ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

Acoustic diffraction-resistant adaptive profile technology (ADAPT) for elasticity imaging

Acoustic beam shaping with high degrees of freedom is critical for applications such as ultrasound imaging, acoustic manipulation, and stimulation. However, the ability to fully control the acoustic pressure profile over its propagation path has not yet been achieved. Here, we demonstrate an acoustic diffraction-resistant adaptive profile technology (ADAPT) that can generate a propagation-invariant beam with an arbitrarily desired profile. By leveraging wave number modulation and beam multiplexing, we develop a general framework for creating a highly flexible acoustic beam with a linear array ultrasonic transducer. The designed acoustic beam can also maintain the beam profile in lossy material by compensating for attenuation. We show that shear wave elasticity imaging is an important modality that can benefit from ADAPT for evaluating tissue mechanical properties. Together, ADAPT overcomes the existing limitation of acoustic beam shaping and can be applied to various fields, such as medicine, biology, and material science.

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.

Repeatability of MRI Biomarkers in Nonalcoholic Fatty Liver Disease: The NIMBLE Consortium

Background There is a need for reliable noninvasive methods for diagnosing and monitoring nonalcoholic fatty liver disease (NAFLD). Thus, the multidisciplinary Non-invasive Biomarkers of Metabolic Liver disease (NIMBLE) consortium was formed to identify and advance the regulatory qualification of NAFLD imaging biomarkers. Purpose To determine the different-day same-scanner repeatability coefficient of liver MRI biomarkers in patients with NAFLD at risk for steatohepatitis. Materials and Methods NIMBLE 1.2 is a prospective, observational, single-center short-term cross-sectional study (October 2021 to June 2022) in adults with NAFLD across a spectrum of low, intermediate, and high likelihood of advanced fibrosis as determined according to the fibrosis based on four factors (FIB-4) index. Participants underwent up to seven MRI examinations across two visits less than or equal to 7 days apart. Standardized imaging protocols were implemented with six MRI scanners from three vendors at both 1.5 T and 3 T, with central analysis of the data performed by an independent reading center (University of California, San Diego). Trained analysts, who were blinded to clinical data, measured the MRI proton density fat fraction (PDFF), liver stiffness at MR elastography (MRE), and visceral adipose tissue (VAT) for each participant. Point estimates and CIs were calculated using χ2 distribution and statistical modeling for pooled repeatability measures. Results A total of 17 participants (mean age, 58 years ± 8.5 [SD]; 10 female) were included, of which seven (41.2%), six (35.3%), and four (23.5%) participants had a low, intermediate, or high likelihood of advanced fibrosis, respectively. The different-day same-scanner mean measurements were 13%-14% for PDFF, 6.6 L for VAT, and 3.15 kPa for two-dimensional MRE stiffness. The different-day same-scanner repeatability coefficients were 0.22 L (95% CI: 0.17, 0.29) for VAT, 0.75 kPa (95% CI: 0.6, 0.99) for MRE stiffness, 1.19% (95% CI: 0.96, 1.61) for MRI PDFF using magnitude reconstruction, 1.56% (95% CI: 1.26, 2.07) for MRI PDFF using complex reconstruction, and 19.7% (95% CI: 15.8, 26.2) for three-dimensional MRE shear modulus. Conclusion This preliminary study suggests that thresholds of 1.2%-1.6%, 0.22 L, and 0.75 kPa for MRI PDFF, VAT, and MRE, respectively, should be used to discern measurement error from real change in patients with NAFLD. ClinicalTrials.gov registration no. NCT05081427 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kozaka and Matsui in this issue.

Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices

Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder region segmentation and urine volume calculation. B-mode ultrasound bladder images of 360 patients were divided into training and validation sets; another 74 patients were used as the test dataset. Our 1-bit quantized models with 4-bits and 6-bits skip connections achieved an accuracy within [Formula: see text] and [Formula: see text], respectively, of a full precision state-of-the-art neural network (NN) without any floating-point operations and with an [Formula: see text] and [Formula: see text] reduction in memory requirements to fit under 150 kB. The means and standard deviations of the volume estimation errors, relative to estimates from ground-truth clinician annotations, were [Formula: see text] ml and [Formula: see text] ml, respectively. This lightweight NN can be easily integrated on the wearable ultrasound device for automated and continuous monitoring of urine volume. Our approach can potentially be extended to other clinical applications, such as monitoring blood pressure and fetal heart rate.

