2 of 3 radiologists; each performed 3 acquisitions with transducer and patient repositioning in between acquisitions. BSC was estimated using a low-variance QUS approach based on regularization. Intra- and inter-operator variability of the intra-lesion median BSC was evaluated with a multifactorial ANOVA test (P-values) and the intraclass correlation coefficient (ICC).

RESULTS

Inter-operator variability was only significant in the first protocol (P < .007); ICCinter  = .77 (95% CI .71-.82), indicating good inter-operator agreement. In the second protocol, the inter-operator variability was not significant (P > .05) and agreement was excellent (ICCinter  = .92 [.89-.94]). In both protocols, the intra-operator variability was not significant.

CONCLUSIONS

Our findings demonstrate the need for standardizing image acquisition protocols for backscatter-based QUS to reduce inter-operator variability and ensure its successful translation to the characterization of suspicious breast masses.

Physics-Inspired Regularized Pulse-Echo Quantitative Ultrasound: Efficient Optimization With ADMM

Pulse-echo quantitative ultrasound (PEQUS), which estimates the quantitative properties of tissue microstructure, entails estimating the average attenuation and the backscatter coefficient (BSC). Growing recent research has focused on the regularized estimation of these parameters. Herein, we make two contributions to this field: first, we consider the physics of the average attenuation and backscattering to devise regularization terms accordingly. More specifically, since the average attenuation gradually alters in different parts of the tissue, while BSC can vary markedly from tissue to tissue, we apply L2 and L1 norms for the average attenuation and the BSC, respectively. Second, we multiply different frequencies and depths of the power spectra with different weights according to their noise levels. Our rationale is that the high-frequency contents of the power spectra at deep regions have a low signal-to-noise ratio (SNR). We exploit the alternating direction method of multipliers (ADMM) for optimizing the cost function. The qualitative and quantitative evaluations of bias and variance exhibit that our proposed algorithm improves the estimations of the average attenuation and the BSC up to about 100%.

High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue

Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N = 5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R2 > 0.27, p < 0.001) and the inverse relationship with nuclear spacing (R2 > 0.17, p < 0.001).

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.

Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images

Traditional breast ultrasound imaging is a low-cost, real-time and portable method to assist with breast cancer screening and diagnosis, with particular benefits for patients with dense breast tissue. We previously demonstrated that incorporating coherence-based beamforming additionally improves the distinction of fluid-filled from solid breast masses, based on qualitative image interpretation by board-certified radiologists. However, variable sensitivity (range: 0.71-1.00 when detecting fluid-filled masses) was achieved by the individual radiologist readers. Therefore, we propose two objective coherence metrics, lag-one coherence (LOC) and coherence length (CL), to quantitatively determine the content of breast masses without requiring reader assessment. Data acquired from 31 breast masses were analyzed. Ideal separation (i.e., 1.00 sensitivity and specificity) was achieved between fluid-filled and solid breast masses based on the mean or median LOC value within each mass. When separated based on mean and median CL values, the sensitivity/specificity decreased to 1.00/0.95 and 0.92/0.89, respectively. The greatest sensitivity and specificity were achieved in dense, rather than non-dense, breast tissue. These results support the introduction of an objective, reader-independent method for automated diagnoses of cystic breast masses.

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.

In Vivo Measurements of the Bulk Ultrasonic Attenuation Coefficient of Breast Tissue Using a Novel Phase-Insensitive Receiver

This study describes the first in vivo acoustic attenuation measurements of breast tissue undertaken using a novel phase-insensitive detection technique employing a differential pyroelectric sensor. The operation of the sensor is thermal in nature, with its output signal being dictated by the acoustic power integrated over its surface. The particularly novel feature of the sensor lies in its differential principle of operation, which significantly enhances its immunity to background acoustic and vibration noise. A large area variant of the sensor was used to detect ultrasonic energy generated by an array of 14 discrete 3.2-MHz plane piston transducers, transmitted through pendent breasts in water. The transduction and reception capability represent key parts of a prototype Quantitative Ultrasound Computed Tomography Test Facility developed at the National Physical Laboratory to study the efficacy of phase-insensitive ultrasound computed tomography of breast phantoms containing a range of appropriate inclusions, in particular, the measurement uncertainties associated with quantitative reconstructions of the acoustic attenuation coefficient. For this study, attenuation coefficient measurements were made using 1-D projections on 12 nominally healthy study volunteers, whose age ranged from 19 to 65 years. Averaged or bulk attenuation coefficient values were generated in the range 1.7-4.6 dBcm ^-1 at 3.2 MHz and have been compared with existing literature, derived from in vivo and ex vivo studies. Results are encouraging and indicate that the relatively simple technique could be applied as a robust method for assessing the properties of breast tissue, particularly the balance of fatty (adipose) and fibroglandular components.

