Contrast-enhanced ultrasound (CEUS): applications from the kidneys to the bladder.

Contrast-enhanced ultrasound (CEUS) is an advanced ultrasound (US) technique utilizing ultrasound contrast agents (UCAs) to provide detailed visualization of anatomic and vascular architecture, including the depiction of microcirculation. CEUS has been well-established in echocardiography and imaging of focal hepatic lesions and recent studies have also shown the utility of CEUS in non-hepatic applications like the urinary system. The updated guidelines by the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) from 2018 describe the use of CEUS for non-hepatic applications. CEUS' excellent safety profile and spatial resolution make it a superior modality to conventional US and is often comparable and even superior to CECT in some instances. In comparison to other cross-sectional imaging modalities such as CECT or MRI, CEUS offers a safe (by virtue of non-nephrotoxic US contrast agents), accurate, cost-efficient, readily available, and a quick means of evaluation of multiple pathologies of the urinary system. CEUS also has the potential to reduce the overall economic burden on patients requiring long-term follow-up due to its low cost as compared to CT or MRI techniques. This comprehensive review focuses on the applications of CEUS in evaluating the urinary system from the kidneys to the urinary bladder. CEUS can be utilized in the kidney to evaluate complex cystic lesions, indeterminate lesions, pseudotumors (vs solid renal tumors), renal infections, and renal ischemic disorders. Additionally, CEUS has also been utilized in evaluating renal transplants. In the urinary bladder, CEUS is extremely useful in differentiating a bladder hematoma and bladder cancer when conventional US techniques show equivocal results. Quantitative parameters of time-intensity curves (TICs) of CEUS examinations have also been studied to stage and grade bladder cancers. Although promising, further research is needed to definitively stage bladder cancers and classify them as muscle-invasive or non-muscle invasive using quantitative CEUS to guide appropriate intervention. CEUS has been very effective in the classification of cystic renal lesions, however, further research is needed in differentiating benign from malignant renal masses.

Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study.

This study aims to develop a semiautomated pipeline and user interface (LiVaS) for rapid segmentation and labeling of MRI liver vasculature and evaluate its time efficiency and accuracy against manual reference standard. Retrospective feasibility pilot study. Liver MR images from different scanners from 36 patients were included, and 4/36 patients were randomly selected for manual segmentation as referenced standard. The liver was segmented in each contrast phase and masks registered to the pre-contrast segmentation. Voxel-wise signal trajectories were clustered using the k-means algorithm. Voxel clusters that best segment the liver vessels were selected and labeled by three independent radiologists and a research scientist using LiVaS. Segmentation times were compared using a paired-sample t-test on log-transformed data. The agreement was analyzed qualitatively and quantitatively using DSC for hepatic and portal vein segmentations. The mean segmentation time among four readers was significantly shorter than manual (3.6 ± 1.4 vs. 70.0 ± 29.2 min; p < 0.001), even when using a higher number of clusters to enhance accuracy. The DSC for portal and hepatic veins reached up to 0.69 and 0.70, respectively. LiVaS segmentations were overall of good quality, with variations in performance related to the presence/severity of liver disease, acquisition timing, and image quality. Our semi-automated pipeline was robust to different MRI vendors in producing segmentation and labeling of liver vasculature in agreement with expert manual annotations, with significantly higher time efficiency. LiVaS could facilitate the creation of large, annotated datasets for training and validation of neural networks for automated MRI liver vascularity segmentation. HIGHLIGHTS: Key Finding: In this pilot feasibility study, our semiautomated pipeline for segmentation of liver vascularity (LiVaS) on MR images produced segmentations with simultaneous labeling of portal and hepatic veins in good agreement with the manual reference standard but at significantly shorter times (mean LiVaS 3.6 ± 1.4 vs. mean manual 70.0 ± 29.2 min; p < 0.001). Importance: LiVaS was robust in producing liver MRI vascular segmentations across images from different scanners in agreement with expert manual annotations, with significant ly higher time efficiency, and therefore potential scalability.

Radiological Society of North America/Quantitative Imaging Biomarker Alliance Shear Wave Speed Bias Quantification in Elastic and Viscoelastic Phantoms.

