Inter-platform reproducibility of ultrasonic attenuation and backscatter coefficients in assessing NAFLD

Altered probe pressure and body position increase diagnostic accuracy for men and women in detecting hepatic steatosis using quantitative ultrasound

OBJECTIVES

To evaluate the diagnostic performance of ultrasound guided attenuation parameter (UGAP) for evaluating liver fat content with different probe forces and body positions, in relation to sex, and compared with proton density fat fraction (PDFF).

METHODS

We prospectively enrolled a metabolic dysfunction-associated steatotic liver disease (MASLD) cohort that underwent UGAP and PDFF in the autumn of 2022. Mean UGAP values were obtained in supine and 30° left decubitus body position with normal 4 N and increased 30 N probe force. The diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC).

RESULTS

Among 60 individuals (mean age 52.9 years, SD 12.9; 30 men), we found the best diagnostic performance with increased probe force in 30° left decubitus position (AUC 0.90; 95% CI 0.82-0.98) with a cut-off of 0.58 dB/cm/MHz. For men, the best performance was in supine (AUC 0.91; 95% CI 0.81-1.00) with a cut-off of 0.60 dB/cm/MHz, and for women, 30° left decubitus position (AUC 0.93; 95% CI 0.83-1.00), with a cut-off 0.56 dB/cm/MHz, and increased 30 N probe force for both genders. No difference was in the mean UGAP value when altering body position. UGAP showed good to excellent intra-reproducibility (Intra-class correlation 0.872; 95% CI 0.794-0.921).

CONCLUSION

UGAP provides excellent diagnostic performance to detect liver fat content in metabolic dysfunction-associated steatotic liver diseases, with good to excellent intra-reproducibility. Regardless of sex, the highest diagnostic accuracy is achieved with increased probe force with men in supine and women in 30° left decubitus position, yielding different cut-offs.

CLINICAL RELEVANCE STATEMENT

The ultrasound method ultrasound-guided attenuation parameter shows excellent diagnostic accuracy and performs with good to excellent reproducibility. There is a possibility to alter body position and increase probe pressure, and different performances for men and women should be considered for the highest accuracy.

KEY POINTS

• There is a possibility to alter body position when performing the ultrasound method ultrasound-guided attenuation parameter. • Increase probe pressure for the highest accuracy. • Different performances for men and women should be considered.

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.

Diagnostic accuracy of ultrasound-derived fat fraction for the detection and quantification of hepatic steatosis in patients with liver biopsy

PURPOSE

This retrospective study was conducted to investigate the diagnostic accuracy of ultrasound-derived fat fraction (UDFF) for grading hepatic steatosis using liver histology as the reference standard.

METHODS

Seventy-three patients with liver disease were assessed using UDFF and liver biopsy. Pearson's test and the Bland-Altman plot were used to assess the correlation between UDFF and histological fat content in liver sections. The UDFF cutoff values for histologically proven steatosis grades were determined using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The median age of the patients was 66 (interquartile range 54-74) years, and 33 (45%) were females. The UDFF values showed a stepwise increase with increasing steatosis grade (p < .001) and were strongly correlated with the histological fat content (r = .7736, p < .001). The Bland-Altman plot revealed a mean bias of 2.384% (95% limit of agreement, - 6.582 to 11.351%) between them. Univariate regression analysis revealed no significant predictors of divergence. The AUROCs for distinguishing steatosis grades of ≥ 1, ≥2, and 3 were 0.956 (95% confidence interval [CI], 0.910-1.00), 0.926 (95% CI, 0.860-0.993), and 0.971 (95% CI, 0.929-1.000), respectively. The UDFF cutoff value of > 6% had a sensitivity and specificity of 94.8% and 82.3%, respectively, for diagnosing steatosis grade ≥ 1. There was no association between UDFF and the fibrosis stage.

CONCLUSION

UDFF shows strong agreement with the histological fat content and excellent diagnostic accuracy for grading steatosis. UDFF is a promising tool for detecting and quantifying hepatic steatosis in clinical practice.

Interoperator reproducibility of quantitative ultrasound analysis of hepatic steatosis in participants with suspected MASLD: A prospective study

OBJECTIVES

To evaluate the reproducibility of tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) measurements in adults with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) between radiologists with varying experience.