Ultrasonic attenuation of canine mammary tumours

BACKGROUND

Canine mammary tumours (CMTs) are the most common neoplasm appearing in female dogs and are considered the equivalent animal model of human breast cancer. However, in the literature, there is a gap for ultrasonic characterisation of these tumours. In this study, experimental measurements for acoustic attenuation and propagation speed of three surgically excised malignant CMTs were implemented.

METHODS

The three tumours were fixed in formaldehyde for up to 72 h and a total of five sample pieces were sectioned from the three tumours to account for the varied morphology observed along the tumours. The through-transmission and pulse-echo techniques were employed for experimental measurements of the acoustic attenuation and propagation speed.

RESULTS

Acoustic propagation speed of the five samples as measured at 2.7 MHz was in the range of 1568-1636 m/s. Correspondingly, acoustic attenuation was in the range of 1.95-3.45 dB/cm.MHz. Variations in both speed and attenuation were observed between samples acquired from the same tumour.

CONCLUSIONS

Present findings suggest that both acoustic attenuation and propagation speed of CMTs are higher than normal canine tissues due to increased heterogeneity and varied morphology visually observed between the tumour specimens and evidenced by histological examination. Nevertheless, experimental results could aid in enhancing the use of ultrasound in the diagnosis and treatment of CMTs as well as provide essential data for comparative oncology.

Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework

In this study we investigate the potential of parametric images formed from ultrasound B-mode scans using the Nakagami distribution for non-invasive classification of breast lesions and characterization of breast tissue. Through a sliding window technique, we generated seven types of Nakagami images for each patient scan in our dataset using basic and as well as derived parameters of the Nakagami distribution. To determine the suitable window size for image generation, we conducted an empirical analysis using 4 windows, which includes 3 column windows of lengths 0.1875 mm, 0.45 mm and 0.75 mm and widths of 0.002 mm, along with the standard square window with sides equal to three times the pulse length of incident ultrasound. From the parametric image sets generated using each window, we extracted a total of 72 features that consisted of morphometric, elemental and hybrid features. To our knowledge no other literature has conducted such a comprehensive analysis of Nakagami parametric images for the classification of breast lesions. Feature selection was performed to find the most useful subset of features from each of the parametric image sets for the classification of breast cancer. Analyzing the classification accuracy and Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of the selected feature subsets, we determined that the selected features acquired from Nakagami parametric images generated using a column window of length 0.75 mm provides the best results for characterization of breast lesions. This optimal feature set provided a classification accuracy of 93.08%, an AUC of 0.9712, a False Negative Rate (FNR) of 0%, and a very low False Positive Rate (FPR) of 8.65%. Our results indicate that the high accuracy of such a procedure may assist in the diagnosis of breast cancer by helping to reduce false positive diagnoses.

Feasibility of ultrasound tomography-guided localized mild hyperthermia using a ring transducer: Ex vivo and in silico studies

BACKGROUND

As of 2022, breast cancer continues to be the most diagnosed cancer worldwide. This problem persists within the United States as well, as the American Cancer Society has reported that ∼12.5% of women will be diagnosed with invasive breast cancer over the course of their lifetime. Therefore, a clinical need continues to exist to address this disease from a treatment and therapeutic perspective. Current treatments for breast cancer and cancers more broadly include surgery, radiation, and chemotherapy. Adjuncts to these methods have been developed to improve the clinical outcomes for patients. One such adjunctive treatment is mild hyperthermia therapy (MHTh), which has been shown to be successful in the treatment of cancers by increasing effectiveness and reduced dosage requirements for radiation and chemotherapies. MHTh-assisted treatments can be performed with invasive thermal devices, noninvasive microwave induction, heating and recirculation of extracted patient blood, or whole-body hyperthermia with hot blankets.

PURPOSE

One common method for inducing MHTh is by using microwave for heat induction and magnetic resonance imaging for temperature monitoring. However, this leads to a complex, expensive, and inaccessible therapy platform. Therefore, in this work we aim to show the feasibility of a novel all-acoustic MHTh system that uses focused ultrasound (US) to induce heating while also using US tomography (UST) to provide temperature estimates. Changes in sound speed (SS) have been shown to be strongly correlated with temperature changes and can therefore be used to indirectly monitor heating throughout the therapy. Additionally, these SS estimates allow for heterogeneous SS-corrected phase delays when heating complex and heterogeneous tissue structures.