Individualized Ultrasound-Guided Intervention Phantom Development, Fabrication, and Proof of Concept

Commercial ultrasound vascular phantoms lack the anatomic diversity required for robust pre-clinical interventional device testing. We fabricated individualized phantoms to test an artificial intelligence enabled ultrasound-guided surgical robotic system (AI-GUIDE) which allows novices to cannulate deep vessels. After segmenting vessels on computed tomography scans, vessel cores, bony anatomy, and a mold tailored to the skin contour were 3D-printed. Vessel cores were coated in silicone, surrounded in tissue-mimicking gel tailored for ultrasound and needle insertion, and dissolved with water. One upper arm and four inguinal phantoms were constructed. Operators used AI-GUIDE to deploy needles into phantom vessels. Two groin phantoms were tested due to imaging artifacts in the other two phantoms. Six operators (medical experience: none, 3; 1-5 years, 2; 5+ years, 1) inserted 27 inguinal needles with 81% (22/27) success in a median of 48 seconds. Seven operators performed 24 arm injections, without tuning the AI for arm anatomy, with 71% (17/24) success. After excluding failures due to motor malfunction and a defective needle, success rate was 100% (22/22) in the groin and 85% (17/20) in the arm. Individualized 3D-printed phantoms permit testing of surgical robotics across a large number of operators and different anatomic sites. AI-GUIDE operators rapidly and reliably inserted a needle into target vessels in the upper arm and groin, even without prior medical training. Virtual device trials in individualized 3-D printed phantoms may improve rigor of results and expedite translation.Clinical Relevance- Individualized phantoms enable rigorous and efficient evaluation of interventional devices and reduce the need for animal and human subject testing.

The Ultra fit community mask-Toward maximal respiratory protection via personalized face fit

Effective masking policies to prevent the spread of airborne infections depend on public access to masks with high filtration efficacy. However, poor face-fit is almost universally present in pleated multilayer disposable face masks, severely limiting both individual and community respiratory protection. We developed a set of simple mask modifications to mass-manufactured disposable masks, the most common type of mask used by the public, that dramatically improves both their personalized fit and performance in a low-cost and scalable manner. These modifications comprise a user-moldable full mask periphery wire, integrated earloop tension adjusters, and an inner flange to trap respiratory droplets. We demonstrate that these simple design changes improve quantitative fit factor by 320%, triples the level of protection against aerosolized droplets, and approaches the model efficacy of N95 respirators in preventing the community spread of COVID-19, for an estimated additional cost of less than 5 cents per mask with automated production.

HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy

Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles' diameter is usually in the range of 2-10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.

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.

Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation

Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutions of feature maps of the same resolution) outputs feature maps from the last convolution, limiting the variety of the receptive fields. Second, the network has a symmetric structure where the encoder and the decoder paths have similar numbers of channels. We explored two novel revisions: a stacked dilated operation that outputs feature maps from multi-scale receptive fields to replace the consecutive convolutions; an asymmetric architecture with fewer channels in the decoder path. Two novel models were developed: U-Net using the stacked dilated operation (SDU-Net) and asymmetric SDU-Net (ASDU-Net). We used both publicly available and private datasets to assess the efficacy of the proposed models. Extensive experiments confirmed SDU-Net outperformed or achieved performance similar to the state-of-the-art while using fewer parameters (40% of U-Net). ASDU-Net further reduced the model parameters to 20% of U-Net with performance comparable to SDU-Net. In conclusion, the stacked dilated operation and the asymmetric structure are promising for improving the performance of U-Net and its variants.

Liver fibrosis assessment: MR and US elastography

Elastography has emerged as a preferred non-invasive imaging technique for the clinical assessment of liver fibrosis. Elastography methods provide liver stiffness measurement (LSM) as a surrogate quantitative biomarker for fibrosis burden in chronic liver disease (CLD). Elastography can be performed either with ultrasound or MRI. Currently available ultrasound-based methods include strain elastography, two-dimensional shear wave elastography (2D-SWE), point shear wave elastography (pSWE), and vibration-controlled transient elastography (VCTE). MR Elastography (MRE) is widely available as two-dimensional gradient echo MRE (2D-GRE-MRE) technique. US-based methods provide estimated Young's modulus (eYM) and MRE provides magnitude of the complex shear modulus. MRE and ultrasound methods have proven to be accurate methods for detection of advanced liver fibrosis and cirrhosis. Other clinical applications of elastography include liver decompensation prediction, and differentiation of non-alcoholic steatohepatitis (NASH) from simple steatosis (SS). In this review, we briefly describe the different elastography methods, discuss current clinical applications, and provide an overview of advances in the field of liver elastography.

