Diagnostic Performance of 9 Quantitative Ultrasound Parameters for Detection and Classification of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer

OBJECTIVES

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer.

METHODS

We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively.

RESULTS

Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93).

CONCLUSION

Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

MRI Dixon Fat-Corrected Look-Locker T1 Mapping for Quantification of Liver Fibrosis and Inflammation-A Comparison With the Non-Fat-Corrected Shortened Modified Look-Locker Inversion Recovery Technique

OBJECTIVES

This study evaluates the impact of liver steatosis on the discriminative ability for liver fibrosis and inflammation using a novel Dixon water-only fat-corrected Look-Locker T1 mapping sequence, compared with a standard shortened Modified Look-Locker Inversion Recovery (shMOLLI) sequence, with the aim of overcoming the limitation of steatosis-related confounding in liver T1 mapping.

MATERIALS AND METHODS

3 T magnetic resonance imaging of the liver including the 2 T1 mapping sequences and proton density fat fraction (PDFF) was prospectively performed in 24 healthy volunteers and 38 patients with histologically proven liver fibrosis evaluated within 90 days of liver biopsy. Paired Mann-Whitney test compared sequences between participants with and without significant liver steatosis (PDFF cutoff 10%), and unpaired Kruskal-Wallis test compared healthy volunteers to patients with early (F0-2) and advanced (F3-4) liver fibrosis, as well as low (A0-1) and marked (A2-3) inflammatory activity. Univariate and multivariate logistic regression models assessed the impact of liver steatosis on both sequences.

RESULTS

Dixon_W T1 was higher than shMOLLI T1 in participants without steatosis (median 896 ms vs 890 ms, P = 0.04), but lower in participants with liver steatosis (median 891 ms vs 973 ms, P < 0.001). Both methods accurately differentiated between volunteers and patients with early and advanced fibrosis (Dixon_W 849 ms, 910 ms, 947 ms, P = 0.011; shMOLLI 836 ms, 918 ms, 978 ms, P < 0.001), and those with mild and marked inflammation (Dixon_W 849 ms, 896 ms, 941 ms, P < 0.01; shMOLLI 836 ms, 885 ms, 978 ms, P < 0.001). Univariate logistic regression showed slightly lower performance of the Dixon_W sequence in differentiating fibrosis (0.69 vs 0.73, P < 0.01), compensated by adding liver PDFF in the multivariate model (0.77 vs 0.75, P < 0.01).

CONCLUSIONS

Dixon water-only fat-corrected Look-Locker T1 mapping accurately identifies liver fibrosis and inflammation, with less dependency on liver steatosis than the widely adopted shMOLLI T1 mapping technique, which may improve its predictive value for these conditions.

Correlation between qualitative and semi-quantitative ultrasound assessment of diffuse fatty liver disease: A case-control study

Objective

To study the relationship between qualitative and semi-quantitative assessment of diffuse liver steatosis in ultrasound.

Patients and Methods

This was a case-control study, conducted in the Campus University Hospital Centre of Lome (Togo) over a 3-month period. It included 40 patients showing ultrasonographic signs of diffuse hepatic steatosis and 40 volunteers (healthy) whose echostructure and echogenicity of the hepatic parenchyma were normal. The B-mode sonographic grade of steatosis was compared with the hepatorenal echogenicity gradient and the ultrasound attenuation coefficient.

Results

The average body mass index in patients was 30.87 ± 4.65 kg/m2 versus 24.25 ± 4.30 kg/m2 in the healthy group (p < 0.00001). Hepatomegaly was observed in 57.5% of the patients versus 17.5% in the healthy group (p = 0.0005). The average hepatorenal echogenicity ratio was 1.18 ± 0.07 in patients versus 1.01 ± 0.03 in the healthy group (p < 0.00001). The average difference in hepatorenal echogenicity was 9.30 ± 3.41 dB in patients versus 1.52 ± 1.07 dB in the healthy group (p < 0.00001). The attenuation of ultrasound waves increased with the grade of steatosis, averaging 0.08 ± 0.23 dB/cm/MHz (ranging from -0.33 to 0.61 dB/cm/MHz) in patients versus -0.24 ± 0.21 (ranging from -0.69 to 0.19 dB/cm/MHz) in the healthy group (p < 0.00001).

Conclusion

Despite the advancements in new ultrasound technologies today, qualitative methods continue to be effective for the detection of hepatic steatosis and could prove useful in monitoring the effectiveness of hepatic steatosis treatment.

Comparing ultrasound-derived fat fraction and MRI-PDFF for quantifying hepatic steatosis: a real-world prospective study

OBJECTIVE

To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard.

METHODS

This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis.

RESULTS

A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5-18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0-18.0) and 9.0% (IQR: 5.0-18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934-0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852-0.930). The Bland-Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, -8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively.

CONCLUSIONS

UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.

KEY POINTS

Question Is ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)? Findings UDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively. Clinical relevance UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.

