Ultrasound Attenuation Estimation in Harmonic Imaging for Robust Fatty Liver Detection

Ultrasound attenuation imaging as a strategy for evaluation of early and late ambulatory functions in Duchenne muscular dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is a genetic neuromuscular disorder that leads to mobility loss and life-threatening cardiac or respiratory complications. Quantitative ultrasound (QUS) envelope statistics imaging, which characterizes fat infiltration and fibrosis in muscles, has been extensively used for DMD evaluations.

PURPOSE

Notably, changes in muscle microstructures also result in acoustic attenuation, potentially serving as another crucial imaging biomarker for DMD. Expanding upon the reference frequency method (RFM), this study contributes to the field by introducing the robust RFM (RRFM) as a novel approach for ultrasound attenuation imaging in DMD.

METHODS

The RRFM algorithm was developed using an iterative reweighted least squares technique. We conducted standard phantom measurements with a clinical ultrasound system equipped with a linear array transducer to assess the improvement in attenuation estimation bias by RRFM. Additionally, 161 DMD patients, included in both a validation dataset (n = 130) and a testing dataset (n = 31), underwent ultrasound scanning of the gastrocnemius for RRFM-based attenuation imaging. The diagnostic performances for ambulatory functions and discrimination between early and late ambulatory stages were evaluated and compared with those of QUS envelope statistics imaging (involving Nakagami distribution, homodyned K distribution, and entropy values) using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The results indicated that the RRFM method more closely matched the actual attenuation properties of the phantom, reducing measurement bias by 50% compared to conventional RFM. The AUROCs for RRFM-based attenuation imaging, used to discriminate between early and late ambulatory stages, were 0.88 and 0.92 for the validation and testing datasets, respectively. These performances significantly surpassed those of QUS envelope statistics imaging (p < 0.05).

CONCLUSIONS

Ultrasound attenuation imaging employing RRFM may serve as a sensitive tool for evaluating the progression of ambulatory function deterioration, offering substantial potential for the health management and follow-up care of DMD patients.

Novel Quantitative Liver Steatosis Assessment Method With Ultrasound Harmonic Imaging

OBJECTIVES

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder in Western countries, with approximately 20%-30% of the MASLD patients progressing to severe stages. There is an urgent need for noninvasive, cost-effective, widely accessible, and precise biomarkers to evaluate liver steatosis. This study aims to assess and compare the diagnostic performance of a novel reference frequency method-based ultrasound attenuation coefficient estimation (ACE) in both fundamental (RFM-ACE-FI) and harmonic (RFM-ACE-HI) imaging for detecting and grading liver steatosis.

METHODS

An Institutional Review Board-approved prospective study was carried out between December 2018 and October 2022. A total number of 130 subjects were enrolled in the study. The correlation between RFM-ACE-HI values and magnetic resonance imaging proton density fat fraction (MRI-PDFF), as well as between RFM-ACE-FI values and MRI-PDFF were calculated. The diagnostic performance of RFM-ACE-FI and RFM-ACE-HI was evaluated using receiver operating characteristic (ROC) curve analysis, as compared to MRI-PDFF. The reproducibility of RFM-ACE-HI was assessed by interobserver agreement between two sonographers.

RESULTS

A strong correlation was observed between RFM-ACE-HI and MRI-PDFF, with R = 0.88 (95% confidence interval [CI]: 0.83-0.92; P < .001), while the correlation between RFM-ACE-FI and MRI-PDFF was R = 0.65 (95% CI: 0.50-0.76; P < .001). The area under the ROC (AUROC) curve for RFM-ACE-HI in staging liver steatosis grades of S ≥ 1 and S ≥ 2 was 0.97 (95% CI: 0.91-0.99; P < .001) and 0.98 (95% CI: 0.93-1.00; P < .001), respectively, and 0.76 (95% CI: 0.65-0.85) and 0.80 (95% CI: 0.70-0.88) for RFM-ACE-FI, respectively. Great reproducibility was achieved for RFM-ACE-HI, with an interobserver agreement of R = 0.97 (95% CI: 0.94-0.99; P < .001).

CONCLUSIONS

The novel RFM-ACE-HI method offered high liver steatosis diagnostic accuracy and reproducibility, which has important clinical implications for early disease intervention and treatment evaluation.