Machine Learning-Assisted Diagnostic System for Indeterminate Thyroid Nodules

To develop an ultrasound-based machine learning classifier to diagnose benignity within indeterminate thyroid nodules (ITNs) by fine-needle aspiration, 180 patients with 194 ITNs (Bethesda classes III, IV and V) undergoing surgery over a 5-y study period were analyzed. The data set was randomly divided into training and testing data sets with 155 and 39 ITNs, respectively. All nodules were evaluated by ultrasound using the American College of Radiology Thyroid Imaging Reporting and Data System by manually scoring composition, echogenicity, shape, margin and echogenic foci. Nodule size, participant age and patient sex were recorded. A support vector machine (SVM) model with a cost-sensitive approach was developed using the aforementioned eight parameters with surgical histopathology as the reference standard. Surgical pathology determined 90 (46.4%) ITNs were malignant and 104 (53.6%) were benign. The SVM model classified 14 nodules as benign in the testing data set, of which 13 were correct (sensitivity = 93.8%, specificity = 56.5%). Considering malignancy prevalence by Bethesda group, the negative predictive values of this model for Bethesda III and IV categories were 93.9% and 93. 8%, respectively. The high negative predictive value of the SVM ultrasound-based model suggests a pathway by which surgical excision of Bethesda III and IV ITNs classified as benign may be avoided.

AI-Enabled, Ultrasound-Guided Handheld Robotic Device for Femoral Vascular Access

Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions. However, central access is normally performed by highly experienced critical care physicians in a hospital setting. We developed a handheld AI-enabled interventional device, AI-GUIDE (Artificial Intelligence Guided Ultrasound Interventional Device), capable of directing users with no ultrasound or interventional expertise to catheterize a deep blood vessel, with an initial focus on the femoral vein. AI-GUIDE integrates with widely available commercial portable ultrasound systems and guides a user in ultrasound probe localization, venous puncture-point localization, and needle insertion. The system performs vascular puncture robotically and incorporates a preloaded guidewire to facilitate the Seldinger technique of catheter insertion. Results from tissue-mimicking phantom and porcine studies under normotensive and hypotensive conditions provide evidence of the technique's robustness, with key performance metrics in a live porcine model including: a mean time to acquire femoral vein insertion point of 53 ± 36 s (5 users with varying experience, in 20 trials), a total time to insert catheter of 80 ± 30 s (1 user, in 6 trials), and a mean number of 1.1 (normotensive, 39 trials) and 1.3 (hypotensive, 55 trials) needle insertion attempts (1 user). These performance metrics in a porcine model are consistent with those for experienced medical providers performing central vascular access on humans in a hospital.

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.

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.

Cervical Extension of the Normal Thymus in Children and Adolescents: Sonographic Features and Prevalence

OBJECTIVE

This study aims to confirm the prevalence of incidental cervical extension of normal thymus in children and adolescents undergoing neck ultrasound and describe the ultrasound appearance to minimize future misdiagnosis.

MATERIALS AND METHODS

This retrospective study was conducted in a single institution. Thyroid and lower neck ultrasound images of the consecutive pediatric subjects between January 1, 2011 and September 30, 2017 were independently reviewed by 2 radiologists for the presence of cervical thymus. When identified on sonographic images, cervical thymus was described on the basis of echogenicity, location, and shape.

RESULTS

In 278 consecutive cases, the 2 reviewers identified 105 (37.8%) and 103 (37.1%) cases respectively as having sonographically visible tissue in the expected location of cervical extension of the thymus. The internal echotexture was variable with 38.1% of cases being hypoechoic, 37.1% mixed, and 24.8% hyperechoic. Cervical extension of the thymus was most commonly (65.0%) to the left of the trachea or (30.9%) bilateral/anterior to the trachea; isolated right paratracheal thymus was uncommon. Thymic shape was variable: quadrilateral (30.9%), oval (29.9%), triangular (25.8%), and other (13.4%). The logistic regression model including age, gender, and BMI z-scores showed that, when controlled for sex and BMI z-scores, younger age was a predictor for the presence of cervical thymic extension (p < .001).

CONCLUSION

Cervical thymic extension is sonographically visible as a soft tissue mass of variable appearance in about a third of children and adolescents undergoing neck ultrasonography with decreasing prevalence with age. Sonographically visible cervical thymic tissue is more common in younger patients.

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.

