Quantitative ultrasound imaging of soft biological tissues: a primer for radiologists and medical physicists

Quantitative Ultrasound for Periodontal Soft Tissue Characterization

OBJECTIVE

Periodontal diseases are a spectrum of inflammatory diseases that affect 45.9% of adults aged ≥30 years in the United States Current standard of care in clinics for the assessment of oral soft tissue inflammation is bleeding on probing,which is invasive, subjective and semi-qualitative. Quantitative ultrasound (QUS) has shown promising results in the non-invasive quantitative characterization of various soft tissues; however, it has not been used in clinical periodontics.

METHODS

Here, we investigated the QUS analysis of two periodontal soft tissues (alveolar mucosa and gingiva) in vivo. The study cohort included 10 swine scanned at four oral quadrants, resulting in 40 scans. Two-parameter Burr and Nakagami models were employed for QUS-based speckle modeling. Parametric imaging of these parameters was also created using an optimal window size estimated in a separate phantom study.

RESULTS

Phantom results suggested a window size of 10 wavelengths as the reasonable estimation kernel. The Burr power-law parameter and Nakagami shape factor were higher in gingiva than alveolar mucosa, while Burr and Nakagami scale factors were both lower in the gingiva. The difference between the two tissue types was statistically significant (p < 0.0001). Linear classifications of these two tissue types using a 2-D parameter space of the Burr and Nakagami models resulted in a segmentation accuracy of 93.51% and 90.91%, respectively. Findings from histology-stained images showed that gingiva and alveolar mucosa had distinct underlying structures, with the gingiva showing a denser stain.

CONCLUSION

QUS results suggest that gingiva and alveolar mucosa can be differentiated using Burr and Nakagami parameters. We propose that QUS holds promising potential for the characterization of periodontal soft tissues and could become an objective and quantitative diagnostic tool for periodontology and implant dentistry to improve dental health care.

Quantitative Ultrasound Parameters as Predictors of Chemotherapy Toxicity in Lymphoma: A Novel Approach to Assessing Muscle Mass and Quality Based on Ultrasound Radiofrequency Signals

OBJECTIVES

The aim of this study was to use quantitative ultrasound (QUS) parameters to assess the muscle mass and quality in patients with lymphoma. Additionally, the study aimed to investigate the relationship between these QUS parameters and post-chemotherapy myelosuppression.

METHODS

The study cohort comprised 202 patients diagnosed with lymphoma (105 males, 97 females; mean age 57.0 ± 14.9 years). The skeletal muscle index (SMI) and mean skeletal muscle density (SMD) were measured on CT and used as the gold standards to evaluate low skeletal muscle mass and quality. The muscle thickness (MT) of the forearm flexor and extensor muscles was measured in both the relaxed and contracted states, while the normalized non-linear parameter B/A (MusQBOX.NLP) and normalized mean intensity (MusQBOX.NMI) were extracted from retained ultrasound radiofrequency signals. The correlations between the QUS parameters and grip strength were assessed. Models were constructed using these QUS parameters to predict low SMI and SMD, and to evaluate whether these factors were independently associated with post-chemotherapy myelosuppression.

RESULTS

The MT in both the relaxed and contracted states exhibited the strongest correlations with grip strength, while the MusQBOX.NLP and MusQBOX.NMI were only weakly correlated with grip strength. Models incorporating QUS parameters to predict low SMI and SMD achieved high area under the receiver operating characteristic curve values. The MT, MusQBOX.NLP, and MusQBOX.NMI were independent factors associated with post-chemotherapy myelosuppression.

CONCLUSIONS

QUS parameters show promise in characterizing muscle strength, mass, and quality. They are also independent factors influencing post-chemotherapy myelosuppression.

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

BACKGROUND AND OBJECTIVE

Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization.

METHODS

We theoretically analyzed the CRE for Rayleigh and Nakagami distributions and proposed a normalized CRE for characterizing scatterer distribution patterns. We proposed a method based on an empirical cumulative distribution function estimator and a trapezoidal numerical integration for estimating the normalized CRE from ultrasound backscatter envelope signals. We presented an ultrasound normalized CRE imaging scheme based on the normalized CRE estimator and the parallel computation technique. We also conducted theoretical analysis of the differential entropy which is an extension of the Shannon entropy to a continuous distribution, and introduced a method for ultrasound differential entropy estimation and imaging. Monte-Carlo simulation experiments were performed to evaluate the estimation accuracy of the normalized CRE and differential entropy estimators. Phantom simulation and clinical experiments were conducted to evaluate the performance of the proposed normalized CRE imaging in characterizing scatterer concentrations and hepatic steatosis (n = 204), respectively.

RESULTS

The theoretical normalized CRE for the Rayleigh distribution was π/4, corresponding to the case where there were ≥10 randomly distributed scatterers within the resolution cell of an ultrasound transducer. The theoretical normalized CRE for the Nakagami distribution decreased as the Nakagami parameter m increased, corresponding to that the ultrasound backscattered statistics varied from pre-Rayleigh to Rayleigh and to post-Rayleigh distributions. Monte-Carlo simulation experiments showed that the proposed normalized CRE and differential entropy estimators can produce a satisfying estimation accuracy even when the size of the test samples is small. Phantom simulation experiments showed that the proposed normalized CRE and differential entropy imaging can characterize scatterer concentrations. Clinical experiments showed that the proposed ultrasound normalized CRE imaging is capable to quantitatively characterize hepatic steatosis, outperforming ultrasound differential entropy imaging and being comparable to ultrasound Shannon entropy and Nakagami imaging.

CONCLUSION

This study sheds light on the theory and methodology of ultrasound normalized CRE. The proposed ultrasound normalized CRE can serve as a new, flexible quantitative ultrasound envelope statistics parameter. The proposed ultrasound normalized CRE imaging may find applications in quantified characterization of biological tissues. Our code will be made available publicly at https://github.com/zhouzhuhuang.

Quantitative ultrasound assessment of fatty infiltration of the rotator cuff muscles using backscatter coefficient

BACKGROUND

To prospectively evaluate ultrasound backscatter coefficients (BSCs) of the supraspinatus and infraspinatus muscles and compare with Goutallier classification on magnetic resonance imaging (MRI).

METHODS

Fifty-six participants had shoulder MRI exams and ultrasound exams of the supraspinatus and infraspinatus muscles. Goutallier MRI grades were determined and BSCs were measured. Group means were compared and the strength of relationships between the measures were determined. Using binarized Goutallier groups (0-2 versus 3-4), areas under the receiver operating characteristic curves (AUROCs) were calculated. The nearest integer cutoff value was determined using Youden's index.

