Rotational 3D shear wave elasticity imaging: Effect of knee flexion on 3D shear wave propagation in in vivo vastus lateralis.

Skeletal muscle is a complex tissue, exhibiting not only direction-dependent material properties (commonly modeled as a transversely isotropic material), but also changes in observed material properties due to factors such as contraction and passive stretch. In this work, we evaluated the effect of muscle passive stretch on shear wave propagation along and across the muscle fibers using a rotational 3D shear wave elasticity imaging system and automatic analysis methods. We imaged the vastus lateralis of 10 healthy volunteers, modulating passive stretch by imaging at 8 different knee flexion angles (controlled by a BioDex system). In addition to demonstrating the ability of this acquisition and automatic processing system to estimate muscle shear moduli over a range of values, we evaluated potential higher order biomarkers for muscle health that capture the change in muscle stiffness along and across the fibers with changing knee flexion. The median within-subject variability of these biomarkers is found to be <16%, suggesting promise as a repeatable clinical metric. Additionally, we report an unexpected observation: that shear wave signal amplitude along the fibers increases with increasing flexion and muscle stiffness, which is not predicted by transversely isotropic (TI) material simulations. This observation may point to an additional potential biomarker for muscle health or inform other material modeling choices for muscle.

A Hybrid Deep Learning Approach to Identify Preventable Childhood Hearing Loss.

OBJECTIVE: Childhood hearing loss has well-known, lifelong consequences. Infection-related hearing loss disproportionately affects underserved communities yet can be prevented with early identification and treatment. This study evaluates the utility of machine learning in automating tympanogram classifications of the middle ear to facilitate layperson-guided tympanometry in resource-constrained communities. DESIGN: Diagnostic performance of a hybrid deep learning model for classifying narrow-band tympanometry tracings was evaluated. Using 10-fold cross-validation, a machine learning model was trained and evaluated on 4810 pairs of tympanometry tracings acquired by an audiologist and layperson. The model was trained to classify tracings into types A (normal), B (effusion or perforation), and C (retraction), with the audiologist interpretation serving as reference standard. Tympanometry data were collected from 1635 children from October 10, 2017, to March 28, 2019, from two previous cluster-randomized hearing screening trials (NCT03309553, NCT03662256). Participants were school-aged children from an underserved population in rural Alaska with a high prevalence of infection-related hearing loss. Two-level classification performance statistics were calculated by treating type A as pass and types B and C as refer. RESULTS: For layperson-acquired data, the machine-learning model achieved a sensitivity of 95.2% (93.3, 97.1), specificity of 92.3% (91.5, 93.1), and area under curve of 0.968 (0.955, 0.978). The model's sensitivity was greater than that of the tympanometer's built-in classifier [79.2% (75.5, 82.8)] and a decision tree based on clinically recommended normative values [56.9% (52.4, 61.3)]. For audiologist-acquired data, the model achieved a higher AUC of 0.987 (0.980, 0.993), had an equivalent sensitivity of 95.2 (93.3, 97.1), and a higher specificity of 97.7 (97.3, 98.2). CONCLUSIONS: Machine learning can detect middle ear disease with comparable performance to an audiologist using tympanograms acquired either by an audiologist or a layperson. Automated classification enables the use of layperson-guided tympanometry in hearing screening programs in rural and underserved communities, where early detection of treatable pathology in children is crucial to prevent the lifelong adverse effects of childhood hearing loss.

Evaluation of Myocardial Stiffness in Cardiac Amyloidosis Using Acoustic Radiation Force Impulse and Natural Shear Wave Imaging.