OBJECTIVES: To quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms. METHODS: Two elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz. RESULTS: The SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a - 0.27 ± 0.027-m/s (-12.2% ± 1.2%) bias when using the clinically implemented 60-Hz vibration source. CONCLUSIONS: Shear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140-Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

Variation of Shear Wave Elastography With Preload in the Thyroid: Quantitative Validation.

OBJECTIVES: Thyroid shear wave elastography (SWE) has been shown to have advantages compared to biopsy or other imaging modalities in the evaluation of thyroid nodules. However, studies show variability in its assessment. The objective of this study was to evaluate whether stiffness measurements of the normal thyroid, as estimated by SWE, varied due to preload force or the pressure applied between the transducer and the patient. METHODS: In this study, a measurement system was attached to the ultrasound transducer to measure the applied load. Shear wave elastographic measurements were obtained from the left lobe of the thyroid at applied transducer forces between 2 and 10 N. A linear mixed-effects model was constructed to quantify the association between the preload force and stiffness while accounting for correlations between repeated measurements within each participant. The preload force effect on elasticity was modeled by both linear and quadratic terms to account for a possible nonlinear association between these variables. RESULTS: Nineteen healthy volunteers without known thyroid disease participated in the study. The participants had a mean age ± SD of 36 ± 8 years; 74% were female; 74% had a normal body mass index; and 95% were white non-Hispanic/Latino. The estimated elastographic value at a 2-N preload force was 16.7 kPa (95% confidence interval, 14.1-19.3 kPa), whereas the value at 10 N was 29.9 kPa (95% confidence interval, 24.9-34.9 kPa). CONCLUSIONS: The preload force was significantly and nonlinearly associated with SWE estimates of thyroid stiffness. Quantitative standardization of preload forces in the assessment of thyroid nodules using elastography is an integral factor for improving the accuracy of thyroid nodule evaluation.

Image Processing Pipeline for Liver Fibrosis Classification Using Ultrasound Shear Wave Elastography.

The purpose of this study was to develop an automated method for classifying liver fibrosis stage ≥F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference approach consists of manually selecting a region of interest from each of eight or more SWE images, computing the mean tissue stiffness within each of the regions of interest and computing a resulting stiffness value as the median of the means. The 527-subject database consisted of 5526 SWE images and pathologist-scored biopsies, with data collected from a single system at a single site. The automated method integrates three modules that assess SWE image quality, select a region of interest from each SWE measurement and perform machine learning-based, multi-image SWE classification for fibrosis stage ≥F2. Several classification methods were developed and tested using fivefold cross-validation with training, validation and test sets partitioned by subject. Performance metrics were area under receiver operating characteristic curve (AUROC), specificity at 95% sensitivity and number of SWE images required. The final automated method yielded an AUROC of 0.93 (95% confidence interval: 0.90-0.94) versus 0.69 (95% confidence interval: 0.65-0.72) for the reference method, 71% specificity with 95% sensitivity versus 5% and four images per decision versus eight or more. In conclusion, the automated method reported in this study significantly improved the accuracy for ≥F2 classification of SWE measurements as well as reduced the number of measurements needed, which has the potential to reduce clinical workflow.

Diagnostic Accuracy of Shear Wave Elastography as a Non-invasive Biomarker of High-Risk Non-alcoholic Steatohepatitis in Patients with Non-alcoholic Fatty Liver Disease.

In this study, we evaluated the diagnostic accuracy of shear wave elastography (SWE) for differentiating high-risk non-alcoholic steatohepatitis (hrNASH) from non-alcoholic fatty liver and low-risk non-alcoholic steatohepatitis (NASH). Patients with non-alcoholic fatty liver disease scheduled for liver biopsy underwent pre-biopsy SWE. Ten SWE measurements were obtained. Biopsy samples were reviewed using the NASH Clinical Research Network Scoring System and patients with hrNASH were identified. Receiver operating characteristic curves for SWE-based hrNASH diagnosis were charted. One hundred sixteen adult patients underwent liver biopsy at our institution for the evaluation of non-alcoholic fatty liver disease. The area under the receiver operating characteristic curve of SWE for hrNASH diagnosis was 0.73 (95% confidence interval: 0.61-0.84, p < 0.001). The Youden index-based optimal stiffness cutoff value for hrNASH diagnosis was calculated as 8.4 kPa (1.67 m/s), with a sensitivity of 77% and specificity of 66%. SWE may be useful for the detection of NASH patients at risk of long-term liver-specific morbidity and mortality.