Contrast-Enhanced Sonography of the Liver: How to Avoid Artifacts

Contrast-enhanced sonography (CEUS) is a very important diagnostic imaging tool in clinical settings. However, it is associated with possible artifacts, such as B-mode US-related artifacts. Sufficient knowledge of US physics and these artifacts is indispensable to avoid the misinterpretation of CEUS images. This review aims to explain the basic physics of CEUS and the associated artifacts and to provide some examples to avoid them. This review includes problems related to the frame rate, scanning modes, and various artifacts encountered in daily CEUS examinations. Artifacts in CEUS can be divided into two groups: (1) B-mode US-related artifacts, which form the background of the CEUS image, and (2) artifacts that are specifically related to the CEUS method. The former includes refraction, reflection, reverberation (multiple reflections), attenuation, mirror image, and range-ambiguity artifacts. In the former case, the knowledge of B-mode US is sufficient to read the displayed artifactual image. Thus, in this group, the most useful artifact avoidance strategy is to use the reference B-mode image, which allows for a simultaneous comparison between the CEUS and B-mode images. In the latter case, CEUS-specific artifacts include microbubble destruction artifacts, prolonged heterogeneous accumulation artifacts, and CEUS-related posterior echo enhancement; these require an understanding of the mechanism of their appearance in CEUS images for correct image interpretation. Thus, in this group, the most useful artifact avoidance strategy is to confirm the phenomenon's instability by changing the examination conditions, including the frequency, depth, and other parameters.

Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework

Noninvasive, accurate, and simultaneous grading of liver fibrosis, inflammation, and steatosis is valuable for reversing the progression and improving the prognosis quality of chronic liver diseases (CLDs). In this study, we established an artificial intelligence framework for simultaneous grading diagnosis of these three pathological types through fusing multimodal tissue characterization parameters dug by quantitative ultrasound methods derived from ultrasound radiofrequency signals, B-mode images, shear wave elastography images, and clinical ultrasound systems, using the liver biopsy results as the classification criteria. One hundred forty-two patients diagnosed with CLD were enrolled in this study. The results show that for the classification of fibrosis grade ≥ F1, ≥ F2, ≥ F3, and F4, the highest AUCs were respectively 0.69, 0.82, 0.84, and 0.88 with single clinical indicator alone, and were 0.81, 0.83, 0.89, and 0.91 with the proposed method. For the classification of inflammation grade ≥ A2 and A3, the highest AUCs were respectively 0.66 and 0.76 with single clinical indicator alone and were 0.80 and 0.93 with the proposed method. For the classification of steatosis grade ≥ S1 and ≥ S2, the highest AUCs were respectively 0.71 and 0.90 with single clinical indicator alone and were 0.75 and 0.92 with the proposed method. The proposed method can effectively improve the grading diagnosis performance compared with the present clinical indicators and has potential applications for noninvasive, accurate, and simultaneous diagnosis of CLDs.

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.

Improved Ultrasound Attenuation Estimation with Non-uniform Structure Detection and Removal

Accurate detection of liver steatosis is important for liver disease management. Ultrasound attenuation coefficient estimation (ACE) has great potential in quantifying liver fat content. The ACE methods commonly assume uniform tissue characteristics. However, in vivo tissues typically contain non-uniform structures, which may bias the attenuation estimation and lead to large standard deviations. Here we propose a series of non-uniform structure detection and removal (NSDR) methods to reduce the impact from non-uniform structures during ACE analysis. The effectiveness of NSDR was validated through phantom and in vivo studies. In a pilot clinical study, ACE with NSDR provided more robust in vivo performance as compared with ACE without NSDR, indicating its potential for in vivo applications.

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tissues. The support vector machine (SVM) has shown promise as a supervised classification algorithm and has been widely used. However, the classification results typically identify a category of abnormal tissues but do not necessarily differentiate progressive stages of a disease. Moreover, the classification result is typically provided independently as a supplement to medical images, which contributes to an overload of information sources in the clinic. Hence, we propose a new imaging method utilizing the SVM to integrate classification results into medical images. This framework is called disease-specific imaging (DSI) that produces a color overlaid highlight on B-mode ultrasound images indicating the type, location, and severity of pathology from different conditions. In this article, the SVM training was performed to construct hyperplanes that can differentiate normal, fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastases in livers based on ultrasound echoes. Also, cluster centroids for specific diseases define unique disease axes, and the inner product between measured features and any disease axis selected by the SVM quantifies the disease progression. The features were measured from 2794 ultrasound frames using the H-scan analysis, attenuation estimation, and B-mode image analysis. The performance of our proposed DSI method was evaluated for a preclinical model of steatosis ( n = 400 frames). The contribution of each feature was assessed, and the results were compared with ground truth from histology. Moreover, the images generated by our DSI were compared with earlier imaging methods of B-mode, H-scan, and histology. The comparisons demonstrate that DSI images yield higher sensitivity to monitor progressive steatosis than B-mode and H-scan and provide a comparable performance with the histology. For the parameter comparison, DSI and H-scan resulted in similar correlation with histology ( r_s = 0.83 ) but higher than attenuation ( r_s = 0.73 ) and B-mode ( r_s = 0.47 ). Therefore, we conclude that DSI utilizing the SVM applied to steatosis can visually represent the classification results with color highlighting, which can simplify the interpretation of classification compared to the traditional SVM result. We expect that the proposed DSI can be used for any medical imaging modality that can estimate multiple quantitative parameters at high resolution.