Renal Volume Estimation Using Freehand Ultrasound Scans: An Ex Vivo Demonstration

Renal volume has the potential to serve as a robust biomarker for tracking the onset and progression of renal diseases and also for quantifying renal function. We propose a method to estimate renal volumes using freehand ultrasound scans at the point of care. A conventional ultrasound probe was augmented with an Intel RealSense D435 i camera. Visual inertial simultaneous localization and mapping was used to localize the probe in free space. The acquired 2-D ultrasound images, segmented by trained clinicians, were combined with the estimated poses of the probe to yield accurate volumes. The method was tested on two ex vivo sheep kidneys embedded in gelatin phantoms. Four different scanning protocols were tested: transverse linear, transverse fan, longitudinal linear and longitudinal fan. The estimated renal volumes were compared with those obtained using the water displacement method, the ellipsoidal method and computed tomography imaging. The water displacement method yielded mean volumes of 66.00 and 66.20 mL for kidneys 1 and 2, respectively (ground truth). Freehand ultrasound scans produced mean volumes of 64.08 mL (2.90% error) and 65.25 mL (1.40% error); the ellipsoidal method yielded volumes of 57.49 mL (12.90% error) and 60.15 mL (9.13% error); and computed tomography yielded a volume of 63.00 mL (4.54% error).

[18F]-Alfatide PET imaging of integrin αvβ3 for the non-invasive quantification of liver fibrosis

BACKGROUND & AIMS

The vitronectin receptor integrin αvβ3 drives fibrogenic activation of hepatic stellate cells (HSCs). Molecular imaging targeting the integrin αvβ3 could provide a non-invasive method for evaluating the expression and the function of the integrin αvβ3 on activated HSCs (aHSCs) in the injured liver. In this study, we sought to compare differences in the uptake of [18F]-Alfatide between normal and injured liver to evaluate its utility for assessment of hepatic fibrogenesis.

METHODS

PET with [18F]-Alfatide, non-enhanced CT, histopathology, immunofluorescence staining, immunoblotting and gene analysis were performed to evaluate and quantify hepatic integrin αvβ3 levels and liver fibrosis progression in mouse models of fibrosis (carbon tetrachloride [CCl4] and bile duct ligation [BDL]). The liver AUC divided by the blood AUC over 30 min was used as an integrin αvβ3-PET index to quantify fibrosis progression. Ex vivo analysis of frozen liver tissue from patients with fibrosis and cirrhosis verified the animal findings.

RESULTS

Fibrotic mouse livers showed enhanced [18F]-Alfatide uptake and retention compared to control livers. The radiotracer was demonstrated to bind specifically with integrin αvβ3, which is mainly expressed on aHSCs. Autoradiography and histopathology confirmed the PET imaging results. Further, the mRNA and protein level of integrin αvβ3 and its signaling complex were higher in CCl4 and BDL models than controls. The results obtained from analyses on human fibrotic liver sections supported the animal findings.

CONCLUSIONS

Imaging hepatic integrin αvβ3 with PET and [18F]-Alfatide offers a potential non-invasive method for monitoring the progression of liver fibrosis.

LAY SUMMARY

Integrin αvβ3 expression on activated hepatic stellate cells (aHSCs) is associated with HSC proliferation during hepatic fibrogenesis. Herein, we show that a radioactive tracer, [18F]-Alfatide, binds to integrin αvβ3 with high affinity and specificity. [18F]-Alfatide could thus be used as a non-invasive imaging biomarker to track hepatic fibrosis progression.

Magnetic resonance imaging/transrectal ultrasonography fusion guided seed placement in a phantom: Accuracy between 2-seed versus 1-seed strategies

PURPOSE

To investigate whether the 2-seed placement per Magnetic Resonance Imaging (MRI) suspicious lesion yields a higher seed placement accuracy than a 1-seed strategy on a phantom.

METHODS

Eight olives embedded in gelatin, each simulating a prostate, underwent MRI. Three virtual spherical lesions (3, 5, and 8 mm diameters) were marked in each olive on the MRI images and co-registered to the MRI/Transrectal Ultrasonography (TRUS) fusion biopsy system. Two radiologists placed 0.5 mm fiducials, targeting the center of each virtual lesion under fusion image guidance. Half of the 8 olives in each phantom were assigned either to the 1-seed or 2-seeds per lesion strategy. Post-procedure Computed Tomography (CT) images identified each seed and were fused with MR to localize each virtual lesion and collected the seed placement error - distance between the virtual target and the corresponding seed (using the closer seed for the 2-seed strategy). Seed placement success is defined as fiducial placement within a lesion boundary.

RESULTS

Each operator repeated the procedure on three different phantoms, and data from 209 seeds placed for 137 lesions were analyzed, with an overall error of 3.03 ± 1.52 mm. The operator skill, operator phantom procedural experience, lesion size, and number of seeds, were independently associated with the seed placement error. Seed placement success rate was higher for the 2-seed group compared to 1-seed, although the difference was not statistically significant.