RESULTS

BSC values were significantly different among most Goutallier grades for the supraspinatus and infraspinatus muscles (both p < 0.001). Strong correlations were found between the BSC values and Goutallier grades for the supraspinatus (τb = 0.72, p < 0.001) and infraspinatus (τb = 0.79, p < 0.001) muscles. BSC showed excellent performance for classification of the binarized groups (0-2 versus 3-4) for both supraspinatus (AUROC = 0.98, p < 0.0001) and infraspinatus (AUROC = 0.98, p < 0.0001) muscles. Using a cutoff BSC value of -17 dB, sensitivity, specificity, and accuracy for severe fatty infiltration were 87.0%, 90.0%, and 87.5% for the supraspinatus muscle, and 93.6%, 87.5%, and 92.7% for the infraspinatus muscle.

CONCLUSION

BSC can be applied to the rotator cuff muscles for assessment of fatty infiltration. For both the supraspinatus and infraspinatus muscles, BSC values significantly increased with higher Goutallier grades and showed strong performance in distinguishing low versus high Goutallier grades.

RELEVANCE STATEMENT

Fatty infiltration of the rotator cuff muscles can be quantified using BSC values, which are higher with increasing Goutallier grades.

KEY POINTS

Ultrasound BSC measurements are reliable for the quantification of muscle fatty infiltration. BCS values increased with higher Goutallier MRI grades. BCS values demonstrated high performance for distinguishing muscle fatty infiltration groups.

Enhancing ultrasonic attenuation images through multi-frequency coupling with total nuclear variation

Quantitative ultrasound is a non-invasive image modality that numerically characterizes tissues for medical diagnosis using acoustical parameters, such as the attenuation coefficient slope. A previous study introduced the total variation spectral log difference (TVSLD) method, which denoises spectral log ratios on a single-channel basis without inter-channel coupling. Therefore, this work proposes a multi-frequency joint framework by coupling information across frequency channels exploiting structural similarities among the spectral ratios to increase the quality of the attenuation images. A modification based on the total nuclear variation (TNV) was considered. Metrics were compared to the TVSLD method with simulated and experimental phantoms and two samples of fibroadenoma in vivo breast tissue. The TNV demonstrated superior performance, yielding enhanced attenuation coefficient slope maps with fewer artifacts at boundaries and a stable error. In terms of the contrast-to-noise ratio enhancement, the TNV approach obtained an average percentage improvement of 34% in simulation, 38% in the experimental phantom, and 89% in two in vivo breast tissue samples compared to TVSLD, showing potential to enhance visual clarity and depiction of attenuation images.

Ultrasound Characterization of Oral Soft Tissues in vivo Using the Burr Speckle Model

Periodontal (gum) diseases, reportedly affect 4 out of 10 adults 30 years of age or older in the USA. The standard of care for clinical assessments of these diseases is bleeding on probing, which is invasive, subjective and semi-qualitative. Thus, research on proposing alternative noninvasive modalities for clinical assessments of periodontal tissues is crucial. Quantitative Ultrasound (QUS) has shown promises in noninvasive assessments of various diseases in soft biological tissues; however, it has not been employed in periodontology. Here as the first step, we focused on QUS-based characterization of two very adjacent oral soft tissues of alveolar mucosa and attached gingiva in an in vivo animal study. We investigated first order ultrasonic speckle statistics using the two-parameter Burr model (power-law b and scale factor l). Our QUS analysis was compared with the Masson's Trichrome histology images of the two oral tissue types quantitatively using the RGB color thresholding. QUS study included 10 swine and US scanning was performed at the first and second molars of all four oral quadrants in each swine, resulting in 80 scans. US scan data was acquired at the transit/receive frequency of 24 MHz using a toothbrush-sized transducer. Parametric imaging of Burr parameters was created using a sliding kernel method with linear interpolations. The kernel size and overlap ratio was 10 wavelengths and 70%, respectively. No statistically significant difference was reported for estimated parameters when interpolation was performed (p-value>0.01). Results at both oral sites (molar 1 and molar 2) showed that the difference between the two tissue types using Burr parameters were statistically significant (p-value<0.0001). The average Burr b was reported to be higher in attached gingiva while the average Burr l was lower compared to mucosa. Visual comparison of Masson Trichrome histology images of these tissues showed denser color density in gingiva. The color thresholding of these images further confirmed that the percent of blue, which stains collagenous regions, was at least two times higher in gingiva than alveolar mucosa, based on threshold values. Comparing histology and QUS in characterizing the two tissue types, it was suggested that the elevated Burr b (related to potential scatterer densities) in gingiva could be aligned with findings from Masson's Trichrome histology. This study showed a promising potential of QUS for periodontal soft tissue characterization.

Changes in mechanical properties at the muscle level could be detected by Nakagami imaging during in-vivo fixed-end contractions

In this study, we investigated the capability of the Nakagami transformation to detect changes in vastus lateralis muscle-tendon stiffness (k) during dynamic (and intense) contractions. k was evaluated in eleven healthy males using the gold-standard method (a combination of ultrasound and dynamometric measurements) during maximal and sub-maximal voluntary fixed-end contractions of the knee extensors (20, 40, 60, 80, and 100% of maximum voluntary force), while Nakagami parameters were analysed using the Nakagami transformation during the same contractions. Muscle-belly behaviour was investigated by means of B-mode ultrasound analysis, while Nakagami parameters were obtained in post-processing using radiofrequency data. k was calculated as the slope of the force-muscle-belly elongation relationship. Three contractions at each intensity were performed to calculate the intra-trial reliability and the coefficient of variation (CV) of the Nakagami parameters. At all contraction intensities, high values of intra-trial reliability (range: 0.92-0.96) and low CV (<9%) were observed. k and Nakagami parameters increased as a function of contraction intensity, and significant positive correlations were observed between these variables. These data suggest that changes in mechanical properties (e.g., stiffness) at the muscle level could be investigated by means of Nakagami parameters.