METHODS

Feasibility to induce localized heat in tissue was investigated in silico with a simulated breast model, including an embedded tumor using continuous wave US. Here, both heterogenous acoustic and thermal properties were modeled in addition to blood perfusion. We further demonstrate, with ex vivo tissue phantoms, the feasibility of using ring-based UST to monitor temperature by tracking changes in SS. Two phantoms (lamb tissue and human abdominal fat) with latex tubes containing varied temperature flowing water were imaged. The measured SS of the water at each temperature were compared against values that are reported in literature.

RESULTS

Results from ex vivo tissue studies indicate successful tracking of temperature under various phantom configurations and ranges of water temperature. The results of in silico studies show that the proposed system can heat an acoustically and thermally heterogenous breast model to the clinically relevant temperature of 42°C while accounting for a reasonable time needed to image the current cross section (200 ms). Further, we have performed an initial in silico study demonstrating the feasibility of adjusting the transmit waveform frequency to modify the effective heating height at the focused region. Lastly, we have shown in a simpler 2D breast model that MHTh level temperatures can be maintained by adjusting the transmit pressure intensity of the US ring.

CONCLUSIONS

This work has demonstrated the feasibility of using a 256-element ring array transducer for temperature monitoring; however, future work will investigate minimizing the difference between measured SS and the values shown in literature. A hypothesis attributes this bias to potential volumetric average artifacts from the ray-based SS inversion algorithm that was used, and that moving to a waveform-based SS inversion algorithm will greatly improve the SS estimates. Additionally, we have shown that an all-acoustic MHTh system is feasible via in silico studies. These studies have indicated that the proposed system can heat a tumor within a heterogenous breast model to 42°C within a narrow time frame. This holds great promise for increasing the accessibility and reducing the complexity of a future all-acoustic MHTh system.

Photoacoustic imaging phantoms for assessment of object detectability and boundary buildup artifacts

Standardized phantoms and test methods are needed to accelerate clinical translation of emerging photoacoustic imaging (PAI) devices. Evaluating object detectability in PAI is challenging due to variations in target morphology and artifacts including boundary buildup. Here we introduce breast fat and parenchyma tissue-mimicking materials based on emulsions of silicone oil and ethylene glycol in polyacrylamide hydrogel. 3D-printed molds were used to fabricate solid target inclusions that produced more filled-in appearance than traditional PAI phantoms. Phantoms were used to assess understudied image quality characteristics (IQCs) of three PAI systems. Object detectability was characterized vs. target diameter, absorption coefficient, and depth. Boundary buildup was quantified by target core/boundary ratio, which was higher in transducers with lower center frequency. Target diameter measurement accuracy was also size-dependent and improved with increasing transducer frequency. These phantoms enable evaluation of multiple key IQCs and may support development of comprehensive standardized test methods for PAI devices.

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, that is, the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smooth regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.

Finite Element Modeling of Quantitative Ultrasound Analysis of the Surgical Margin of Breast Tumor

Ultrasound is commonly used as an imaging tool in the medical sector. Compared to standard ultrasound imaging, quantitative ultrasound analysis can provide more details about a material microstructure. In this study, quantitative ultrasound analysis was conducted through computational modeling to detect various breast duct pathologies in the surgical margin tissue. Both pulse-echo and pitch-catch methods were evaluated for a high-frequency (22–41 MHz) ultrasound analysis. The computational surgical margin modeling was based on various conditions of breast ducts, such as normal duct, ductal hyperplasia, DCIS, and calcification. In each model, ultrasound pressure magnitude variation in the frequency spectrum was analyzed through peak density and mean-peak-to-valley distance (MPVD) values. Furthermore, the spectral patterns of all the margin models were compared to extract more pathology-based information. For the pitch-catch mode, only peak density provided a trend in relation to different duct pathologies. For the pulse-echo mode, only the MPVD was able to do that. From the spectral comparison, it was found that overall pressure magnitude, spectral variation, peak pressure magnitude, and corresponding frequency level provided helpful information to differentiate various pathologies in the surgical margin.

3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulation studies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

The ultrasonic attenuation and backscatter coefficients of tissues are relevant acoustic parameters due to their wide range of clinical applications. In this paper, a linear least-squares method for the estimation of these coefficients in a homogeneous region of interest based on pulse-echo measurements is proposed. The method efficiently fits an ultrasound backscattered signal model to the measurements in both the frequency and depth dimension simultaneously at a low computational cost. It is demonstrated that the inclusion of depth information has a positive effect particularly on the accuracy of the estimated attenuation. The sensitivity of the attenuation and backscatter coefficients' estimates to several predefined parameters such as the window length, window overlap and usable bandwidth of the spectrum is also studied. Comparison of the proposed method with a benchmark approach based on dynamic programming highlights better performance of our method in estimating these coefficients, both in terms of accuracy and computation time. Further analysis of the computation time as a function of the predefined parameters indicates our method's potential to be used in real-time clinical settings.