CONCLUSIONS

Placing 2 seeds per MRI lesion yielded a significantly lower error compared to 1-seed strategy, although seed placement success rate was not significantly different.

Abdominal Imaging Findings in COVID-19: Preliminary Observations

Background

Angiotensin-converting enzyme 2, a target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), demonstrates its highest surface expression in the lung, small bowel, and vasculature, suggesting abdominal viscera may be susceptible to injury.

Purpose

To report abdominal imaging findings in patients with coronavirus disease 2019.

Materials and Methods

In this retrospective cross-sectional study, patients consecutively admitted to a single quaternary care center from March 27 to April 10, 2020, who tested positive for SARS-CoV-2 were included. Abdominal imaging studies performed in these patients were reviewed, and salient findings were recorded. Medical records were reviewed for clinical data. Univariable analysis and logistic regression were performed.

Results

A total of 412 patients (average age, 57 years; range, 18 to >90 years; 241 men, 171 women) were evaluated. A total of 224 abdominal imaging studies were performed (radiography, n = 137; US, n = 44; CT, n = 42; MRI, n = 1) in 134 patients (33%). Abdominal imaging was associated with age (odds ratio [OR], 1.03 per year of increase; P = .001) and intensive care unit (ICU) admission (OR, 17.3; P < .001). Bowel-wall abnormalities were seen on 31% of CT images (13 of 42) and were associated with ICU admission (OR, 15.5; P = .01). Bowel findings included pneumatosis or portal venous gas, seen on 20% of CT images obtained in patients in the ICU (four of 20). Surgical correlation (n = 4) revealed unusual yellow discoloration of the bowel (n = 3) and bowel infarction (n = 2). Pathologic findings revealed ischemic enteritis with patchy necrosis and fibrin thrombi in arterioles (n = 2). Right upper quadrant US examinations were mostly performed because of liver laboratory findings (87%, 32 of 37), and 54% (20 of 37) revealed a dilated sludge-filled gallbladder, suggestive of bile stasis. Patients with a cholecystostomy tube placed (n = 4) had negative bacterial cultures.

Conclusion

Bowel abnormalities and gallbladder bile stasis were common findings on abdominal images of patients with coronavirus disease 2019. Patients who underwent laparotomy often had ischemia, possibly due to small-vessel thrombosis.

© RSNA, 2020

Differentiation of regenerative nodule, dysplastic nodule, and small hepatocellular carcinoma in cirrhotic patients: a contrast-enhanced ultrasound-based multivariable model analysis

OBJECTIVE

To develop a contrast-enhanced ultrasound (CEUS)-based model for differentiating cirrhotic liver lesions and for active surveillance of hepatocellular carcinoma (HCC).

METHODS

Patients with focal liver lesions (FLLs) with biopsy/resection-proven pathology and pre-procedure CEUS were enrolled from our institution between January 2011 and November 2014. Univariable and multivariable regression models were constructed using qualitative CEUS features and/or contrast arrival time ratio (CAT_R). The optimism-adjusted Harrell's generalized concordance index (C_H) was used to quantify the discriminatory ability of each CEUS feature and model.

RESULTS

A total of 149 patients (113 men and 36 women) with 162 FLLs were enrolled with mean age 53.4 ± 12.7 years. A 0.1-unit reduction in CAT_R was associated with a 68% increase in the odds of having a higher nodule ranking (RN < DN < small HCC) (OR, 0.32; 95% CI, 0.20-0.50, p < .001). Arterial phase hypoenhancement and isoenhancement were inversely associated with a higher nodule ranking compared to hyperenhancement. Late-phase isoenhancement was associated with lower odds of a higher nodule ranking. The CEUS + CAT_R model (C_H 0.92, 0.89-0.95) provided greater discriminatory ability when compared to the CAT_R model (ΔC_H 0.09, 0.04-0.13, p < .001) and the CEUS model (ΔC_H 0.03, 0.01-0.05, p = .02).

CONCLUSIONS

Our results provide preliminary evidence that multivariable regression model constructed using both qualitative CEUS features and CAT_R provides the greatest discriminatory ability to differentiate RN, DN, and small HCC in patients with cirrhosis, and might allow for active surveillance of the progression of cirrhotic liver lesions.

KEY POINTS

• Proportional odds logistic regression models based on qualitative CEUS features and/or CAT_R can be used for differentiating cirrhotic liver lesions and for active surveillance of HCC. • The reduction of CAT_R (RN < DN < small HCC) was strongly associated with high-risk cirrhotic liver nodules. • Inclusion of CAT_R in the CEUS prediction model significantly improved its performance for cirrhotic liver lesions risk-stratification.