Comparing Three Ultrasound-Based Techniques for Diagnosing and Grading Hepatic Steatosis in Metabolic Dysfunction-Associated Steatotic Liver Disease

RATIONALE AND OBJECTIVES

To compare the diagnostic accuracy and grading ability of ultrasound-derived fat fraction (UDFF), controlled attenuation parameters (CAP), and hepatic/renal ratio (HRR) for hepatic steatosis in metabolic dysfunction-associated steatotic liver disease (MASLD) using magnetic resonance imaging proton density fat fraction (PDFF) as the gold standard.

METHODS

Patients suspected of having MASLD in our hospital between October 2023 and May 2024 were divided into the MASLD group and the control group. All patients underwent UDFF, CAP, and PDFF examinations. HRR was measured during routine ultrasound examination. In statistical analysis, we initially assessed the correlation between UDFF, CAP, HRR, and general characteristics of subjects with PDFF. Subsequently, receiver operating characteristic curve were employed to evaluate and compare the diagnostic performance of UDFF, CAP, and HRR for different grades of hepatic steatosis in MASLD. Their area under the curve, optimal cut-off value, sensitivity, and specificity were also determined. Finally, predictive factors determined hepatic steatosis in MASLD (PDFF≥6%) were identified through binary logistic regression analysis.

RESULTS

115 individuals were ultimately included in the MASLD group, while 102 were included in the control group. UDFF, CAP, and HRR were all positively correlated with PDFF. Among them, UDFF exhibited the strongest correlation with PDFF (ρ = 0.91). Furthermore, in the comparison of diagnostic efficacy among different grades of hepatic steatosis, UDFF outperformed CAP and HRR (p < 0.05). However, there were no statistically significant differences in AUCs between CAP and HRR across all three grades. The AUCs for UDFF in ≥S1, ≥S2, and ≥S3 were 0.99 (95% CI 0.97 to 1.00), 0.96 (95% CI 0.93 to 0.98), and 0.97 (95% CI 0.94 to 0.99), respectively. The optimal thresholds for UDFF are determined as follows: ≥ 6% for grade S1; ≥ 15% for grade S2; and ≥ 23% for grade S3. Multivariate analysis revealed that only age, UDFF, and CAP were important influencing factors for hepatic steatosis in MASLD.

CONCLUSION

The diagnostic accuracy of UDFF surpassed that of CAP and HRR in the detection and grading of hepatic steatosis in MASLD.

Early and accurate diagnosis of steatotic liver by artificial intelligence (AI)-supported ultrasonography

OBJECTIVES

Steatotic liver disease is the most frequent chronic liver disease worldwide. Ultrasonography (US) is commonly employed for the assessment and diagnosis. Few information is available on the possible use of artificial intelligence (AI) to ameliorate the diagnostic accuracy of ultrasonography.

MATERIALS AND METHODS

An AI-based algorithm was developed using a dataset of US images. We prospectively enrolled 134 patients for algorithm validation. Patients underwent abdominal US and Proton Density Fat Fraction MRI scans (MRI-PDFF), assumed as reference technique. The hepatorenal index was manually calculated (HRIM) by 4 operators. An automatic hepatorenal index (HRIA) was obtained by the algorithm. The accuracy of HRIA to discriminate steatosis grades was evaluated by ROC analysis using MRI-PDFF cut-offs.

RESULTS

Overweight was 40 % of subjects (BMI 26.4 kg/cm2). The median HRIA was 1.11 (IQR 0.32) and the average of 4 manually calculated HRIM was 1.08 (IQR 0.26), with a 15 % inter-operator variability. Both HRIA (R = 0.79, P < 0.0001) and HRIM (R = 0.69, P < 0.0001) significantly correlated with liver fat percentage (MRI-PDFF). According to MRI-PDFF, 32 % of enrolled subjects had steatosis. Discrimination capacity by AUC between patient with steatosis and patient without steatosis was better for HRIA than HRIM (AUC: 0.87 vs. 0.82, respectively). ROC analysis showed an AUC = 0.98 for HRIA with 1.64 cut-off in distinguishing between mild and moderate/severe groups.

CONCLUSIONS

The use of AI improves accuracy and speed of ultrasonography in the diagnosis of liver steatosis. Further studies should evaluate the routine use of this technique in the management of liver steatosis at high cardio-metabolic risk.

Multiparametric ultrasound imaging for early-stage steatosis: Comparison with magnetic resonance imaging-based proton density fat fraction

BACKGROUND

The prevalence of liver diseases, especially steatosis, requires a more convenient and noninvasive tool for liver diagnosis, which can be a surrogate for the gold standard biopsy. Magnetic resonance (MR) measurement offers potential, however ultrasound (US) has better accessibility than MR.

PURPOSE

This study aims to suggest a multiparametric US approach which demonstrates better quantification and imaging performance than MR imaging-based proton density fat fraction (MRI-PDFF) for hepatic steatosis assessment.