Breast Cancer Detection using Multimodal Time Series Features from Ultrasound Shear Wave Absolute Vibro-Elastography

In shear wave absolute vibro-elastography (S-WAVE), a steady-state multi-frequency external mechanical excitation is applied to tissue, while a time-series of ultrasound radio-frequency (RF) data are acquired. Our objective is to determine the potential of S-WAVE to classify breast tissue lesions as malignant or benign. We present a new processing pipeline for feature-based classification of breast cancer using S-WAVE data, and we evaluate it on a new data set collected from 40 patients. Novel bi-spectral and Wigner spectrum features are computed directly from the RF time series and are combined with textural and spectral features from B-mode and elasticity images. The Random Forest permutation importance ranking and the Quadratic Mutual Information methods are used to reduce the number of features from 377 to 20. Support Vector Machines and Random Forest classifiers are used with leave-one-patient-out and Monte Carlo cross-validations. Classification results obtained for different feature sets are presented. Our best results (95% confidence interval, Area Under Curve = 95%1.45%, sensitivity = 95%, and specificity = 93%) outperform the state-of-the-art reported S-WAVE breast cancer classification performance. The effect of feature selection and the sensitivity of the above classification results to changes in breast lesion contours is also studied. We demonstrate that time-series analysis of externally vibrated tissue as an elastography technique, even if the elasticity is not explicitly computed, has promise and should be pursued with larger patient datasets. Our study proposes novel directions in the field of elasticity imaging for tissue classification.

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.

Analytic Global Regularized Backscatter Quantitative Ultrasound

Although a variety of techniques have been developed to reduce the appearance of B-mode speckle, quantitative ultrasound (QUS) aims at extracting the hidden properties of the tissue. Herein, we propose two novel techniques to accurately and precisely estimate two important QUS parameters, namely, the average attenuation coefficient and the backscatter coefficient. Both the techniques optimize a cost function that incorporates data and continuity constraint terms, which we call AnaLytical Global rEgularized BackscatteR quAntitative ultrasound (ALGEBRA). We propose two versions of ALGEBRA, namely, 1-D- and 2-D-ALGEBRA. In 1-D-ALGEBRA, the regularized cost function is formulated in the axial direction, and the QUS parameters are calculated for one line of radio frequency (RF) echo data. In 2-D-ALGEBRA, the regularized cost function is formulated for the entire image, and the QUS parameters throughout the image are estimated simultaneously. This simultaneous optimization allows 2-D-ALGEBRA to "see" all the data before estimating the QUS parameters. In both the methods, we efficiently optimize the cost functions by casting it as a sparse linear system of equations. As a result of this efficient optimization, 1-D-ALGEBRA and 2-D-ALGEBRA are, respectively, 600 and 300 times faster than optimization using the dynamic programming (DP) method previously proposed by our group. In addition, the proposed technique has fewer input parameters that require manual tuning. Our results demonstrate that the proposed ALGEBRA methods substantially outperform least-square and DP methods in estimating the QUS parameters in phantom experiments.

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.

Amplitude based segmentation of ultrasound echoes for attenuation coefficient estimation

In vivo ultrasound attenuation coefficient measurements are of interest as they can provide insight into tissue pathology. They are also needed so that measurements of the tissue's frequency dependent ultrasound backscattering coefficient may be corrected for attenuation. In vivo measurements of the attenuation coefficient are challenging because it has to be estimated from the depth dependent decay of backscatter signals that display a large degree of magnitude variation. In this study we describe and evaluate an improved backscatter method to estimate ultrasound attenuation which is tolerant to the presence of some backscatter inhomogeneity. This employs an automated algorithm to segment and remove atypically strong echoes to lessen the potential bias these may introduce on the attenuation coefficient estimates. The benefit of the algorithm was evaluated by measuring the frequency dependent attenuation coefficient of a gelatine phantom containing randomly distributed cellulose scatterers as a homogeneous backscattering component and planar pieces of cooked leek to provide backscattering inhomogeneities. In the phantom the segmentation algorithm was found to improve the accuracy and precision of attenuation coefficient estimates by up to 80% and 90%, respectively. The effect of the algorithm was then measured invivo using 32 radiofrequency B-mode datasets from the breasts of two healthy female volunteers, producing a 5 to 25% reduction in mean attenuation coefficient estimates and a 30 to 50% reduction in standard deviation of attenuation coefficient across different positions within each breast. The results suggest that the segmentation algorithm may improve the accuracy and precision of attenuation coefficient estimates invivo.

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

Aim:

We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT).

Materials & methods:

QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders.

Results:

Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively.

Conclusion:

QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment.

Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this study, we had performed ultrasound scans from the patient’s neck node during radiation treatment (after 24 h, 1 and 4 weeks). Artificial intelligence was used to interpret the ultrasound imaging and predict the response to radiation at the end of 3 months. The scans obtained after the first week were able to predict the treatment response with reasonable accuracy (86%).

Optimization of Ultrasound Backscatter Spectroscopy to Assess Neurotoxic Effects of Anesthesia in the Newborn Non-human Primate Brain

Studies in animal models have revealed that long exposures to anesthetics can induce apoptosis in the newborn and young developing brain. These effects have not been confirmed in humans because of the lack of a non-invasive, practical in vivo imaging tool with the ability to detect these changes. Following the successful use of ultrasound backscatter spectroscopy (UBS) to monitor in vivo cell death in breast tumors, we aimed to use UBS to assess the neurotoxicity of the anesthetic sevoflurane (SEVO) in a non-human primate (NHP) model. Sixteen 2- to 7-day-old rhesus macaques were exposed for 5 h to SEVO. Ultrasound scanning was done with a phased array transducer on a clinical ultrasound scanner operated at 10 MHz. Data consisting of 10-15 frames of radiofrequency (RF) echo signals from coronal views of the thalamus were obtained 0.5 and 6.0 h after initiating exposure. The UBS parameter "effective scatterer size" (ESS) was estimated by fitting a scattering form factor (FF) model to the FF measured from RF echo signals. The approach involved analyzing the frequency dependence of the measured FF to characterize scattering sources and selecting the FF model based on a χ² goodness-of-fit criterion. To assess data quality, a rigorous acceptance criterion based on the analysis of prevalence of diffuse scattering (an assumption in the estimation of ESS) was established. ESS changes after exposure to SEVO were compared with changes in a control group of five primates for which ultrasound data were acquired at 0 and 10 min (no apoptosis expected). Over the entire data set, the average measured FF at 0.5 and 6.0 h monotonically decreased with frequency, justifying fitting a single FF over the analysis bandwidth. χ² values of a (inhomogeneous continuum) Gaussian FF model were one-fifth those of the discrete fluid sphere model, suggesting that a continuum scatterer model better represents ultrasound scattering in the young rhesus brain. After application of the data quality criterion, only 5 of 16 subjects from the apoptotic group and 5 of 5 subjects from the control group fulfilled the acceptance criteria. All subjects in the apoptotic group that passed the acceptance criterion exhibited a significant ESS reduction at 6.0 h. These changes (-6.4%, 95% Interquartile Range: -14.3% to -3.3%) were larger than those in the control group (-0.8%, 95% Interquartile Range: -2.0% to 1.5%]). Data with a low prevalence of diffuse scattering corresponded to possibly biased results. Thus, ESS has the potential to detect changes in brain microstructure related to anesthesia-induced apoptosis.

Coherence-Based Beamforming Increases the Diagnostic Certainty of Distinguishing Fluid from Solid Masses in Breast Ultrasound Exams

Ultrasound is often used as a supplement for mammography to detect breast cancer. However, one known limitation is the high false-positive rates associated with breast ultrasound. We investigated the use of coherence-based beamforming (which directly displays spatial coherence) as a supplement to standard ultrasound B-mode images in 25 patients recommended for biopsy (26 masses in total), with the eventual goal of decreasing false-positive rates. Because of the coherent signal present within solid masses, coherence-based beamforming methods allow solid and fluid-filled masses to appear significantly different (p < 0.001). When presented to five board-certified radiologists, the inclusion of robust short-lag spatial coherence (R-SLSC) images in the diagnostic pipeline reduced the uncertainty of fluid-filled mass contents from 47.5% to 15.8% and reduced the percentage of fluid-filled masses unnecessarily recommended for biopsy from 43.3% to 13.3%. These results are promising for the potential introduction of R-SLSC (and related coherence-based beamforming methods) into the breast clinic to improve diagnostic certainty and reduce the number of unnecessary biopsies.

Fine-tuning the H-scan for discriminating changes in tissue scatterers

The H-scan approach ('H' denoting hue, or Hermite) is a recent matched filter methodology that aims to add information to the traditional ultrasound B-scan. The theory is based on the differences in the echoes produced by different classes of reflectors or scatterers. Matched filters can be created for different types of scatterers, whereby the maximum output indicates a match, and color schemes can be used to indicate the class of scatterer responsible for echoes, providing a visual interpretation of the results. However, within the theory of weak scattering from a variety of shapes, small changes in the size of the inhomogeneous objects will create shifts in the scattering transfer function. In this paper, we argue for a general power law transfer function as the canonical model for transfer functions from most normal soft vascularized tissues, at least over some bandpass spectrum illuminated by the incident pulse. In cases where scatterer size and distributions change, this produces a corresponding shift in center frequency, along with time and frequency domain characteristics of echoes, and these are captured by matched filters to distinguish and visualize in color the major characteristics of scattering types. With this general approach, the H-scan matched filters can be set to elicit more fine grain shifts in scattering types, commensurate with more subtle changes in tissue morphology. Compensation for frequency-dependent attenuation is helpful for avoiding beam softening effects with increasing depths. Examples from phantoms and normal and pathological tissues are provided to demonstrate that the H-scan analysis and displays are sensitive to scatterer size and morphology, and can be adapted to conventional imaging systems.