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.

Deep Learning-Based Automatic Endometrium Segmentation and Thickness Measurement for 2D Transvaginal Ultrasound

Endometrial thickness is closely related to gyneco-logical function and is an important biomarker in transvaginal ultrasound (TVUS) examinations for assessing female reproductive health. Manual measurement is time-consuming and subject to high inter- and intra- observer variability. In this paper, we present a fully automated endometrial thickness measurement method using deep learning. Our pipeline consists of: 1) endometrium segmentation using a VGG-based U-Net, and 2) endometrial thickness estimation using medial axis transformation. We conducted experimental studies on 137 2D TVUS cases (74/63 secretory phase/proliferative phase). On a test set of 27 cases/277 images, the segmentation Dice score is 0.83. For thickness measurement, we achieved mean absolute error of 1.23/1.38 mm and root mean squared error of 1.79/1.85 mm on two different test sets. The results are considered well within the clinically acceptable range of ±2 mm. Furthermore, our phase-stratified analysis shows that the measurement variance from the secretory phase is higher than that from the proliferative phase, largely due to the high variability of the endometrium appearance in the secretory phase. Future work will extend our current algorithm toward different clinical outcomes for a broader spectrum of clinical applications.

Liver fibrosis imaging: A clinical review of ultrasound and magnetic resonance elastography

Liver fibrosis is a histological hallmark of most chronic liver diseases, which can progress to cirrhosis and liver failure, and predisposes to hepatocellular carcinoma. Accurate diagnosis of liver fibrosis is necessary for prognosis, risk stratification, and treatment decision-making. Liver biopsy, the reference standard for assessing liver fibrosis, is invasive, costly, and impractical for surveillance and treatment response monitoring. Elastography offers a noninvasive, objective, and quantitative alternative to liver biopsy. This article discusses the need for noninvasive assessment of liver fibrosis and reviews the comparative advantages and limitations of ultrasound and magnetic resonance elastography techniques with respect to their basic concepts, acquisition, processing, and diagnostic performance. Variations in clinical contexts of use and common pitfalls associated with each technique are considered. In addition, current challenges and future directions to improve the diagnostic accuracy and clinical utility of elastography techniques are discussed. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:25-42.

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.

Predictive Value of Duplex Ultrasound for Significant In-Stent Restenosis after Percutaneous Transluminal Renal Artery Stent Placement: A Propensity Score Matching Analysis

To evaluate the value of pre-stenting and early post-stenting (<1 mo) duplex ultrasound parameters in predicting significant in-stent restenosis (ISR), we matched significant ISR patients 1:1 with controls without ISR in pre-stenting and early post-stenting (<1 mo) periods, respectively, using propensity score matching. Duplex ultrasound parameters, such as renal length difference between non-lesion side and lesion side within patient, trans-lesion peak systolic velocity and renal aortic ratio, were compared between cases and controls, and the area under the receiver operating characteristic curve (AUROC) was charted to predict ISR. After propensity score matching, 28 cases were matched in the pre-stenting period and 16 cases in the early post-stenting time period. Pre-stenting renal length difference, early post-stenting peak systolic velocity and renal aortic ratio showed significant differences in case-control comparisons. Early post-stenting peak systolic velocity (AUROC: 0.826, cutoff: 141 cm/s) and renal aortic ratio (AUROC: 0.770, cutoff: 1.75) performed well in predicting significant ISR and may serve as non-invasive markers in ISR surveillance.

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.

Imaging-Related Risk Factors for Bleeding Complications of US-Guided Native Renal Biopsy: A Propensity Score Matching Analysis

PURPOSE

To evaluate imaging-related hemorrhagic risk factors for ultrasound (US)-guided native kidney biopsy.

MATERIALS AND METHODS

A retrospective review was conducted of adult patients who underwent US-guided native kidney biopsy at a single center between January 2006 and March 2016 and identified 37 of 551 patients (6.72%) with postbiopsy bleeding complications, including 11 major complications (2.00%; n = 11) and 26 minor complications (4.72%; n = 26). Ten patients with major complications and 20 with minor complications were matched with 20 control subjects each by propensity score matching based on age, needle size, number of cores, blood pressure, partial thromboplastin time, prothrombin time, platelet count, and estimated glomerular filtration rate.