Portal Venous Pulsatility Index: A Novel Biomarker for Diagnosis of High-Risk Nonalcoholic Fatty Liver Disease.

OBJECTIVE. The purpose of this study was to assess the accuracy of portal vein pulsatility for noninvasive diagnosis of high-risk nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS. This retrospective study included patients with biopsy-proven diagnosis of NAFLD who underwent duplex Doppler ultrasound assessment of the main portal vein within 1 year of liver biopsy (January 2014 to February 2018). Doppler ultrasound images were reviewed. The spectral waveform was used to measure the maximum (Vmax) and minimum (Vmin) velocity of blood in the portal veins. Venous pulsatility index (VPI) defined as (Vmax - Vmin) / Vmax was calculated. ROC curve analysis was used to calculate AUC as a measure of accuracy to determine the value of this index for diagnosis of high-risk NAFLD and compared with that of the following four clinical decision aids: NAFLD fibrosis score (FS), fibrosis-4 index (FIB-4), BARD score (body mass index, aspartate aminotransferase [AST]-to-alanine aminotransferase ratio, diabetes mellitus), and AST-to-platelet ratio index (APRI). The value of adding VPI to these indexes was also investigated. RESULTS. Of 123 study subjects, 33 (26.8%) had high-risk NAFLD and were found to have a lower VPI than the other 90 subjects (0.19 vs 0.32; p < 0.001). VPI, NAFLD FS, FIB-4, and APRI had statistically significant diagnostic values for high-risk NAFLD. VPI had the highest optimism-corrected AUC (VPI, 0.84 [95% CI, 0.77-0.91]; NAFLD FS, 0.74 [95% CI, 0.63-0.83]; FIB-4, 0.81 [95% CI, 0.72-0.89]; APRI, 0.73 [95% CI, 0.61-0.82]). Addition of VPI to any of the four scoring systems significantly improved the diagnostic value of the score for high-risk NAFLD. CONCLUSION. VPI may be an accurate noninvasive biomarker for diagnosis of high-risk NAFLD.

Challenges in IBD Research: Novel Technologies.

Novel technologies is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, environmental triggers, precision medicine and pragmatic clinical research. The Challenges in IBD research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the novel technologies section is focused on prioritizing unmet clinical needs in IBD that will benefit from novel technologies applied to: 1) non-invasive detection and monitoring of active inflammation and assessment of treatment response; 2) mucosal targeted drug delivery systems; and 3) prevention of post-operative septic complications and treatment of fistulizing complications. Proposed approaches include development of multiparametric imaging modalities and biosensors, to enable non invasive or minimally invasive detection of pro-inflammatory signals to monitor disease activity and treatment responses. Additionally, technologies for local drug delivery to control unremitting disease and increase treatment efficacy while decreasing systemic exposure are also proposed. Finally, research on biopolymers and other sealant technologies to promote post-surgical healing; and devices to control anastomotic leakage and prevent post-surgical complications and recurrences are also needed.

Diagnostic Performance of Shear Wave Elastography in Patients With Autoimmune Liver Disease.