Quantitative Ultrasound and Ultrasound-Based Elastography for Chronic Liver Disease: Practical Guidance, From the AJR Special Series on Quantitative Imaging

Quantitative ultrasound (QUS) and ultrasound-based elastography techniques are emerging as non-invasive effective methods for assessing chronic liver disease. They are more accurate than B-mode imaging alone and more accessible than MRI as alternatives to liver biopsy. Early detection and monitoring of diffuse liver processes such as steatosis, inflammation, and fibrosis play an important role in guiding patient management. The most widely available and validated techniques are attenuation-based QUS techniques and shear-wave elastography techniques that measure shear-wave speed. Other techniques are supported by a growing body of evidence and are increasingly commercialized. This review explains general physical concepts of QUS and ultrasound-based elastography techniques for evaluating chronic liver disease. The first section describes QUS techniques relying on attenuation, backscatter, and speed of sound. The second section discusses ultrasound-based elastography techniques analyzing shear-wave speed, shear-wave dispersion, and shear-wave attenuation. With an emphasis on clinical implementation, each technique's diagnostic performance along with thresholds for various clinical applications are summarized, to provide guidance on analysis and reporting for radiologists. Measurement methods, advantages, and limitations are also discussed. The third section explores developments in quantitative contrast-enhanced and vascular ultrasound that are relevant to chronic liver disease evaluation.

Ultrasound Imaging in Diagnosis and Management of Lower Limb Injuries: A Comprehensive Review

Medical imaging tests are widely used to diagnose a broad spectrum of lower-limb injuries. Among these modalities, ultrasound (US) imaging has gained significant traction as a valuable diagnostic instrument for assessing conditions primarily affecting muscles, tendons, ligaments, and other soft tissues. However, there are important dilemmas related to the indications and possibilities of US in lower-limb injuries. Conflicting findings and approaches raise questions regarding the validity, accuracy, and usefulness of the US in that area. This narrative review attempts to summarize the current state of knowledge regarding US imaging of lower-limb injuries. The study provides a detailed discussion of the existing literature and contemporary insights on the diagnosis of lower-limb injuries using US examination, and draws attention to the role of the US in interventional procedures and monitoring of the healing process. The characteristics of normal muscles, tendons, and ligaments in US imaging are presented, along with the most commonly documented conditions affecting these tissues. Furthermore, the benefits and justifications for employing US in interventional procedures are discussed, ranging from platelet-rich plasma injections to physiotherapeutic treatments like percutaneous electrolysis. The study was further augmented with US pictures depicting various lower-limb injuries, mainly affecting young athletes. This article aims to review the role of US imaging in the diagnosis and management of common lower-limb injuries.

Ultrasound-stimulated microbubbles to enhance radiotherapy: A scoping review

INTRODUCTION

Primarily used as ultrasound contrast agents, microbubbles have recently emerged as a versatile therapeutic vector that can be 'burst' to deliver payloads in the presence of suitably optimised ultrasound fields. Ultrasound-stimulated microbubbles (USMB) have recently demonstrated improvements in treatment outcomes across a variety of clinical applications. This scoping review investigates whether this potential translates into the context of radiation therapy by evaluating the application of this technology across all three phases of radiation action.

METHODS

Primary research articles, excluding poster presentations and conference proceedings, were identified through systematic searches of the PubMed NCBI/Medline, Embase/OVID, Web of Science and CINAHL/EBSCOhost databases, with additional articles identified via manual Google Scholar searching. Articles were dual screened for inclusion using the Covidence systematic review platform and classified against all three phases of radiation action.

RESULTS

Overall, 57 eligible publications from a total of 1389 identified articles were included in the review, with studies dating back to 2012. Study heterogeneity prevented formal statistical analysis; however, most articles reported improved outcomes using USMB in the presence of radiation compared to that of radiation alone. These improvements appear to result from the use of USMB as either a biovascular disruptor causing tumour cell damage via indirect mechanisms, or as a localised treatment vector that directly increases tumour cell uptake of other therapeutic and physical agents designed to enhance radiation action.

CONCLUSIONS

USMB demonstrate exciting potential to enhance the effects of radiation treatments due to their versatility and capacity to target all three phases of radiation action.

2D ultrasound thermometry during thermal ablation with high-intensity focused ultrasound

The clinical use of high intensity focused ultrasound (HIFU) therapy for noninvasive tissue ablation has recently gained momentum. Guidance is provided by either magnetic resonance imaging (MRI) or conventional B-mode ultrasound imaging, each with its own advantages and disadvantages. The main limitation of ultrasound imaging is its inability to provide temperature measurements over the ranges corresponding to the target temperatures during ablative thermal therapies (between 55 °C and 70 °C). Here, variations in ultrasound backscattered energy (ΔBSE) were used to monitor temperature increases in liver tissue up to an absolute value of 90 °C during and after HIFU treatment. In vitro experimental measurements were performed in 47 bovine liver samples using a toroidal HIFU transducer operating at 2.5 MHz to increase the temperature of tissues. An ultrasound imaging probe working at 7.5 MHz was placed in the center of the HIFU transducer to monitor the backscattered signals. The free-field acoustic power was set to 9 W, 12 W or 16 W in the different experiments. HIFU sonications were performed for 240 s using a duty cycle of 83 % to allow ultrasound imaging and raw radiofrequency data acquisition during exposures. Measurements showed a linear relationship between ΔBSE (in dB) and temperature (r = 0.94, p < 0.001) over a temperature range from 37 °C to 90 °C, with a high reliability of temperature measurements below 75 °C. Monitoring can be performed at the frame rate of ultrasound imaging scanners with an accuracy within an acceptable threshold of 5 °C, given the temperatures targeted during thermal ablations. If the maximum temperature reached is below 70 °C, ΔBSE is also a reliable approach for estimating the temperature during cooling. Histological analysis shown the impact of the treatment on the spatial arrangement of cells that can explain the observed variation of ΔBSE. These results demonstrate the ability of ΔBSE measurements to estimate temperature in ultrasound images within an effective therapeutic range. This method can be implemented clinically and potentially applied to other thermal-based therapies.

Ultrasound wavelet spectra enable direct tissue recognition and full-color visualization

Traditional brightness-mode ultrasound imaging is primarily constrained by the low specificity among tissues and the inconsistency among sonographers. The major cause is the imaging method that represents the amplitude of echoes as brightness and ignores other detailed information, leaving sonographers to interpret based on organ contours that depend highly on specific imaging planes. Other ultrasound imaging modalities, color Doppler imaging or shear wave elastography, overlay motion or stiffness information to brightness-mode images. However, tissue-specific scattering properties and spectral patterns remain unknown in ultrasound imaging. Here we demonstrate that the distribution (size and average distance) of scattering particles leads to characteristic wavelet spectral patterns, which enables tissue recognition and high-contrast ultrasound imaging. Ultrasonic wavelet spectra from similar particle distributions tend to cluster in the eigenspace according to principal component analysis, whereas those with different distributions tend to be distinguishable from one another. For each distribution, a few wavelet spectra are unique and act as a fingerprint to recognize the corresponding tissue. Illumination of specific tissues and organs with designated colors according to the recognition results yields high-contrast ultrasound imaging. The fully-colorized tissue-specific ultrasound imaging potentially simplifies the interpretation and promotes consistency among sonographers, or even enables the applicability for non-professionals.