METHODS

We investigated early-stage steatosis to evaluate our approach. An in vivo (within the living) animal study was performed. Fat inclusions were accumulated in the animal livers by feeding a methionine and choline deficient (MCD) diet for 2 weeks. The animals (n = 19) underwent US and MR imaging, and then their livers were excised for histological staining. From the US, MR, and histology images, fat accumulation levels were measured and compared: multiple US parameters; MRI-PDFF; histology fat percentages. Seven individual US parameters were extracted using B-mode measurement, Burr distribution estimation, attenuation estimation, H-scan analysis, and shear wave elastography. Feature selection was performed, and the selected US features were combined, providing quantification of fat accumulation. The combined parameter was used for visualizing the localized probability of fat accumulation level in the liver; This procedure is known as disease-specific imaging (DSI).

RESULTS

The combined US parameter can sensitively assess fat accumulation levels, which is highly correlated with histology fat percentage (R = 0.93, p-value < 0.05) and outperforms the correlation between MRI-PDFF and histology (R = 0.89, p-value < 0.05). Although the seven individual US parameters showed lower correlation with histology compared to MRI-PDFF, the multiparametric analysis enabled US to outperform MR. Furthermore, this approach allowed DSI to detect and display gradual increases in fat accumulation. From the imaging output, we measured the color-highlighted area representing fatty tissues, and the fat fraction obtained from DSI and histology showed strong agreement (R = 0.93, p-value < 0.05).

CONCLUSIONS

We demonstrated that fat quantification utilizing a combination of multiple US parameters achieved higher performance than MRI-PDFF; therefore, our multiparametric analysis successfully combined selected features for hepatic steatosis characterization. We anticipate clinical use of our proposed multiparametric US analysis, which could be beneficial in assessing steatosis in humans.

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.

Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features

Aim

Cancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.

Methods

72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and k-nearest neighbour (k-NN)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.

Results

It was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with k-NN classifier.

Conclusion

Binary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.

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.

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.

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.

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

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

Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis

Adiposity accumulation in the liver is an early-stage indicator of non-alcoholic fatty liver disease. Analysis of ultrasound (US) backscatter echoes from liver parenchyma with deep learning (DL) may offer an affordable alternative for hepatic steatosis staging. The aim of this work was to compare DL classification scores for liver steatosis using different data representations constructed from raw US data. Steatosis in N = 31 patients with confirmed or suspected non-alcoholic fatty liver disease was stratified based on fat-fraction cutoff values using magnetic resonance imaging as a reference standard. US radiofrequency (RF) frames (raw data) and clinical B-mode images were acquired. Intermediate image formation stages were modeled from RF data. Power spectrum representations and phase representations were also calculated. Co-registered patches were used to independently train 1-, 2- and 3-D convolutional neural networks (CNNs), and classifications scores were compared with cross-validation. There were 67,800 patches available for 2-D/3-D classification and 1,830,600 patches for 1-D classification. The results were also compared with radiologist B-mode annotations and quantitative ultrasound (QUS) metrics. Patch classification scores (area under the receiver operating characteristic curve [AUROC]) revealed significant reductions along successive stages of the image formation process (p &lt; 0.001). Patient AUROCs were 0.994 for RF data and 0.938 for clinical B-mode images. For all image formation stages, 2-D CNNs revealed higher patch and patient AUROCs than 1-D CNNs. CNNs trained with power spectrum representations converged faster than those trained with RF data. Phase information, which is usually discarded in the image formation process, provided a patient AUROC of 0.988. DL models trained with RF and power spectrum data (AUROC = 0.998) provided higher scores than conventional QUS metrics and multiparametric combinations thereof (AUROC = 0.986). Radiologist annotations indicated lower hepatic steatosis classification accuracies (Acc = 0.914) with respect to magnetic resonance imaging proton density fat fraction that DL models (Acc = 0.989). Access to raw ultrasound data combined with artificial intelligence techniques may offer superior opportunities for quantitative tissue diagnostics than conventional sonographic images.

Reverberation clutter signal suppression in ultrasound attenuation estimation using wavelet-based robust principal component analysis

Objective. Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as quantifying fat content in the liver. Previously, we have proposed a system-independent ACE technique based on spectral normalization of different frequencies, called the reference frequency method (RFM). This technique does not require a well-calibrated reference phantom for normalization. However, this method may be vulnerable to severe reverberation clutter introduced by the body wall. The clutter superimposed on liver echoes may bias the estimation. Approach. We proposed to use robust principal component analysis, combined with wavelet-based sparsity promotion, to suppress the severe reverberation clutters. The capability to mitigate the reverberation clutters was validated through phantom and in vivo studies. Main Results. In the phantom studies with added reverberation clutters, higher normalized cross-correlation and smaller mean absolute errors were attained as compared to RFM results without the proposed method, demonstrating the capability to reconstruct tissue signals from reverberations. In a pilot patient study, the correlation between ACE and proton density fat fraction (PDFF), a measurement of liver fat by MRI as a reference standard, was investigated. The proposed method showed an improvement of the correlation (coefficient of determination, R = 0.82) as compared with the counterpart without the proposed method (R = 0.69). Significance: The proposed method showed the feasibility of suppressing the reverberation clutters, providing an important basis for the development of a robust ACE with large reverberation clutters.