MATERIALS AND METHODS

Participants with suspected MASLD were prospectively recruited. TAI and TSI were performed for each participant by two radiologists with different levels of experience. Interoperability reliability was assessed on the basis of Bland-Altman analysis and intraclass correlation coefficients (ICCs). The study determined and compared the diagnostic performance of TAI and TSI with clinical prediction models using proton magnetic resonance spectroscopy (1H-MRS) as a reference.

RESULTS

A total of 180 participants (women, n = 56; men, n = 124, mean age, 46.98 ± 14.92 years; mean BMI, 25.81 ± 4.47) were enrolled from August 2022 to September 2022. Bland-Altman plots showed only slight deviation in the TAI and TSI results of the two radiologists; there was good interoperator reproducibility for TAI (ICC = 0.92) and TSI (ICC = 0.86). Senior and junior radiologists performed examinations labeled as TAI-1 and TSI-1, and TAI-2 and TSI-2, respectively. The areas under the curves (AUCs) of TAI-1, TAI-2, TSI-1, and TAI-2 for the detection of ≥5 % hepatic steatosis were 0.90, 0.96, 0.91 and 0.96, respectively. According to ROC analysis, the diagnostic performance of both radiologists for TAI and TSI was statistically similar and superior to that of the clinical prediction model.

CONCLUSIONS

TAI and TSI have good reproducibility between radiologists with different levels of experience. Meanwhile, both TAI and TSI demonstrated good diagnostic performance for hepatic steatosis (≥5%), surpassing that of clinical prediction models.

Inter-platform reproducibility of ultrasound-based fat fraction for evaluating hepatic steatosis in nonalcoholic fatty liver disease

OBJECTIVES

To evaluate the inter-platform reproducibility of ultrasound-based fat fraction examination in nonalcoholic fatty liver disease (NAFLD).

METHODS

Patients suspected of having NAFLD were prospectively enrolled from January 2023. Ultrasound-based fat fraction examinations were performed using two different platforms (ultrasound-derived fat fraction [UDFF] and quantitative ultrasound-derived estimated fat fraction [USFF]) on the same day. The correlation between UDFF and USFF was assessed using Pearson correlation coefficient. Intraclass correlation coefficient (ICC), Bland-Altman analysis with 95% limits of agreement (LOAs), and the coefficient of variation (CV) were used to assess inter-platform reproducibility.

RESULTS

A total of 41 patients (21 men and 20 women; mean age, 53.9 ± 12.6 years) were analyzed. Moderate correlation was observed between UDFF and USFF (Pearson's r = 0.748; 95% confidence interval [CI]: 0.572-0.858). On Bland-Altman analysis, the mean difference between UDFF and USFF values was 1.3% with 95% LOAs ranging from -8.0 to 10.6%. The ICC between UDFF and USFF was 0.842 (95% CI: 0.703-0.916), with a CV of 29.9%.

CONCLUSION

Substantial inter-platform variability was observed among different ultrasound-based fat fraction examinations. Therefore, it is not appropriate to use ultrasound-based fat fraction values obtained from different vendors interchangeably.

CRITICAL RELEVANCE STATEMENT

Considering the substantial inter-platform variability in ultrasound-based fat fraction assessments, caution is imperative when interpreting and comparing fat fraction values obtained from different ultrasound platforms in clinical practice.

KEY POINTS

• Inter-platform reproducibility of ultrasound-based fat fraction examinations is important for its clinical application. • Significant variability across different ultrasound-based fat fraction examinations was observed. • Using ultrasound-based fat fraction values from different vendors interchangeably is not advisable.

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.