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log10(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60-80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log10(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.

A Dynamic Ultrasound Phantom with Tissue-Mimicking Mechanical and Acoustic Properties

Tissue-mimicking phantoms are valuable tools that aid in improving the equipment and training available to medical professionals. However, current phantoms possess limited utility due to their inability to precisely simulate multiple physical properties simultaneously, which is crucial for achieving a system understanding of dynamic human tissues. In this work, novel materials design and fabrication processes to produce various tissue-mimicking materials (TMMs) for skin, adipose, muscle, and soft tissue at a human scale are developed. Target properties (Young's modulus, density, speed of sound, and acoustic attenuation) are first defined for each TMM based on literature. Each TMM recipe is developed, associated mechanical and acoustic properties are characterized, and the TMMs are confirmed to have comparable mechanical and acoustic properties with the corresponding human tissues. Furthermore, a novel sacrificial core to fabricate a hollow, ellipsoid-shaped bladder phantom complete with inlet and outlet tubes, which allow liquids to flow through and expand this phantom, is adopted. This dynamic bladder phantom with realistic mechanical and acoustic properties to human tissues in combination with the developed skin, soft tissue, and subcutaneous adipose tissue TMMs, culminates in a human scale torso tank and electro-mechanical system that can be systematically utilized for characterizing various medical imaging devices.

DAFT-Net: Dual Attention and Fast Tongue Contour Extraction Using Enhanced U-Net Architecture

In most silent speech research, continuously observing tongue movements is crucial, thus requiring the use of ultrasound to extract tongue contours. Precisely and in real-time extracting ultrasonic tongue contours presents a major challenge. To tackle this challenge, the novel end-to-end lightweight network DAFT-Net is introduced for ultrasonic tongue contour extraction. Integrating the Convolutional Block Attention Module (CBAM) and Attention Gate (AG) module with entropy-based optimization strategies, DAFT-Net establishes a comprehensive attention mechanism with dual functionality. This innovative approach enhances feature representation by replacing traditional skip connection architecture, thus leveraging entropy and information-theoretic measures to ensure efficient and precise feature selection. Additionally, the U-Net's encoder and decoder layers have been streamlined to reduce computational demands. This process is further supported by information theory, thus guiding the reduction without compromising the network's ability to capture and utilize critical information. Ablation studies confirm the efficacy of the integrated attention module and its components. The comparative analysis of the NS, TGU, and TIMIT datasets shows that DAFT-Net efficiently extracts relevant features, and it significantly reduces extraction time. These findings demonstrate the practical advantages of applying entropy and information theory principles. This approach improves the performance of medical image segmentation networks, thus paving the way for real-world applications.

Toward confident prostate cancer detection using ultrasound: a multi-center study

PURPOSE

Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue.

METHODS

Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods.

RESULTS

PCa detection models achieve performance scores up to 76 % average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as 2 % , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue.

CONCLUSION

Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

Simultaneous photoacoustic and ultrasound imaging: A review

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that combines the advantages of optical and ultrasound imaging, enabling the generation of images with both optical resolution and acoustic penetration depth. By leveraging similar signal acquisition and processing methods, the integration of photoacoustic and ultrasound imaging has introduced a novel hybrid imaging modality suitable for clinical applications. Photoacoustic-ultrasound imaging allows for non-invasive, high-resolution, and deep-penetrating imaging, providing a wealth of image information. In recent years, with the deepening research and the expanding biomedical application scenarios of photoacoustic-ultrasound bimodal systems, the immense potential of photoacoustic-ultrasound bimodal imaging in basic research and clinical applications has been demonstrated, with some research achievements already commercialized. In this review, we introduce the principles, technical advantages, and biomedical applications of photoacoustic-ultrasound bimodal imaging techniques, specifically focusing on tomographic, microscopic, and endoscopic imaging modalities. Furthermore, we discuss the future directions of photoacoustic-ultrasound bimodal imaging technology.

Characterization of the in vivo transient responses of the femoral cartilage by means of quantitative ultrasound imaging techniques

BACKGROUND

Quantitative ultrasound (QUS) techniques are new diagnostic tools able to identify changes in structural and material properties of the investigated tissue. For the first time, we evaluated the capability of QUS techniques in determining the in vivo transient changes in knee joint cartilage after a stressful task.

METHODS

An ultrasound scanner collecting B-mode and radiofrequency data simultaneously was used to collect data from the femoral cartilage of the right knee in 15 participants. Cartilage thickness (CTK), ultrasound roughness index (URI), average magnitude ratio (AMR), and Nakagami parameters (NA) were evaluated before, immediately after and every 5 min up to 45 min a stressful task (30 min of running on a treadmill with a negative slope of 5%).

RESULTS

CTK was affected by time (main effect: p < 0.001). Post hoc test showed significant differences with CTK at rest, which were observed up to 30 min after the run. AMR and NA were affected by time (p < 0.01 for both variables), while URI was unaffected by it. For AMR, post hoc test showed significant differences with rest values in the first 35 min of recovery, while NA was increased compared to rest values in all time points.

CONCLUSION

Data suggest that a single running trial is not able to modify the integrity of the femoral cartilage, as reported by URI data. In vivo evaluation of QUS parameters of the femoral cartilage (NA, AMR, and URI) are able to characterize changes in cartilage properties over time.

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

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

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.

Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD.

METHODS

Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC).

RESULTS

Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 (p < 0.001 compared with conventional radiomics analysis).

CONCLUSIONS

The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.

Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy

Locally advanced breast cancer (LABC) is a severe type of cancer with a poor prognosis, despite advancements in therapy. As the disease is often inoperable, current guidelines suggest upfront aggressive neoadjuvant chemotherapy (NAC). Complete pathological response to chemotherapy is linked to improved survival, but conventional clinical assessments like physical exams, mammography, and imaging are limited in detecting early response. Early detection of tissue response can improve complete pathological response and patient survival while reducing exposure to ineffective and potentially harmful treatments. A rapid, cost-effective modality without the need for exogenous contrast agents would be valuable for evaluating neoadjuvant therapy response. Conventional ultrasound provides information about tissue echogenicity, but image comparisons are difficult due to instrument-dependent settings and imaging parameters. Quantitative ultrasound (QUS) overcomes this by using normalized power spectra to calculate quantitative metrics. This study used a novel transfer learning-based approach to predict LABC response to neoadjuvant chemotherapy using QUS imaging at pre-treatment. Using data from 174 patients, QUS parametric images of breast tumors with margins were generated. The ground truth response to therapy for each patient was based on standard clinical and pathological criteria. The Residual Network (ResNet) deep learning architecture was used to extract features from the parametric QUS maps. This was followed by SelectKBest and Synthetic Minority Oversampling (SMOTE) techniques for feature selection and data balancing, respectively. The Support Vector Machine (SVM) algorithm was employed to classify patients into two distinct categories: nonresponders (NR) and responders (RR). Evaluation results on an unseen test set demonstrate that the transfer learning-based approach using spectral slope parametric maps had the best performance in the identification of nonresponders with precision, recall, F1-score, and balanced accuracy of 100, 71, 83, and 86%, respectively. The transfer learning-based approach has many advantages over conventional deep learning methods since it reduces the need for large image datasets for training and shortens the training time. The results of this study demonstrate the potential of transfer learning in predicting LABC response to neoadjuvant chemotherapy before the start of treatment using quantitative ultrasound imaging. Prediction of NAC response before treatment can aid clinicians in customizing ineffectual treatment regimens for individual patients.

In Situ Monitoring and Assessment of Ischemic Skin Flap by High-Frequency Ultrasound and Quantitative Parameters

Skin flap surgery is a critical procedure for treating severe skin injury in which post-surgery lesions must well monitored and cared for noninvasively. In the present study, attempts using high-frequency ultrasound imaging, quantitative parameters, and statistical analysis were made to extensively assess variations in the skin flap. Experiments were arranged by incising the dorsal skin of rats to create a skin flap using the chamber model. Measurements, including photographs, 30 MHz ultrasound B-mode images, skin thickness, echogenicity, Nakagami statistics, and histological analysis of post-surgery skin flap, were performed. Photograph results showed that color variations in different parts of the skin flap may readily correspond to ischemic states of local tissues. Compared to post-surgery skin flap on day 7, both integrated backscatter (IB) and Nakagami parameter (m) of the distal part of tissues were increased, and those of the skin thickness were decreased. Overall, relative skin thickness, IB, and m of the distal part of post-surgery skin flap varied from 100 to 67%, -66 to -61 dB, and 0.48 to 0.36, respectively. These results demonstrate that this modality and quantitative parameters can be feasibly applied for long-term and in situ assessment of skin flap tissues.

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.

Extracellular Matrix Cues Regulate Mechanosensing and Mechanotransduction of Cancer Cells

Extracellular biophysical properties have particular implications for a wide spectrum of cellular behaviors and functions, including growth, motility, differentiation, apoptosis, gene expression, cell-matrix and cell-cell adhesion, and signal transduction including mechanotransduction. Cells not only react to unambiguously mechanical cues from the extracellular matrix (ECM), but can occasionally manipulate the mechanical features of the matrix in parallel with biological characteristics, thus interfering with downstream matrix-based cues in both physiological and pathological processes. Bidirectional interactions between cells and (bio)materials in vitro can alter cell phenotype and mechanotransduction, as well as ECM structure, intentionally or unintentionally. Interactions between cell and matrix mechanics in vivo are of particular importance in a variety of diseases, including primarily cancer. Stiffness values between normal and cancerous tissue can range between 500 Pa (soft) and 48 kPa (stiff), respectively. Even the shear flow can increase from 0.1-1 dyn/cm2 (normal tissue) to 1-10 dyn/cm2 (cancerous tissue). There are currently many new areas of activity in tumor research on various biological length scales, which are highlighted in this review. Moreover, the complexity of interactions between ECM and cancer cells is reduced to common features of different tumors and the characteristics are highlighted to identify the main pathways of interaction. This all contributes to the standardization of mechanotransduction models and approaches, which, ultimately, increases the understanding of the complex interaction. Finally, both the in vitro and in vivo effects of this mechanics-biology pairing have key insights and implications for clinical practice in tumor treatment and, consequently, clinical translation.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.

Ultrasound tissue scatterer distribution imaging: An adjunctive diagnostic tool for shear wave elastography in characterizing focal liver lesions

OBJECTIVES

Focal liver lesion (FLL) is a prevalent finding in cross-sectional imaging, and distinguishing between benign and malignant FLLs is crucial for liver health management. While shear wave elastography (SWE) serves as a conventional quantitative ultrasound tool for evaluating FLLs, ultrasound tissue scatterer distribution imaging (TSI) emerges as a novel technique, employing the Nakagami statistical distribution parameter to estimate backscattered statistics for tissue characterization. In this prospective study, we explored the potential of TSI in characterizing FLLs and evaluated its diagnostic efficacy with that of SWE.

METHODS

A total of 235 participants (265 FLLs; the study group) were enrolled to undergo abdominal examinations, which included data acquisition from B-mode, SWE, and raw radiofrequency data for TSI construction. The area under the receiver operating characteristic curve (AUROC) was used to evaluate performance. A dataset of 20 patients (20 FLLs; the validation group) was additionally acquired to further evaluate the efficacy of the TSI cutoff value in FLL characterization.

RESULTS

In the study group, our findings revealed that while SWE achieved a success rate of 49.43 % in FLL measurements, TSI boasted a success rate of 100 %. In cases where SWE was effectively implemented, the AUROCs for characterizing FLLs using SWE and TSI stood at 0.84 and 0.83, respectively. For instances where SWE imaging failed, TSI achieved an AUROC of 0.78. Considering all cases, TSI presented an overall AUROC of 0.81. There was no statistically significant difference in AUROC values between TSI and SWE (p > 0.05). In the validation group, using a TSI cutoff value of 0.67, the AUROC for characterizing FLLs was 0.80.

CONCLUSIONS

In conclusion, ultrasound TSI holds promise as a supplementary diagnostic tool to SWE for characterizing FLLs.

Hermite-scan imaging for differentiating glioblastoma from normal brain: Simulations and ex vivo studies for applications in intra-operative tumor identificationa)

Hermite-scan (H-scan) imaging is a tissue characterization technique based on the analysis of raw ultrasound radio frequency (RF) echoes. It matches the RF echoes to Gaussian-weighted Hermite polynomials of various orders to extract information related to scatterer diameter. It provides a color map of large and small scatterers in the red and blue H-scan image channels, respectively. H-scan has been previously reported for characterizing breast, pancreatic, and thyroid tumors. The present work evaluated H-scan imaging to differentiate glioblastoma tumors from normal brain tissue ex vivo. First, we conducted 2-D numerical simulations using the k-wave toolbox to assess the performance of parameters derived from H-scan images of acoustic scatterers (15-150 μm diameters) and concentrations (0.2%-1% w/v). We found that the parameter intensity-weighted percentage of red (IWPR) was sensitive to changes in scatterer diameters independent of concentration. Next, we assessed the feasibility of using the IWPR parameter for differentiating glioblastoma and normal brain tissues (n = 11 samples per group). The IWPR parameter estimates for normal tissue (44.1% ± 1.4%) were significantly different (p < 0.0001) from those for glioblastoma (36.2% ± 0.65%). These findings advance the development of H-scan imaging for potential use in differentiating glioblastoma tumors from normal brain tissue during resection surgery.