Noninvasive, quantitative evaluation of hepatic steatosis of donor livers by reference frequency method: A preliminary study

PURPOSE

Evaluating degree of hepatic steatosis is of great value for prognosis of liver transplantation. There is an urgent need for a non-invasive method to assess hepatic steatosis grade of donor livers. Purpose of our study was to evaluate diagnostic accuracy of attenuation coefficient estimation (ACE) by reference frequency method (RFM) in detecting hepatic steatosis of donor livers.

METHOD

We retrospectively enrolled 62 potential liver donors which underwent ACE by RFM ex-vivo, in-vivo or both. We acquired raw data of B-mode images of liver parenchyma and offline-processes for attenuation estimation. Finally, we calculated and compared diagnostic performance of ACEs for steatosis grade detection and used histological results as the gold standard.

RESULTS

ACEs with none, mild and moderate hepatic steatosis were 0.57, 0.73 and 0.80 dB/cm/MHz in potential donor livers. The cutoff value to diagnose mild hepatic steatosis was 0.63 dB/cm/MHz and 0.77 dB/cm/MHz for moderate hepatic steatosis, and values for the area under the receiver operating characteristic curve for diagnosis of mild and moderate hepatic steatosis were 0.92 and 0.90, respectively.

CONCLUSIONS

According to our results, ACE by RFM is an accurate non-invasive method in detecting hepatic steatosis, which may be of great help for clinical evaluation of donor livers before liver transplantation.

Noise Suppression for Ultrasound Attenuation Coefficient Estimation Based on Spectrum Normalization

Ultrasound attenuation coefficient estimation (ACE) has great diagnostic potential for fatty liver detection and assessment. In a previous study, we proposed a reference phantom-free ACE method, called reference frequency method (RFM), which does not require a calibrated phantom for normalization. The power of each frequency component can be normalized by the power of an adjacent frequency component in the spectrum to cancel system-dependent effects such as focusing and time gain compensation (TGC). RFM demonstrated accurate ACE in both phantom and in in-vivo liver studies. However, our study also showed that the robustness and penetration of RFM were affected by noise in the ACE signals. Here we propose a noise suppression (NS) and a signal-to-noise ratio (SNR) quality control method to reduce the influence of noise on ACE-RFM performance. The proposed methods were tested in harmonic ACE because harmonic imaging is a more frequently used mode than fundamental imaging for abdominal applications. After applying the NS and SNR control methods, the noise-induced bias for attenuation estimation in harmonic ACE was effectively reduced, leading to significantly improved effective penetration depth. The proposed methods directly measure the noise spectrum of the ultrasound system, which can also be adapted to other spectrum-based ACE methods, such as the reference phantom method and the spectra shift method.

H-scan trajectories indicate the progression of specific diseases

PURPOSE

The ability of ultrasound to assess pathology is increasing with the development of quantitative parameters. Among these are a set of parameters derived from the recent H-scan analysis of subresolvable scattering. The emergence of these quantitative measures of tissue/ultrasound interactions now enables a study of the unique trajectories of multiparametric features in multidimensional space, representing the progression of specific diseases over time. We develop the mathematical and visual tools that are effective for classifying, quantifying, and visualizing the steady progression of several diseases from independent studies, all within a uniform framework.

METHODS

After applying the H-scan analysis of ultrasound echoes, we trained a support vector machine (SVM) to classify the unique trajectories of progressive liver disease from fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastasis. Our approaches include the development of trajectory maps and disease-specific color imaging stains.

RESULTS

The multidimensional SVM image classification reached 100% accuracy across the three different studies.

CONCLUSION

H-scan trajectories can be useful to track the progression of multiple classes of diseases, improving diagnosis, staging, and assessing the response to therapy.