Multivariable Quantitative US Parameters for Assessing Hepatic Steatosis

Background Because of the global increase in the incidence of nonalcoholic fatty liver disease, the development of noninvasive, widely available, and highly accurate methods for assessing hepatic steatosis is necessary. Purpose To evaluate the performance of models with different combinations of quantitative US parameters for their ability to predict at least 5% steatosis in patients with chronic liver disease (CLD) as defined using MRI proton density fat fraction (PDFF). Materials and Methods Patients with CLD were enrolled in this prospective multicenter study between February 2020 and April 2021. Integrated backscatter coefficient (IBSC), signal-to-noise ratio (SNR), and US-guided attenuation parameter (UGAP) were measured in all participants. Participant MRI PDFF value was used to define at least 5% steatosis. Four models based on different combinations of US parameters were created: model 1 (UGAP alone), model 2 (UGAP with IBSC), model 3 (UGAP with SNR), and model 4 (UGAP with IBSC and SNR). Diagnostic performance of all models was assessed using area under the receiver operating characteristic curve (AUC). The model was internally validated using 1000 bootstrap samples. Results A total of 582 participants were included in this study (median age, 64 years; IQR, 52-72 years; 274 female participants). There were 364 participants in the steatosis group and 218 in the nonsteatosis group. The AUC values for steatosis diagnosis in models 1-4 were 0.92, 0.93, 0.95, and 0.96, respectively. The C-indexes of models adjusted by the bootstrap method were 0.92, 0.93, 0.95, and 0.96, respectively. Compared with other models, models 3 and 4 demonstrated improved discrimination of at least 5% steatosis (P < .01). Conclusion A model built using the quantitative US parameters UGAP, IBSC, and SNR could accurately discriminate at least 5% steatosis in patients with CLD. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Han in this issue.

Non-invasive evaluation of liver steatosis with imaging modalities: New techniques and applications

In the world, nonalcoholic fatty liver disease (NAFLD) accounts for majority of diffuse hepatic diseases. Notably, substantial liver fat accumulation can trigger and accelerate hepatic fibrosis, thus contributing to disease progression. Moreover, the presence of NAFLD not only puts adverse influences for liver but is also associated with an increased risk of type 2 diabetes and cardiovascular diseases. Therefore, early detection and quantified measurement of hepatic fat content are of great importance. Liver biopsy is currently the most accurate method for the evaluation of hepatic steatosis. However, liver biopsy has several limitations, namely, its invasiveness, sampling error, high cost and moderate intraobserver and interobserver reproducibility. Recently, various quantitative imaging techniques have been developed for the diagnosis and quantified measurement of hepatic fat content, including ultrasound- or magnetic resonance-based methods. These quantitative imaging techniques can provide objective continuous metrics associated with liver fat content and be recorded for comparison when patients receive check-ups to evaluate changes in liver fat content, which is useful for longitudinal follow-up. In this review, we introduce several imaging techniques and describe their diagnostic performance for the diagnosis and quantified measurement of hepatic fat content.

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.

Clinical evaluation of grayscale and linear scale hepatorenal indices for fatty liver quantification: a prospective study of a native Chinese population

BACKGROUND AND AIMS

Hepato-renal index (HRI) has been investigated extensively in various clinical studies. New linear scale HRI (LS-HRI) is proposed as an alternative to conventional grayscale HRI (GS-HRI) that suffers from lack of a widely accepted cut-off value for differentiation of fatty from normal livers. To investigate the diagnostic performance of conventional GS-HRI and new LS-HRI for a relatively large Chinese population with NAFLD using a well-established ultrasonographic fatty liver indicator (US-FLI) as the reference standard for steatosis grades.

MATERIALS AND METHODS

A total of 106 patients with various stages of NAFLD were prospectively enrolled. All ultrasound images for these patients were first acquired by a highly experienced ultrasound doctor and their US-FLI scores then obtained by the same doctor. Both GS-HRI and LS-HRI values were measured off-line by two additional ultrasound doctors. Four steatosis grades were determined from US-FLI scores for steatosis detection and staging.

RESULTS

Inter-observer agreements for both GS-HRI and LS-HRI were excellent with the respective concordance correlation coefficient (CCC) of 0.900 for GS-HRI and 0.822 for LS-HRI. A linear correlation to US-FLI for LS-HRI (R = 0.74) was substantially superior to that for GS-HRI (R = 0.46). LS-HRI had a sensitivity of 85.9% and a specificity of 96.3% to differentiate steatosis from the normal liver (AUROC: 95.5%) while GS-HRI had a sensitivity of 85.9% and a specificity of 92.6% to distinguish steatosis from the normal liver (AUROC: 94.7%).