Quantitative ultrasound envelope statistics imaging as a screening approach for pediatric hepatic steatosis and liver fibrosis: using biomarker and transient elastography as reference standards

Quantitative ultrasound (QUS) envelope statistics imaging is an emerging technique for the assessment of hepatic steatosis in adults. Blood tests are currently recommended as the screening tool for pediatric hepatic steatosis, a condition that can lead to liver fibrosis in children. This study examined the utility of QUS envelope statistics imaging in grading biomarker-diagnosed hepatic steatosis and detecting liver fibrosis in a pediatric population. A total of 173 subjects was enrolled (Group A) for QUS envelope statistics imaging using two statistical distributions, Nakagami and homodyned K (HK) models, and information entropy. QUS parameter values were compared with the hepatic steatosis index (HSI) and steatosis grade (G0: HSI <30; G1: 30 ≤ HSI <36; G2: 36 ≤ HSI <41.6; G3: ≥41.6). An additional cohort of 63 subjects (Group B) was recruited to undergo both QUS envelope statistics imaging and liver stiffness measurements (LSM) obtained from the transient elastography (Fibroscan), with a cutoff value set at 5 kPa to indicate liver fibrosis. The diagnostic performances were evaluated using the area under the receiver operating characteristic curve (AUROC). QUS envelope statistics imaging generated the AUROC values for steatosis grading at levels ≥ G1, ≥ G2, and ≥ G3 ranged from 0.94 to 0.97, 0.91 to 0.93, and 0.83 to 0.87, respectively, and produced an AUROC range of between 0.82 and 0.84 for identifying liver fibrosis. QUS envelope statistics imaging integrates the benefits of both biomarkers and elastography, enabling the screening of hepatic steatosis and detection of liver fibrosis in a pediatric population.

The Future Is Beyond Bright: The Evolving Role of Quantitative US for Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a common cause of morbidity and mortality. Nonfocal liver biopsy is the historical reference standard for evaluating NAFLD, but it is limited by invasiveness, high cost, and sampling error. Imaging methods are ideally situated to provide quantifiable results and rule out other anatomic diseases of the liver. MRI and US have shown great promise for the noninvasive evaluation of NAFLD. US is particularly well suited to address the population-level problem of NAFLD because it is lower-cost, more available, and more tolerable to a broader range of patients than MRI. Noninvasive US methods to evaluate liver fibrosis are widely available, and US-based tools to evaluate steatosis and inflammation are gaining traction. US techniques including shear-wave elastography, Doppler spectral imaging, attenuation coefficient, hepatorenal index, speed of sound, and backscatter-based estimation have regulatory clearance and are in clinical use. New methods based on channel and radiofrequency data analysis approaches have shown promise but are mostly experimental. This review discusses the advantages and limitations of clinically available and experimental approaches to sonographic liver tissue characterization for NAFLD diagnosis as well as future applications and strategies to overcome current limitations.

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%.

Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis

Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.

Emerging Quantitative Imaging Techniques in Sports Medicine

This article describes recent advances in quantitative imaging of musculoskeletal extremity sports injuries, citing the existing literature evidence and what additional evidence is needed to make such techniques applicable to clinical practice. Compositional and functional MRI techniques including T2 mapping, diffusion tensor imaging, and sodium imaging as well as contrast-enhanced US have been applied to quantify pathophysiologic processes and biochemical compositions of muscles, tendons, ligaments, and cartilage. Dual-energy and/or spectral CT has shown potential, particularly for the evaluation of osseous and ligamentous injury (eg, creation of quantitative bone marrow edema maps), which is not possible with standard single-energy CT. Recent advances in US technology such as shear-wave elastography or US tissue characterization as well as MR elastography enable the quantification of mechanical, elastic, and physical properties of tissues in muscle and tendon injuries. The future role of novel imaging techniques such as photon-counting CT remains to be established. Eventual prediction of return to play (ie, the time needed for the injury to heal sufficiently so that the athlete can get back to playing their sport) and estimation of risk of repeat injury is desirable to help guide sports physicians in the treatment of their patients. Additional values of quantitative analyses, as opposed to routine qualitative analyses, still must be established using prospective longitudinal studies with larger sample sizes.

Understanding a mass in the paraspinal region: an anatomical approach

The paraspinal region encompasses all tissues around the spine. The regional anatomy is complex and includes the paraspinal muscles, spinal nerves, sympathetic chains, Batson's venous plexus and a rich arterial network. A wide variety of pathologies can occur in the paraspinal region, originating either from paraspinal soft tissues or the vertebral column. The most common paraspinal benign neoplasms include lipomas, fibroblastic tumours and benign peripheral nerve sheath tumours. Tumour-like masses such as haematomas, extramedullary haematopoiesis or abscesses should be considered in patients with suggestive medical histories. Malignant neoplasms are less frequent than benign processes and include liposarcomas and undifferentiated sarcomas. Secondary and primary spinal tumours may present as midline expansile soft tissue masses invading the adjacent paraspinal region. Knowledge of the anatomy of the paraspinal region is of major importance since it allows understanding of the complex locoregional tumour spread that can occur via many adipose corridors, haematogenous pathways and direct contact. Paraspinal tumours can extend into other anatomical regions, such as the retroperitoneum, pleura, posterior mediastinum, intercostal space or extradural neural axis compartment. Imaging plays a crucial role in formulating a hypothesis regarding the aetiology of the mass and tumour staging, which informs preoperative planning. Understanding the complex relationship between the different elements and the imaging features of common paraspinal masses is fundamental to achieving a correct diagnosis and adequate patient management. This review gives an overview of the anatomy of the paraspinal region and describes imaging features of the main tumours and tumour-like lesions that occur in the region.

Association of Backscattered Ultrasonographic Imaging of the Tongue With Severity of Obstructive Sleep Apnea in Adults

Importance

Determining interventions to manage obstructive sleep apnea (OSA) depends on clinical examination, polysomnography (PSG) results, and imaging analysis. There remains the need of a noninvasive and cost-effective way to correlate relevant upper airway anatomy with severity of OSA to direct treatment and optimize outcome.