OBJECTIVES: To assess performance of shear wave elastography for evaluation of fibrosis and the histologic stage in patients with autoimmune liver disease (ALD) and to validate previously established advanced fibrosis cutoff values in this cohort. METHODS: Shear wave elastography was performed on patients with ALD with an Aixplorer ultrasound system (SuperSonic Imagine, Aix-en-Provence, France) using an SC6-1 transducer. The median estimated tissue Young modulus was calculated from sets of 8 to 10 elastograms. A blinded, subspecialty-trained pathologist reviewed biopsy specimens. The METAVIR classification was used to stage liver fibrosis and necroinflammation. Steatosis was graded from 0 to 4+. The Kendall τ-b correlation test was performed to identify the correlation between the estimated tissue Young modulus and fibrosis, steatosis, and the necroinflammatory score. The Spearman correlation test was performed to identify the correlation between the estimated tissue Young modulus and clinical data. The diagnostic performance of shear wave elastography for differentiating METAVIR stage F2 or higher from F0 and F1 fibrosis was evaluated by a receiver operating characteristic (ROC) curve analysis. RESULTS: Fifty-one patients with ALD were analyzed. The estimated tissue Young modulus was positively correlated with the fibrosis stage and necroinflammation score (r = 0.386; P < .001; r = 0.338; P = .002, respectively) but not steatosis (r = -0.091; P = .527). Serum aspartate aminotransferase, alanine aminotransferase, and total bilirubin values were positively correlated with the estimated tissue Young modulus (r = 0.501; P < .001; r = 0.44; P = .001; r = 0.291; P = .038). The serum albumin value was negatively correlated (r = -0.309; P = .033). The area under the ROC curve was 0.781 (95% confidence interval, 0.641-0.921) for distinguishing F2 or greater fibrosis from F0 and F1 fibrosis. Based on the ROC curve, an optimal cutoff value of 9.15 kPa was identified (sensitivity, 83.3%; specificity, 72.7%). CONCLUSIONS: Shear wave elastography is a novel noninvasive adjunct to liver biopsy in evaluation and staging of patients with ALD, showing the potential for serial evaluations of disease progression and treatment responses.

Objective Liver Fibrosis Estimation from Shear Wave Elastography.

Diffuse liver disease is common, primarily driven by high prevalence of non-alcoholic fatty liver disease (NAFLD). It is currently assessed by liver biopsy to determine fibrosis, often staged as F0 (normal) - F4 (cirrhosis). A noninvasive assessment method will allow a broader population to be monitored longitudinally, facilitating risk stratification and treatment efficacy assessment. Ultrasound shear wave elastography (SWE) is a promising noninvasive technique for measuring tissue stiffness that has been shown to correlate with fibrosis stage. However, this approach has been limited by variability in stiffness measurements. In this work, we developed and evaluated an automated framework, called SWE-Assist, that checks SWE image quality, selects a region of interest (ROI), and classifies the ROI to determine whether the fibrosis stage is at or exceeds F2, which is important for clinical decisionmaking. Our database consists of 3,392 images from 328 cases. Several classifiers, including random forest, support vector machine, and convolutional neural network (CNN)) were evaluated. The best approach utilized a CNN and yielded an area under the receiver operating curve (AUROC) of 0.89, compared to the conventional stiffness only based AUROC of 0.74. Moreover, the new method is based on single image per decision, vs. 10 images per decision for the baseline. A larger dataset is needed to further validate this approach, which has the potential to improve the accuracy and efficiency of non-invasive liver fibrosis staging.

Quantitative Hepatic Fat Quantification in Non-alcoholic Fatty Liver Disease Using Ultrasound-Based Techniques: A Review of Literature and Their Diagnostic Performance.

Non-alcoholic fatty liver disease is a condition that is characterized by the presence of >5% fat in the liver and affects more than one billion people worldwide. If adequate and early precautions are not taken, non-alcoholic fatty liver disease can progress to cirrhosis and death. The current reference standard for detecting hepatic steatosis is a liver biopsy. However, because of the potential morbidity associated with liver biopsies, non-invasive imaging biomarkers have been extensively investigated. Magnetic resonance imaging-based methods have proven accuracy in quantifying liver steatosis; however, these techniques are costly and have limited availability. Ultrasound-based quantitative imaging techniques are increasingly utilized because of their widespread availability, ease of use and relative cost-effectiveness. Several ultrasound-based liver fat quantification techniques have been investigated, including techniques that measure changes in the acoustic properties of the liver caused by the presence of fat. In this review, we focus on quantitative ultrasound approaches and their diagnostic performance in the realm of non-alcoholic fatty liver disease.

Surgery for Obesity and Related Diseases: I. A Novel Approach to the Quantification of the Longitudinal Speed of Sound and Its Potential for Tissue Characterization.