CONCLUSIONS

Both GS-HRI and LS-HRI measurements are reproducible between two ultrasonographic clinicians and are evidently effective for steatosis detection.

SGLT2 Inhibitors as the Most Promising Influencers on the Outcome of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD), the most frequent liver disease in the Western world, is a common hepatic manifestation of metabolic syndrome (MetS). A specific cure has not yet been identified, and its treatment is currently based on risk factor therapy. Given that the initial accumulation of triglycerides in the liver parenchyma, in the presence of inflammatory processes, mitochondrial dysfunction, lipotoxicity, glucotoxicity, and oxidative stress, can evolve into non-alcoholic steatohepatitis (NASH). The main goal is to identify the factors contributing to this evolution because, once established, untreated NASH can progress through fibrosis to cirrhosis and, ultimately, be complicated by hepatocellular carcinoma (HCC). Several drugs have been tested in clinical trials for use as specific therapy for NAFLD; most of them are molecules used to cure type 2 diabetes mellitus (T2DM), which is one of the main risk factors for NAFLD. Among the most studied is pioglitazone, either alone or in combination with vitamin E, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 (DPP-4) inhibitors. Actually, the most promising category seems to be sodium-glucose cotransporter (SGLT2) inhibitors. Their action is carried out by inhibiting glucose reabsorption in the proximal renal tubule, leading to its increased excretion in urine and decreased levels in plasma. Experimental studies in animal models have suggested that SGLT2 inhibitors may have beneficial modulatory effects on NAFLD/NASH, and several trials in patients have proven their beneficial effects on liver enzymes, BMI, blood lipids, blood glucose, and insulin resistance in NAFLD patients, thus creating strong expectations for their possible use in preventing the evolution of liver damage in these patients. We will review the main pathogenetic mechanisms, diagnostic modalities, and recent therapies of NAFLD, with particular attention to the use of SGLT2 inhibitors.

Non-invasive methods for imaging hepatic steatosis and their clinical importance in NAFLD

Hepatic steatosis is a key histological feature of nonalcoholic fatty liver disease (NAFLD). The non-invasive quantification of liver fat is now possible due to advances in imaging modalities. Emerging data suggest that high levels of liver fat and its temporal change, as measured by quantitative non-invasive methods, might be associated with NAFLD progression. Ultrasound-based modalities have moderate diagnostic accuracy for liver fat content and are suitable for screening. However, of the non-invasive imaging modalities, MRI-derived proton density fat fraction (MRI-PDFF) has the highest diagnostic accuracy and is used for trial enrolment and to evaluate therapeutic effects in early-phase clinical trials in nonalcoholic steatohepatitis (NASH). In patients with NAFLD without advanced fibrosis, high levels of liver fat are associated with rapid disease progression. Furthermore, changes on MRI-PDFF (≥30% decline relative to baseline) are associated with NAFLD activity score improvement and fibrosis regression. However, an inverse association exists between liver fat and complications of cirrhosis. Liver fat decreases as liver fibrosis progresses towards cirrhosis, and the clinical importance of quantitative measurements of liver fat differs by NAFLD status. As such, patients with NAFLD should be stratified by fibrosis severity to investigate the utility of quantitative measurements of liver fat for assessing NAFLD progression and prognosis.

Deep impact of superficial skin inking: acoustic analysis of underlying tissue

Abstract Background: Skin tattoos are a common decoration, but profound scientific study whether the presence of a skin tattoo alters the acoustic response from superficial tissue, and therefore from underlying tissue, was previously lacking. Any image aberrations caused by tattoo presence may have been thought negligible, yet empirically found artifacts in brightness-mode images of tattooed skin suggest otherwise. This study investigated the nature of these artifacts theoretically and experimentally in extremely simplified cases of perfectly flat and homogenous layered media and in tattooed pork.Methods: Theory was derived for computing the acoustic response from horizontally and vertically layered media containing a thin inked layer. Experiments were performed in vitro. Artificial and pork skin were tattooed, attached to phantom material, and sonicated with a 13‐6-MHz probe. The speed of sound of these materials was determined, and the perceived refraction angles was measured.Results: The measured speeds of sound of tattooed materials were higher than those of their uninked counterparts. The presence of tattoo ink was found to have increased the linear acoustic attenuation by 1 dB/cm. This value is negligible for typical tattoos of only few millimeters. The perceived critical refraction angles of adjacent materials could be detected, and their corresponding speeds of sound were quantified. These coincided with values derived from theory.Conclusion: The ratio of speeds of sound of adjacent materials was shown to create distinct highlights in brightness-mode images. The artifacts observed in in vitro and in vivo brightness-mode scans were explained from near-vertical transitions between areas of different sound speed. This is the first study correlating so-called critical refraction highlighting with speed-of-sound information. In addition, it was found that phantom material is a room-temperature acoustic alternative for experiments on live human skin. In summary, the presence of superficial tattoos has a small but quantifiable effect on the acoustic response from deeper tissues.