Objective

To determine whether backscattered ultrasonographic imaging (BUI) analysis of the tongue is associated with severity of OSA in adults.

Design, Setting, and Participants

In this prospective, single-center, diagnostic study of a consecutive series of patients (aged ≥18 years) at a sleep surgery clinic, the 89 included patients had a PSG within 3 years at the time of ultrasonography and BUI analysis between July 2020 and March 2022. Patients were excluded if body mass index had changed more than 10% since time of PSG. A standardized submental ultrasonographic scan with laser alignment was used with B-mode and BUI analysis applied to the tongue. The B-mode and BUI intensity were associated with the apnea-hypopnea index (AHI), a measure of severity of apnea from normal (no OSA) to severe OSA.

Exposures

Ultrasonography and PSG.

Main Outcomes and Measures

The main outcomes were BUI parameters and their association with AHI value.

Results

Eighty-nine patients were included between July 2020 and March 2022. A total of 70 (78.7%) male patients were included; and distribution by race and ethnicity was 46 (52%) White participants, 22 (25%) Asian participants, and 2 (2%) African American participants, and 19 (21%) others. Median (IQR) age was 37.0 (29.0-48.3) years; median (IQR) BMI was 25.3 (23.2-29.8); and median (IQR) AHI was 11.1 (5.6-23.1) events per hour. At the middle to posterior tongue region, the 4 OSA severity levels explained a significant portion of the BUI variance (η2 = 0.153-0.236), and a significant difference in BUI values was found between the subgroups with AHI values of less than 15 (no OSA and mild OSA) and greater than or equal to 15 (moderate OSA and severe OSA) events per hour. The echo intensity showed no significant differences. The BUI values showed a positive association with AHI, with a Spearman correlation coefficient of up to 0.43. Higher BUI values remained associated with higher AHI after correction for the covariates of BMI and age.

Conclusions and Relevance

In this prospective diagnostic study, standardized BUI analysis of the tongue was associated with OSA severity. With the practicality of ultrasonography, this analysis is pivotal in connecting anatomy with physiology in treatment planning for patients with OSA.

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.

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

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

Simultaneous imaging of ultrasonic relative backscatter and attenuation coefficients for quantitative liver steatosis assessment

Prevalence of liver disease is continuously increasing and nonalcoholic fatty liver disease (NAFLD) is the most common etiology. We present an approach to detect the progression of liver steatosis based on quantitative ultrasound (QUS) imaging. This study was performed on a group of 55 rats that were subjected to a control or methionine and choline deficient (MCD) diet known to induce NAFLD. Ultrasound (US) measurements were performed at 2 and 6 weeks. Thereafter, animals were humanely euthanized and livers excised for histological analysis. Relative backscatter and attenuation coefficients were simultaneously estimated from the US data and envelope signal-to-noise ratio was calculated to train a regression model for: (1) fat fraction percentage estimation and (2) performing classification according to Brunt's criteria in grades (0 <5%; 1, 5-33%; 2, >33-66%; 3, >66%) of liver steatosis. The trained regression model achieved an [Formula: see text] of 0.97 (p-value < 0.01) and a RMSE of 3.64. Moreover, the classification task reached an accuracy of 94.55%. Our results suggest that in vivo QUS is a promising noninvasive imaging modality for the early assessment of NAFLD.

Skeletal Muscle Assessment Using Quantitative Ultrasound: A Narrative Review

Ultrasound (US) is an important imaging tool for skeletal muscle analysis. The advantages of US include point-of-care access, real-time imaging, cost-effectiveness, and absence of ionizing radiation. However, US can be highly dependent on the operator and/or US system, and a portion of the potentially useful information carried by raw sonographic data is discarded in image formation for routine qualitative US. Quantitative ultrasound (QUS) methods provide analysis of the raw or post-processed data, revealing additional information about normal tissue structure and disease status. There are four QUS categories that can be used on muscle and are important to review. First, quantitative data derived from B-mode images can help determine the macrostructural anatomy and microstructural morphology of muscle tissues. Second, US elastography can provide information about muscle elasticity or stiffness through strain elastography or shear wave elastography (SWE). Strain elastography measures the induced tissue strain caused either by internal or external compression by tracking tissue displacement with detectable speckle in B-mode images of the examined tissue. SWE measures the speed of induced shear waves traveling through the tissue to estimate the tissue elasticity. These shear waves may be produced using external mechanical vibrations or internal "push pulse" ultrasound stimuli. Third, raw radiofrequency signal analyses provide estimates of fundamental tissue parameters, such as the speed of sound, attenuation coefficient, and backscatter coefficient, which correspond to information about muscle tissue microstructure and composition. Lastly, envelope statistical analyses apply various probability distributions to estimate the number density of scatterers and quantify coherent to incoherent signals, thus providing information about microstructural properties of muscle tissue. This review will examine these QUS techniques, published results on QUS evaluation of skeletal muscles, and the strengths and limitations of QUS in skeletal muscle analysis.

The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions

OBJECTIVE

Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims.

METHODS

B-mode images from n = 821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed.

DISCUSSION

The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11, p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08, p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation.

CONCLUSION

We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.

Acoustic evaluation of photobiomodulation effect on in vitro human blood samples

Although positive photobiomodulation response on wound healing, tissue repair, and therapeutic treatment has been widely reported, additional works are still needed to understand its effects on human blood. This research carried out acoustic measurements using A-scan (GAMPT) ultrasonic techniques to elucidate the photobiomodulation effects on in vitro human blood samples as therapeutic treatment measures. The human blood samples were irradiated using a 532-nm laser with different output laser powers (60 and 80 mW) at various exposure times. The ultrasonic velocity measured in the human blood samples after laser irradiation showed significant changes, most of which were within the acceptance limit for soft tissues (1570 [Formula: see text] 30 m/s). Abnormal cells (echinocyte and crenation) were observed due to excessive exposure during laser treatment.