Spectral analysis framework for compressed sensing ultrasound signals

PURPOSE

Compressed sensing (CS) is the theory of the recovery of signals that are sampled below the Nyquist sampling rate. We propose a spectral analysis framework for CS data that does not require full reconstruction for extracting frequency characteristics of signals by an appropriate basis matrix.

METHODS

The coefficients of a basis matrix already contain the spectral information for CS data, and the proposed framework directly utilizes them without completely restoring original data. We apply three basis matrices, i.e., DCT, DFT, and DWT, for sampling and reconstructing processes, subsequently estimating the attenuation coefficients to validate the proposed method. The estimation accuracy and precision, as well as the execution time, are compared using the reference phantom method (RPM).

RESULTS

The experiment results show the effective extraction of spectral information from CS signals by the proposed framework, and the DCT basis matrix provides the most accurate results while minimizing estimation variances. The execution time is also reduced compared with that of the traditional approach, which completely reconstructs the original data.

CONCLUSION

The proposed method provides accurate spectral analysis without full reconstruction. Since it effectively utilizes the data storage and reduces the processing time, it could be applied to small and portable ultrasound systems using the CS technique.

Oil Gel-Based Phantom for Evaluating Quantitative Accuracy of Speed of Sound Measured in Ultrasound Computed Tomography

To evaluate the quantitative accuracy of the measured speed of sound in ultrasound computed tomography for breast imaging, it is necessary to use a phantom with inclusions whose speed of sound is known. Accordingly, a phantom with known-speed-of-sound inclusions (e.g., containing water and saltwater solution) under the control of temperature was developed. In addition, an oil gel was used as the phantom material for mimicking wave refraction from fatty breast tissue to dense breast tissue. The oil gel was generated by adding SEBS (styrene-ethylene/butylene-styrene, 10% w/w) to paraffin oil. The oil gel-based phantom has a cylindrical shape and contains rod-shaped inclusions that can be filled with water or saltwater solution (3.5% w/w sodium chloride in water). When temperature increases, the speed of sound in the water increases, while that in the oil gel decreases; in particular, the speed of sound in the oil gel was higher than that in the water at temperatures <20.6°C, while the speed of sound in the oil gel was lower than that in the water at temperatures >20.6°C. It has been reported that the speed of sound in dense breast tissue is higher than that in water, while that in fatty breast tissue is lower than that in water. Ultrasound is refracted owing to the difference between the speed of sound in the breast tissue and that in the background water. By controlling the temperatures of the oil gel and water, the oil gel-based phantom simulates the refraction of an ultrasound wave from fatty breast tissue to dense breast tissue. For 43 d, the variation ranges of the speed of sound and attenuation in the oil gel in the reconstructed images were 0.7 m/s and 0.03 dB/MHz/cm, respectively. The concentration of the saltwater solution in the polyacrylamide gel-based phantom decreased from 1% (w/w) to 0.48% (w/w) after 24 h, while that in the oil-gel-based phantom was constant. In addition, magnetic resonance imaging of the oil gel-based phantom revealed that NiSO₄ solution was stably contained in the phantom for 42 d. It is therefore concluded that the liquid cannot penetrate the oil gel. This oil gel-based phantom with such high temporal stability is suitable for multicenter distribution and may be used for standardization of data acquisition and image reconstruction across centers.

A Quantitative Ultrasound-Based Multi-Parameter Classifier for Breast Masses

This manuscript reports preliminary results obtained by combining estimates of two or three (among seven) quantitative ultrasound (QUS) parameters in a model-free, multi-parameter classifier to differentiate breast carcinomas from fibroadenomas (the most common benign solid tumor). Forty-three patients scheduled for core biopsy of a suspicious breast mass were recruited. Radiofrequency echo signal data were acquired using clinical breast ultrasound systems equipped with linear array transducers. The reference phantom method was used to obtain system-independent estimates of the specific attenuation (ATT), the average backscatter coefficients, the effective scatterer diameter (ESD) and an effective scatterer diameter heterogeneity index (ESDHI) over regions of interest within each mass. In addition, the envelope amplitude signal-to-noise ratio (SNR), the Nakagami shape parameter, m, and the maximum collapsed average (maxCA) of the generalized spectrum were also computed. Classification was performed using the minimum Mahalanobis distance to the centroids of the training classes and tested against biopsy results. Classification performance was evaluated with the area under the receiver operating characteristic (ROC) curve. The best performance with a two-parameter classifier used the ESD and ESDHI and resulted in an area under the ROC curve of 0.98 (95% confidence interval [CI]: 0.95-1.00). Classification performance improved with three parameters (ATT, ESD and ESDHI) yielding an area under the ROC curve of 0.999 (0.995-1.000). These results suggest that system-independent QUS parameters, when combined in a model-free classifier, are a promising tool to characterize breast tumors. A larger study is needed to further test this idea.

Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue

The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.

Attenuation Coefficient Estimation of Normal Placentas

Attenuation coefficient estimation has the potential to be a useful tool for placental tissue characterization. A current challenge is the presence of inhomogeneities in biological tissue that result in a large variance in the attenuation coefficient estimate (ACE), restricting its clinical utility. In this work, we propose a new Attenuation Estimation Region Of Interest (AEROI) selection method for computing the ACE based on the (i) envelope signal-to-noise ratio deviation and (ii) coefficient of variation of the transmit pulse bandwidth. The method was first validated on a tissue-mimicking phantom, for which an 18%-21% reduction in the standard deviation of ACE and a 14%-24% reduction in the ACE error, expressed as a percentage of reported ACE, were obtained. A study on 59 post-delivery clinically normal placentas was then performed. The proposed AEROI selection method reduced the intra-subject standard deviation of ACE from 0.72 to 0.39 dB/cm/MHz. The measured ACE of 59 placentas was 0.77 ± 0.37 dB/cm/MHz, which establishes a baseline for future studies on placental tissue characterization.

Quantitative Ultrasound Biomarkers Based on Backscattered Acoustic Power: Potential for Quantifying Remodeling of the Human Cervix during Pregnancy

As pregnancy progresses, the cervix remodels from a rigid structure to one pliable enough to allow delivery of a fetus, a process that involves progressive disorganization of cervical microstructure. Quantitative ultrasound biomarkers that may detect this process include those derived from the backscattered echo signal, namely, acoustic attenuation and backscattered power loss. We recently reported that attenuation and backscattered power loss are affected by tissue anisotropy and heterogeneity in the ex vivo cervix. In this study, we compared attenuation and backscattered power difference in a group of women in early pregnancy (first trimester) with those in a group in late pregnancy (third trimester). We observed a significant decrease in the backscattered power difference in late as compared with early pregnancy, suggesting decreased microstructural organization in late pregnancy, a finding that is consistent with animal models of cervical remodeling. In contrast, we found no difference in attenuation between the time points. These results suggest that the backscattered power difference, but perhaps not attenuation, may be a useful clinical biomarker of cervical remodeling.

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.

Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming

One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties, such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissue attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. To overcome these limitations, in this paper, we incorporate the prior information of piecewise continuity of QUS parameters as a regularization term into our cost function. We further propose to calculate this cost function using dynamic programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a published least squares method in terms of both estimation bias and variance.

Review of quantitative multiscale imaging of breast cancer

Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.

EEMD Domain AR Spectral Method for Mean Scatterer Spacing Estimation of Breast Tumors From Ultrasound Backscattered RF Data

We present a novel method for estimating the mean scatterer spacing (MSS) of breast tumors using ensemble empirical mode decomposition (EEMD) domain analysis of deconvolved backscattered radio frequency (RF) data. The autoregressive (AR) spectrum from which the MSS is estimated is obtained from the intrinsic mode functions (IMFs) due to regular scatterers embedded in RF data corrupted by the diffuse scatterers. The IMFs are chosen by giving priority to the presence of an enhanced fundamental harmonic and the presence of a greater number of higher harmonics in the AR spectrum estimated from the IMFs. The AR model order is chosen by minimizing the mean absolute percentage error (MAPE) criterion. In order to ensure that the backscattered data is indeed from a source of coherent scattering, we begin by performing a non-parametric Kolmogorov-Smirnov (K-S) classification test on the backscattered RF data. Deconvolution of the backscattered RF data, which have been classified by the K-S test as sources of significant coherent scattering, is done to reduce the system effect. EEMD domain analysis is then performed on the deconvolved data. The proposed method is able to recover the harmonics associated with the regular scatterers and overcomes many problems encountered while estimating the MSS from the AR spectrum of raw RF data. Using our technique, a mean absolute percentage error (MAPE) of 5.78% is obtained while estimating the MSS from simulated data, which is lower than that of the existing techniques. Our proposed method is shown to outperform the state of the art techniques, namely, singular spectrum analysis, generalized spectrum (GS), spectral autocorrelation (SAC), and modified SAC for different simulation conditions. The MSS for in vivo normal breast tissue is found to be 0.69 ± 0.04 mm; for benign and malignant tumors it is found to be 0.73 ± 0.03 and 0.79 ± 0.04 mm, respectively. The separation between the MSS values of normal and benign tissues for our proposed method is similar to the separations obtained for the conventional methods, but the separation between the MSS values for benign and malignant tissues for our proposed method is slightly higher than that for the conventional methods. When the MSS is used to classify breast tumors into benign and malignant, for a threshold-based classifier, the increase in specificity, accuracy, and area under curve are 18%, 19%, and 22%, respectively, and that for statistical classifiers are 9%, 13%, and 19%, respectively, from that of the next best existing technique.