Assessment of the inter-platform reproducibility of ultrasound attenuation examination in nonalcoholic fatty liver disease

Purpose

This study aimed to assess the inter-platform reproducibility of ultrasound attenuation examination in patients with nonalcoholic fatty liver disease (NAFLD).

Methods

Between March 2021 and April 2021, patients with clinically suspected or known NAFLD were prospectively enrolled; each patient underwent ultrasound attenuation examinations with three different platforms (Attenuation Imaging [ATI], Canon Medical System; Tissue Attenuation Imaging [TAI], Samsung Medison; and Ultrasound-Guided Attenuation Parameter [UGAP], GE Healthcare) on the same day. The mean attenuation coefficient (AC) values of the three platforms were compared using repeated-measures analysis of variance with the Bonferroni correction. To evaluate inter-platform reproducibility, the AC values obtained for each platform were compared using Bland-Altman analysis with the calculation of 95% limits of agreement (LOA), intraclass correlation coefficients (ICCs), and coefficients of variation (CVs).

Results

Forty-six patients (23 men; mean age±standard deviation, 52.3±12.4 years) were enrolled. The mean AC values showed significant differences among the three platforms (0.75±0.12, 0.80±0.11, and 0.74±0.09 dB/cm/MHz for ATI, TAI, and UGAP, respectively; P<0.001). For inter-platform reproducibility, the 95% LOAs were -0.22 to 0.11 dB/cm/MHz between ATI and TAI, -0.17 to 0.18 dB/cm/MHz between ATI and UGAP, and -0.08 to 0.20 dB/cm/MHz between TAI and UGAP, respectively. The pairwise ICCs were 0.790-0.797 in terms of absolute agreement among the three platforms; the CVs were 8.23%-9.47%.

Conclusion

The AC values obtained from different ultrasound attenuation examination platforms showed significant differences, with significant inter-platform variability. Therefore, the AC values measured using different ultrasound attenuation examination techniques should not be used interchangeably for longitudinal follow-up of patients with NAFLD.

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

Quantitative ultrasound (QUS) aims at quantifying interactions between ultrasound and biological tissues. QUS techniques extract fundamental physical properties of tissues based on interactions between ultrasound waves and tissue microstructure. These techniques provide quantitative information on sub-resolution properties that are not visible on grayscale (B-mode) imaging. Quantitative data may be represented either as a global measurement or as parametric maps overlaid on B-mode images. Recently, major ultrasound manufacturers have released speed of sound, attenuation, and backscatter packages for tissue characterization and imaging. Established and emerging clinical applications are currently limited and include liver fibrosis staging, liver steatosis grading, and breast cancer characterization. On the other hand, most biological tissues have been studied using experimental QUS methods, and quantitative datasets are available in the literature. This educational review addresses the general topic of biological soft tissue characterization using QUS, with a focus on disseminating technical concepts for clinicians and specialized QUS materials for medical physicists. Advanced but simplified technical descriptions are also provided in separate subsections identified as such. To understand QUS methods, this article reviews types of ultrasound waves, basic concepts of ultrasound wave propagation, ultrasound image formation, point spread function, constructive and destructive wave interferences, radiofrequency data processing, and a summary of different imaging modes. For each major QUS technique, topics include: concept, illustrations, clinical examples, pitfalls, and future directions.