Improving diagnostic accuracy of ultrasound texture features in detecting and quantifying hepatic steatosis using various beamforming sound speeds

Objective.While ultrasound image texture has been utilized to detect and quantify hepatic steatosis, the texture features extracted using a single (conventionally 1540 m s-1) beamforming speed of sound (SoS) failed to achieve reliable diagnostic performance. This study aimed to investigate if the texture features extracted using various beamforming SoSs can improve the accuracy of hepatic steatosis detection and quantification.Approach.Patients with suspected non-alcoholic fatty liver disease underwent liver biopsy or MRI proton density fat fraction (PDFF) as part of standard of care, were prospectively enrolled. The radio-frequency data from subjects' right and left liver lobes were collected using 6 beamforming SoSs: 1300, 1350, 1400, 1450, 1500 and 1540 m s-1and analyzed offline. The texture features, i.e. Contrast, Correlation, Energy and Homogeneity from gray-level co-occurrence matrix of normalized envelope were obtained from a region of interest in the liver parenchyma.Main results.Forty-three subjects (67.2%) were diagnosed with steatosis while 21 had no steatosis. Homogeneity showed the area under the curve (AUC) of 0.75-0.82 and 0.58-0.81 for left and right lobes, respectively with varying beamforming SoSs. The combined Homogeneity value over 1300-1540 m s-1from left and right lobes showed the AUC of 0.90 and 0.81, respectively. Furthermore, the combined Homogeneity values from left and right lobes over 1300-1540 m s-1improved the AUC to 0.94. The correlation between texture features and steatosis severity was improved by using the images from various beamforming SoSs. The combined Contrast values over 1300-1540 m s-1from left and right lobes demonstrated the highest correlation (r= 0.90) with the MRI PDFF while the combined Homogeneity values over 1300-1540 m s-1from left and right lobes showed the highest correlation with the biopsy grades (r= -0.81).Significance.The diagnostic accuracy of ultrasound texture features in detecting and quantifying hepatic steatosis was improved by combining its values extracted using various beamforming SoSs.

The Past, Present, and Future of Noninvasive Test in Chronic Liver Diseases

Chronic liver disease is a major global health threat and is the 11th leading cause of death globally. A liver biopsy is frequently required in assessing the degree of steatosis and fibrosis, information that is important in diagnosis, management, and prognostication. However, liver biopsies have limitations and carry a considerable risk, leading to the development of various modalities of noninvasive testing tools. These tools have been developed in recent years and have improved markedly in diagnostic accuracy. Moving forward, they may change the practice of hepatology.

Information Entropy and Its Applications

Ultrasound is a first-line diagnostic tool for imaging many disease states. A number of statistical distributions have been proposed to describe ultrasound backscattering measured from tissues having different disease states. As an example, in this chapter we use nonalcoholic fatty liver disease (NAFLD), which is a critical health issue on a global scale, to demonstrate the capabilities of ultrasound to diagnose disease. Ultrasound interaction with the liver is typically characterized by scattering, which is quantified for the purpose of determining the degree of liver steatosis and fibrosis. Information entropy provides an insight into signal uncertainty. This concept allows for the analysis of backscattered statistics without considering the distribution of data or the statistical properties of ultrasound signals. In this chapter, we examined the background of NAFLD and the sources of scattering in the liver. The fundamentals of information entropy and an algorithmic scheme for ultrasound entropy imaging are then presented. Lastly, some examples of using ultrasound entropy imaging to grade hepatic steatosis and evaluate the risk of liver fibrosis in patients with significant hepatic steatosis are presented to illustrate future opportunities for clinical use.

US Backscatter for Liver Fat Quantification: An AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative

Nonalcoholic fatty liver disease (NAFLD) is believed to affect one-third of American adults. Noninvasive methods that enable detection and monitoring of NAFLD have the potential for great public health benefits. Because of its low cost, portability, and noninvasiveness, US is an attractive alternative to both biopsy and MRI in the assessment of liver steatosis. NAFLD is qualitatively associated with enhanced B-mode US echogenicity, but visual measures of B-mode echogenicity are negatively affected by interobserver variability. Alternatively, quantitative backscatter parameters, including the hepatorenal index and backscatter coefficient, are being investigated with the goal of improving US-based characterization of NAFLD. The American Institute of Ultrasound in Medicine and Radiological Society of North America Quantitative Imaging Biomarkers Alliance are working to standardize US acquisition protocols and data analysis methods to improve the diagnostic performance of the backscatter coefficient in liver fat assessment. This review article explains the science and clinical evidence underlying backscatter for liver fat assessment. Recommendations for data collection are discussed, with the aim of minimizing potential confounding effects associated with technical and biologic variables.

Regularized Ultrasound Phantom-Free Local Attenuation Coefficient Slope (ACS) Imaging in Homogeneous and Heterogeneous Tissues

Attenuation maps or measurements based on the local attenuation coefficient slope (ACS) in quantitative ultrasound (QUS) have shown potential for the diagnosis of liver steatosis. In liver cancers, tissue abnormalities and tumors detected using ACS are also of interest to provide new image contrast to clinicians. Current phantom-based approaches have the limitation of assuming a comparable speed of sound between the reference phantom and insonified tissues. Moreover, these methods present the inconvenience for operators to acquire data on phantoms and patients. The main goal was to alleviate these drawbacks by proposing a methodology for constructing phantom-free regularized (PF-R) local ACS maps and investigate the performance in both homogeneous and heterogeneous media. The proposed method was tested on two tissue-mimicking media with different ACS constructed as homogeneous phantoms, side-by-side and top-to-bottom phantoms, and inclusion phantoms with different attenuations. Moreover, an in vivo proof-of-concept was performed on healthy, steatotic, and cancerous human liver datasets. Modifications brought to previous works include: 1) a linear interpolation of the power spectrum in the log scale; 2) the relaxation of the underlying hypothesis on the diffraction factor; 3) a generalization to nonhomogeneous local ACS; and 4) an adaptive restriction of frequencies to a more reliable range than the usable frequency range. Regularization was formulated as a generalized least absolute shrinkage and selection operator (LASSO), and a variant of the Bayesian information criterion (BIC) was applied to estimate the Lagrangian multiplier on the LASSO constraint. In addition, we evaluated the proposed algorithm when applying median filtering before and after regularization. Tests conducted showed that the PF-R yielded robust results in all tested conditions, suggesting potential for additional validation as a diagnosis method.

Assessment of breast cancer response to neoadjuvant chemotherapy based on ultrasound backscattering envelope statistics

PURPOSE

Neoadjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment.

METHODS

The method assumes that ultrasound scattering is different for responsive and non-responsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov-Smirnov statistics (KSS) and the symmetrical Kullback-Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells (RMC) was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC).

RESULTS

For responding tumors a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In non-responding tumors the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not responding to NAC after the first dose of the drug, with AUC equal 0.83±0.06 and 0.84±0.07 respectively. After the third dose, the AUC increased to 0.90±0.05 and 0.91±0.04 respectively.

CONCLUSIONS

The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of non-responding cases. This article is protected by copyright. All rights reserved.