A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing

Tissues exhibiting quasi-periodic structures can be modeled as a collection of diffuse scatterers and coherent scatterers. The mean scatterer spacing (MSS) of coherent and quasi-periodic components is directly related to tissue microstructure and has become an important quantitative ultrasound (QUS) parameter in the characterization of quasi-periodic tissues. In this paper, a review of the literature on the development of MSS as a QUS parameter was conducted. First, a unified theoretical background of MSS estimates was provided. Then, the application of MSS estimates was summarized with respect to liver, spleen, breast, bone, muscle, and other tissues. MSS estimation techniques were applied to (a) the diagnosis of hepatitis, liver fibrosis and cirrhosis, and lesions in tissues such as liver, breast, and spleen; (b) the differentiation between benign and malignant breast tumors, and the grading of breast cancer; (c) the detection of cancellous bone; and (d) the monitoring of the efficacy of treatments such as thermal ablation, with various levels of success. Future developments were also discussed in terms of real-time implementation of MSS estimates, local MSS estimation, relationship of MSS to other QUS parameters, combination of MSS with other QUS parameters, in vivo validation of MSS estimates, MSS parametric imaging, and three-dimensional ultrasound tissue characterization.

Analysis of Coherent and Diffuse Scattering Using a Reference Phantom

The estimation of many spectral-based quantitative ultrasound parameters assumes that backscattered echo signals are from a stationary, incoherent scattering process. The accuracy of these assumptions in real tissue can limit the diagnostic value of these parameters and the physical insight about tissue microstructure they can convey. This work presents an empirical decision test to determine the presence of significant coherent contributions to echo signals and whether they are caused by low scatterer number densities or the presence of specular reflectors or scatterers with periodic spacing. This is achieved by computing parameters from echo signals that quantify stationary or nonstationary features related to coherent scattering, and then comparing their values to thresholds determined from a reference material providing diffuse scattering. The paper first presents a number of parameters with demonstrated sensitivity to coherent scattering and describes criteria to select those with the highest sensitivity using simulated and phantom-based echo data. Results showed that the echo amplitude signal-to-noise ratio and the multitaper-generalized spectrum were the parameters with the highest sensitivity to coherent scattering with stationary and nonstationary features, respectively. These parameters were incorporated into the reference-based decision test, which successfully identified regions in simulated and tissue-mimicking phantoms with different incoherent and coherent scattering conditions. When scatterers with periodic organization were detected, the combination of stationary and nonstationary analysis permitted the estimation of the mean spacing below and above the resolution limit imposed by the pulse size. Preliminary applications of this algorithm to human cervical tissue ex vivo showed correspondence between regions of B-mode images showing bright reflectors, tissue interfaces, and hypoechoic regions with regions classified as specular reflectors and low scatterer number density. These results encourage further application of the algorithm to more structurally complex phantoms and tissue.

Tissue characterization using simultaneous estimation of backscatter coefficient and elastic shear modulus

Tissue characterization using quantitative ultrasound (QUS) parameters has received significant attention in recent years due to its potential to improve the detection and diagnosis of diseased states. However, the vast majority of studies in QUS tissue typing have used parameters derived from either longitudinal or shear waves in isolation, thereby discarding potentially useful complementary information these parameters may carry. In this study, the simultaneous estimation of backscatter coefficients (derived from longitudinal waves) and shear modulus (derived from shear waves) was implemented on data from a clinical scanner. Both parameters were estimated from five ex vivo porcine kidney samples and used to calculate the anisotropy ratio in the parameters when analyzing the middle and pole regions of the kidneys. For all samples, the estimated parameters were higher in the pole regions than in the middle region, with anisotropy ratios of 1.42±0.11 and 3.07±0.70 for the shear modulus and the backscatter coefficient, respectively. Therefore, these results demonstrate that QUS parameters derived from both longitudinal and shear waves can be estimated simultaneously and may be used in conjunction to track changes in tissue structure and composition.