Multiparametric Quantitative US Examination of Liver Fibrosis: A Feature-engineering and Machine-learning Based Analysis

Quantitative ultrasound (QUS), which is commonly used to extract quantitative features from the ultrasound radiofrequency (RF) data or the RF envelope signals for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, to some extent limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machinelearning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for classifying liver fibrosis with the selected features, based on the results of which the optimal classifier was established and used for final classification. The performance of the proposed MPQUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82% and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24% and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects.

Quantitative ultrasound approaches for diagnosis and monitoring hepatic steatosis in nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease is a major global health concern with increasing prevalence, associated with obesity and metabolic syndrome. Recently, quantitative ultrasound-based imaging techniques have dramatically improved the ability of ultrasound to detect and quantify hepatic steatosis. These newer ultrasound techniques possess many inherent advantages similar to conventional ultrasound such as universal availability, real-time capability, and relatively low cost along with quantitative rather than a qualitative assessment of liver fat. In addition, quantitative ultrasound-based imaging techniques are less operator dependent than traditional ultrasound. Here we review several different emerging quantitative ultrasound-based approaches used for detection and quantification of hepatic steatosis in patients at risk for nonalcoholic fatty liver disease. We also briefly summarize other clinically available imaging modalities for evaluating hepatic steatosis such as MRI, CT, and serum analysis.

Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks

Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue.

Assessment of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease by Using Quantitative US

Background Advanced confounder-corrected chemical shift-encoded MRI-derived proton density fat fraction (PDFF) is a leading parameter for fat fraction quantification in nonalcoholic fatty liver disease (NAFLD). Because of the limited availability of this MRI technique, there is a need to develop and validate alternative parameters to assess liver fat. Purpose To assess relationship of quantitative US parameters to MRI PDFF and to develop multivariable quantitative US models to detect hepatic steatosis and quantify hepatic fat. Materials and Methods Adults with known NAFLD or who were suspected of having NAFLD were prospectively recruited between August 2015 and February 2019. Participants underwent quantitative US and chemical shift-encoded MRI liver examinations. Liver biopsies were performed if clinically indicated. The correlation between seven quantitative US parameters and MRI PDFF was evaluated. By using leave-one-out cross validation, two quantitative US multivariable models were evaluated: a classifier to differentiate participants with NAFLD versus participants without NAFLD and a fat fraction estimator. Classifier performance was summarized by area under the receiver operating characteristic curve and area under the precision-recall curve. Fat fraction estimator performance was evaluated by correlation, linearity, and bias. Results Included were 102 participants (mean age, 52 years ± 13 [standard deviation]; 53 women), 78 with NAFLD (MRI PDFF ≥ 5%). A two-variable classifier yielded a cross-validated area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.82, 0.96) and an area under the precision-recall curve of 0.96 (95% confidence interval: 0.93, 0.99). The cross-validated fat fraction predicted by a two-variable fat fraction estimator was correlated with MRI PDFF (Spearman ρ = 0.82 [P < .001]; Pearson r = 0.76 [P < .001]). The mean bias was 0.02% (P = .97), and 95% limits of agreement were ±12.0%. The predicted fat fraction was linear with MRI PDFF (R ² = 0.63; slope, 0.69; intercept, 4.3%) for MRI PDFF of 34% or less. Conclusion A multivariable quantitative US approach yielded excellent correlation with MRI proton density fat fraction for hepatic steatosis assessment in nonalcoholic fatty liver disease. © RSNA, 2020 Online supplemental material is available for this article.

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis

Objectives

To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading.

Methods

Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2–3 days before surgery. We proposed a deep convolutional neural network by TL to analyze images of gray scale modality (GM) and elastogram modality (EM). The TL process was used for liver fibrosis classification by Inception-V3 network which pretrained on ImageNet. The diagnostic performance of TL and non-TL was compared. The value of single modalities, including GM and EM alone, and multimodalities, including GM + LSM and GM + EM, was evaluated and compared with that of LSM and serological indexes. Receiver operating characteristic curve analysis was performed to calculate the optimal area under the curve (AUC) for classifying fibrosis of S4, ≥ S3, and ≥ S2.