Described here is a method to determine the longitudinal speed of sound in speckle-dominated ultrasound images. The method is based on the concept that the quality of an ultrasound image is maximized when the beamformer's speed of sound matches the speed in the medium. The method captures the quality of the ultrasound image using two quantitative image-quality metrics: image brightness and sharpness around the intended focal zone. The proposed method requires no calibration, is computationally efficient and is deployable on commercial ultrasound systems without hardware or software modifications. Ex vivo testing on tissue-mimicking phantoms indicates the method's accuracy in predicting the true speed of sound to within 1% of ground truth values.

Current status of imaging in nonalcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is the most common diffuse liver disease, with a worldwide prevalence of 20% to 46%. NAFLD can be subdivided into simple steatosis and nonalcoholic steatohepatitis. Most cases of simple steatosis are non-progressive, whereas nonalcoholic steatohepatitis may result in chronic liver injury and progressive fibrosis in a significant minority. Effective risk stratification and management of NAFLD requires evaluation of hepatic parenchymal fat, fibrosis, and inflammation. Liver biopsy remains the current gold standard; however, non-invasive imaging methods are rapidly evolving and may replace biopsy in some circumstances. These methods include well-established techniques, such as conventional ultrasonography, computed tomography, and magnetic resonance imaging and newer imaging technologies, such as ultrasound elastography, quantitative ultrasound techniques, magnetic resonance elastography, and magnetic resonance-based fat quantitation techniques. The aim of this article is to review the current status of imaging methods for NAFLD risk stratification and management, including their diagnostic accuracy, limitations, and practical applicability.

Experimental Validation of Longitudinal Speed of Sound Estimates in the Diagnosis of Hepatic Steatosis (Part II).

This study validates a non-invasive, quantitative technique to diagnose steatosis within tissue. The proposed method is based on two fundamental concepts: (i) the speed of sound in a fatty liver is lower than that in a healthy liver and (ii) the quality of an ultrasound image is maximized when the beamformer's speed of sound matches the speed in the medium under examination. The method uses image brightness and sharpness as quantitative image-quality metrics to predict the true sound speed and capture the effects of fat infiltration, while accounting for the transmission through subcutaneous fat. Ex vivo testing on sheep liver, mouse livers and tissue-mimicking phantoms indicated the technique's ability to predict the true speed of sound with errors less than 0.5% and to quantify the inverse correlation between fat content and speed of sound.

Ultrasound Shear Wave Elastography: Variations of Liver Fibrosis Assessment as a Function of Depth, Force and Distance from Central Axis of the Transducer with a Comparison of Different Systems.

We evaluated variation in fibrosis staging caused by depth, pre-load force and measurement off-axis distance on different ultrasound shear wave elastography (SWE) systems prospectively in 20 patients with diffuse liver disease. Shear wave speed (SWS) was measured with transient elastography, acoustic radiation force impulse (ARFI) and 2-D shear wave elastography (SWE). ARFI and 2-D-SWE measurements were obtained at different depths (3, 5 and 7 cm), with different pre-load forces (4, 7 and 10N and variable) and at 0, 2 and 4cm off the central axis of the transducer. A single, blinded pathologist staged fibrosis using the METAVIR system (F0-F4). Area under the receiver operating characteristic curve was charted to differentiate significant fibrosis (F ≥ 2). Depth was the only factor found to influence ARFI-derived values; no acquisition factors were found to affect 2-D-SWE SWS values. ARFI and 2-D-SWE for diagnosis of significant fibrosis at a depth of 7cm along the central axis had good diagnostic performance (areas under the receiver operating characteristic curve: 0.92 and 0.82, respectively), comparable to that of transient elastography. Further investigation of this finding will likely be of interest.

Machine learning for medical ultrasound: status, methods, and future opportunities.

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

Principles of ultrasound elastography.

Tissue stiffness has long been known to be a biomarker of tissue pathology. Ultrasound elastography measures tissue mechanical properties by monitoring the response of tissue to acoustic energy. Different elastographic techniques have been applied to many different tissues and diseases. Depending on the pathology, patient-based factors, and ultrasound operator-based factors, these techniques vary in accuracy and reliability. In this review, we discuss the physical principles of ultrasound elastography, discuss differences between different ultrasound elastographic techniques, and review the advantages and disadvantages of these techniques in clinical practice.