RESULTS

Biopsy needle passing through the renal sinus was identified in the patients with major (6 of 10; 60%) and minor complications (8 of 20; 40.0%) but not in the control groups. For patients with major complications, the needle-sinus distance was significantly shorter (5.11 mm ± 7.32 vs 11.14 mm ± 3.54; P = .023) and the needle-capsule distance was significantly longer (17.52 mm ± 8.04 vs 9.28 mm ± 3.29; P = .0004) than in control subjects. The bimodal distribution of cortical tangential angles (< 30° or ≥ 60°) in minor complication cases (17 of 20; 85.0%) was significantly greater than in the control group (8 of 20; 40.0%; odds ratio = 8.50; P = .004).

CONCLUSIONS

This study identifies imaging risk factors in US-guided native kidney biopsy and recommends an algorithm to manage them, including appropriate needle path position between the renal capsule and sinus and proper needle cortical tangential angle.

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.

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.

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.

Contrast-Enhanced Ultrasound to Quantifyc Perfusion in a Machine-Perfused Pig Liver

This paper introduces a non-invasive, contrastenhanced ultrasound (CEUS) infusion method to quantify the health of viable donor livers. The method uses the infusion of microbubbles and their destruction and subsequent replenishment to measure the perfusion rate in the liver microvasculature. The proposed method improves on the previous parameter extraction approaches applied to the flashreplenishment technique by addressing the effects of the microbubble mixing within the perfusate bath and destruction rate. By doing so, the tissue perfusion rate can be extracted from the data even though the microbubble concentration is not constant throughout image acquisition. The measured changes in the tissue perfusion rate showed that CEUS infusion is a viable biomarker for assessing liver health.

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.

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.

Current Applications and Future Impact of Machine Learning in Radiology

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

The Role of Imaging in Prostate Cancer Care Pathway: Novel Approaches to Urologic Management Challenges Along 10 Imaging Touch Points

We map out a typical prostate cancer care pathway through discussion of updates on modern imaging. Multiparametric magnetic resonance imaging is the most sensitive and specific imaging tool for diagnosis and local staging, but transrectal ultrasound remains the most widely used technique for prostate biopsy guidance. Computed tomography and bone scan are useful in initial staging and recurrence detection. Novel imaging techniques in ultrasound elastography and multiparametric magnetic resonance imaging allow for increased lesion detection sensitivity and have the potential to enhance biopsy, while the development of new positron emission tomography radiotracers has great promise for improved detection of local and metastatic disease in patients with biochemical recurrence.

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.

Essentials of Statistical Methods for Assessing Reliability and Agreement in Quantitative Imaging

Quantitative imaging is increasing in almost all fields of radiological science. Modern quantitative imaging biomarkers measure complex parameters including metabolism, tissue microenvironment, tissue chemical properties or physical properties. In this paper, we focus on measurement reliability assessment in quantitative imaging. We review essential concepts related to measurement such as measurement variability and measurement error. We also discuss reliability study methods for intraobserver and interobserver variability, and the applicable statistical tests including: intraclass correlation coefficient, Pearson correlation coefficient, and Bland-Altman graphs and limits of agreement, standard error of measurement, and coefficient of variation.

Imaging and Screening of Thyroid Cancer

Ultrasound is the first-line diagnostic tool for diagnosis of thyroid diseases. The low aggressiveness of many thyroid cancers coupled with high sensitivity of sonography can lead to cancer diagnosis and treatment with no effect on outcomes. Ultrasound is recognized as the most important driver of thyroid cancer overdiagnosis. Ultrasound should not be used as a general screening tool and should be reserved for patients at high risk of thyroid cancer and in the diagnostic management of incidentally discovered thyroid nodules. With prescreening risk stratification and application of consensus criteria for nodule biopsy, the value of the diagnostic ultrasound can be maximized.

Non-invasive diagnosis of non-alcoholic fatty liver disease (NAFLD) using ultrasound image echogenicity

This paper introduces a non-invasive, quantitative technique to diagnose the progression of non-alcoholic fatty liver disease (NAFLD). The method is predicated on two fundamental principles: 1) the speed of sound in a fatty liver is lower than that in a healthy liver and 2) the quality of an ultrasound image is maximized when the beamformer's speed of sound matches the true speed of sound in the tissue being examined. The proposed method uses the echogenicity of an ultrasound image as a quantitative measure to estimate the true speed of sound within the liver parenchyma and capture its correlation with the underlying fat content. The proposed technique was evaluated in simulations and then tested ex vivo on sheep liver, mice liver (healthy and fatty) and tissue-mimicking phantoms. In the case of the phantom and sheep liver, the method was able to estimate the true speed of sound with errors of less than 0.5%; in the case of the mice livers, the method was able to accurately estimate the speed of sound within the livers (less than 1% error) and capture the correlation between fat content and speed of sound. Thereby, demonstrating the capability of ultrasound technology to non-invasively, quantitatively, and accurately diagnose NAFLD at point of care.

Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel

Hepatic fibrosis is potentially reversible; however early diagnosis is necessary for treatment in order to halt progression to cirrhosis and development of complications including portal hypertension and hepatocellular carcinoma. Morphologic signs of cirrhosis on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) alone are unreliable and are seen with more advanced disease. Newer imaging techniques to diagnose liver fibrosis are reliable and accurate, and include magnetic resonance elastography and US elastography (one-dimensional transient elastography and point shear wave elastography or acoustic radiation force impulse imaging). Research is ongoing with multiple other techniques for the noninvasive diagnosis of hepatic fibrosis, including MRI with diffusion-weighted imaging, hepatobiliary contrast enhancement, and perfusion; CT using perfusion, fractional extracellular space techniques, and dual-energy, contrast-enhanced US, texture analysis in multiple modalities, quantitative mapping, and direct molecular imaging probes. Efforts to advance the noninvasive imaging assessment of hepatic fibrosis will facilitate earlier diagnosis and improve patient monitoring with the goal of preventing the progression to cirrhosis and its complications.

Validation of Shear Wave Elastography Cutoff Values on the Supersonic Aixplorer for Practical Clinical Use in Liver Fibrosis Staging

The purpose of this study was to determine the validity of previously established ultrasound shear wave elastography (SWE) cut-off values (≥F2 fibrosis) on an independent cohort of patients with chronic liver disease. In this cross-sectional study, approved by the institutional review board and compliant with the Health Insurance Portability and Accountability Act, 338 patients undergoing liver biopsy underwent SWE using an Aixplorer ultrasound machine (SuperSonic Imagine, Aix-en-Provence, France). Median SWE values were calculated from sets of 10 elastograms. A single blinded pathologist evaluated METAVIR fibrosis staging as the gold standard. The study analyzed 277 patients with a mean age of 48 y. On pathologic examination, 212 patients (76.5%) had F0-F1 fibrosis, whereas 65 (23.5%) had ≥F2 fibrosis. Spearman's correlation of fibrosis with SWE was 0.456 (p < 0.001). A cut-off value of 7.29 kPa yielded sensitivity of 95.4% and specificity of 50.5% for the diagnosis of METAVIR stage ≥F2 liver fibrosis in patients with liver disease using the SuperSonic Imagine Aixplorer SWE system.

Aorta-Lesion-Attenuation-Difference (ALAD) on contrast-enhanced CT: a potential imaging biomarker for differentiating malignant from benign oncocytic neoplasms

OBJECTIVE

To evaluate whether the Aorta-Lesion-Attenuation-Difference on contrast-enhanced CT can aid in the differentiation of malignant and benign oncocytic renal neoplasms.

MATERIALS AND METHODS

Two independent cohorts-an initial (biopsy) dataset and a validation (surgical) dataset-with oncocytomas and chromophobe renal cell carcinomas (chRCC) were included in this IRB-approved retrospective study. A region of interest was placed on the renal mass and abdominal aorta on the same CT image slice to calculate an Aorta-Lesion-Attenuation-Difference (ALAD). ROC curves were plotted for different enhancement phases, and diagnostic performance of ALAD for differentiating chRCC from oncocytomas was calculated.

RESULTS

Seventy-nine renal masses (56 oncocytomas, 23 chRCC) were analyzed in the initial (biopsy) dataset. Thirty-six renal masses (16 oncocytomas, 20 chRCC) were reviewed in the validation (surgical) cohort. ALAD showed a statistically significant difference between oncocytomas and chromophobes during the nephrographic phase (p < 0.001), early excretory phase (p < 0.001), and excretory phase (p = 0.029). The area under the ROC curve for the nephrographic phase was 1.00 (95% CI: 1.00-1.00) for the biopsy dataset and showed the narrowest confidence interval. At a threshold value of 25.5 HU, sensitivity was 100 (82.2%-100%) and specificity was 81.5 (61.9%-93.7%). When tested on the validation dataset on measurements made by an independent reader, the AUROC was 0.93 (95% CI: 0.84-1.00) with a sensitivity of 100 (80.0%-100%) and a specificity of 87.5 (60.4%-97.8%).

CONCLUSIONS

Nephrographic phase ALAD has potential to differentiate benign and malignant oncocytic renal neoplasms on contrast-enhanced CT if histologic evaluation on biopsy is indeterminate.