Results

TL in GM and EM demonstrated higher diagnostic accuracy than non-TL, with significantly higher AUCs (all p < .01). Single-modal GM and EM both performed better than LSM and serum indexes (all p < .001). Multimodal GM + EM was the most accurate prediction model (AUCs are 0.950, 0.932, and 0.930 for classifying S4, ≥ S3, and ≥ S2, respectively) compared with GM + LSM, GM and EM alone, LSM, and biomarkers (all p < .05).

Conclusions

Liver fibrosis can be staged by a transfer learning modal based on the combination of gray scale and elastogram ultrasound images, with excellent performance.

Key Points

• Transfer learning consists in applying to a specific deep learning algorithm that pretrained on another relevant problem, expected to reduce the risk of overfitting due to insufficient medical images.

• Liver fibrosis can be staged by transfer learning radiomics with excellent performance.

• The most accurate prediction model of transfer learning by Inception-V3 network is the combination of gray scale and elastogram ultrasound images.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06595-w) contains supplementary material, which is available to authorized users.

A Phantom-Based Assessment of Repeatability and Reproducibility of Transvaginal Quantitative Ultrasound

This article evaluated the repeatability and reproducibility (R&R) of quantitative ultrasound (QUS) biomarkers attenuation coefficient (AC) and backscatter coefficient (BSC) in transvaginal QUS reference phantoms for obstetric applications. Five phantoms were scanned by three sonographers according to the scanning protocol. Each sonographer scanned each phantom with four transvaginal transducers of the same model (MC9-4) and three probe cover types (latex cover, nonlatex cover, and no cover). The AC and BSC were estimated by using a reference phantom method. The R&R analysis was performed for the frequency-averaged AC and logBSC (= 10log₁₀BSC) (5.4-5.8 MHz) by using three-factor random effects Analysis of Variance with interaction. The total R&R variabilities for AC and logBSC are small (AC: 0.042-0.065 dB/cm-MHz; logBSC: 0.50-0.68 dB), indicating high measurement precision. These values are small compared to the ranges of AC (0.28-0.99 dB/cm-MHz) and logBSC (-33.16 to -20.35 dB) of the five phantoms. The AC and logBSC biomarkers measured on transvaginal QUS phantoms using the reference phantom method are repeatable, and reproducible between sonographers, transducers, and probe covers.

Assessment of hepatic steatosis by using attenuation imaging: a quantitative, easy-to-perform ultrasound technique

OBJECTIVES

To evaluate the diagnostic performance of attenuation imaging (ATI) in the detection of hepatic steatosis compared with a histopathology gold standard.

METHODS

We prospectively enrolled 108 consecutive patients (35 males; median age, 54.0 years) who underwent percutaneous liver biopsy for evaluation of diffuse liver disease between January 2018 and November 2018 in a tertiary academic center. Grayscale ultrasound examination with ATI was performed just before biopsy, and an attenuation coefficient (AC) was obtained from each patient. The degree of hepatic steatosis, fibrosis stage, and necroinflammatory activity were assessed on histopathologic examination. The significant factor associated with the AC was found by a linear regression analysis, and the diagnostic performance of the AC for the classification into each hepatic steatosis stage was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

The distribution of hepatic steatosis grade on histopathology was 53/11/22/16/6 for none/mild (< 10%)/mild (≥ 10%)/moderate/severe steatosis, respectively. The area under the ROC curve, sensitivity, specificity, and optimal cutoff AC value for detection of hepatic steatosis ranged from 0.843-0.926, 74.5-100.0%, 77.4-82.8%, and 0.635-0.745, respectively. Multivariate analysis revealed that the degree of steatosis was the only significant determinant factor for the AC.

CONCLUSIONS

The AC from ATI provided good diagnostic performance in detecting the varying degrees of hepatic steatosis. The degree of steatosis was the only significant factor affecting the AC, whereas fibrosis and inflammation were not.

KEY POINTS

• Attenuation imaging (ATI) is based on two-dimensional grayscale ultrasound images that can incorporate into routine ultrasound examinations with less than 2 min of acquisition time. • ATI provided good diagnostic performance in detecting the varying degrees of hepatic steatosis with an area under the ROC curves ranging from 0.843 to 0.926, and there was no technical failure in this study indicating high applicability of this technique. • The degree of hepatic steatosis was the only significant factor affecting the result of ATI examination.