OBJECTIVE: Increased myocardial stiffness (MS) is an important hallmark of cardiac amyloidosis (CA) caused by myocardial amyloid deposition. Standard echocardiography metrics assess MS indirectly via downstream effects of cardiac stiffening. The ultrasound elastography methods acoustic radiation force impulse (ARFI) and natural shear wave (NSW) imaging assess MS more directly. METHODS: This study compared MS in 12 healthy volunteers and 13 patients with confirmed CA using ARFI and NSW imaging. Parasternal long-axis acquisitions of the interventricular septum were obtained using a modified Acuson Sequoia scanner and a 5V1 transducer. ARFI-induced displacements were measured through the cardiac cycle, and ratios of diastolic-over-systolic displacement were calculated. NSW speeds from aortic valve closure were extracted from echocardiography-tracked displacement data. RESULTS: ARFI stiffness ratios were significantly lower in CA patients than controls (mean ± standard deviation: 1.47 ± 0.27 vs. 2.10 ± 0.47, p < 0.001), and NSW speeds were significantly higher in CA patients than controls (5.58 ± 1.10 m/s vs. 3.79 ± 1.10 m/s, p < 0.001). A linear combination of the two metrics exhibited greater diagnostic potential than either metric alone (area under the curve = 0.97 vs. 0.89 and 0.88). CONCLUSION: MS was measured to be significantly higher in CA patients using both ARFI and NSW imaging. Together, these methods have potential utility to aid in clinical diagnosis of diastolic dysfunction and infiltrative cardiomyopathies.

Repeatability of Rotational 3-D Shear Wave Elasticity Imaging Measurements in Skeletal Muscle.

Shear wave elasticity imaging (SWEI) usually assumes an isotropic material; however, skeletal muscle is typically modeled as a transversely isotropic material with independent shear wave speeds in the directions along and across the muscle fibers. To capture these direction-dependent properties, we implemented a rotational 3-D SWEI system that measures the shear wave speed both along and across the fibers in a single 3-D acquisition, with automatic detection of the muscle fiber orientation. We tested and examined the repeatability of this system's measurements in the vastus lateralis of 10 healthy volunteers. The average coefficient of variation of the measurements from this 3-D SWEI system was 5.3% along the fibers and 8.1% across the fibers. When compared with estimated respective 2-D SWEI values of 16.0% and 83.4%, these results suggest using 3-D SWEI has the potential to improve the precision of SWEI measurements in muscle. Additionally, we observed no significant difference in shear wave speed between the dominant and non-dominant legs along (p = 0.26) or across (p = 0.65) the muscle fibers.

Quantifying the Impact of Imaging Through Body Walls on Shear Wave Elasticity Measurements.

In the context of ultrasonic hepatic shear wave elasticity imaging (SWEI), measurement success has been determined to increase when using elevated acoustic output pressures. As SWEI sequences consist of two distinct operations (pushing and tracking), acquisition failures could be attributed to (i) insufficient acoustic radiation force generation resulting in inadequate shear wave amplitude and/or (ii) distorted ultrasonic tissue motion tracking. In the study described here, an opposing window experimental setup that isolated body wall effects separately between the push and track SWEI operations was implemented. A commonly employed commercial track configuration was used, harmonic multiple-track-location SWEI. The effects of imaging through body walls on the pushing and tracking operations of SWEI as a function of mechanical index (MI), spanning 5 different push beam MIs and 10 track beam MIs, were independently assessed using porcine body walls. Shear wave speed yield was found to increase with both increasing push and track MI. Although not consistent across all samples, measurements in a subset of body walls were found to be signal limited during tracking and to increase yield by up to 35% when increasing electronic signal-to-noise ratio by increasing harmonic track transmit pressure.

Parametric Analysis of SV Mode Shear Waves in Transversely Isotropic Materials Using Ultrasonic Rotational 3-D SWEI.

Ultrasonic rotational 3-D shear wave elasticity imaging (SWEI) has been used to induce and evaluate multiple shear wave modes, including both the shear horizontal (SH) and shear vertical (SV) modes in in vivo muscle. Observations of both the SH and SV modes allow the muscle to be characterized as an elastic, incompressible, transversely isotropic (ITI) material with three parameters: the longitudinal shear modulus µL , the transverse shear modulus µT , and the tensile anisotropy χE . Measurement of the SV wave is necessary to characterize χE , but the factors that influence SV mode generation and characterization with ultrasonic SWEI are complicated. This work uses Green's function (GF) simulations to perform a parametric analysis to determine the optimal interrogation parameters to facilitate visualization and quantification of SV mode shear waves in muscle. We evaluate the impact of five factors: µL , µT , χE , fiber tilt angle [Formula: see text], and F-number of the push geometry on SV mode speed, amplitude, and rotational distribution. These analyses demonstrate that the following hold: 1) as µL increases, SV waves decrease in amplitude so are more difficult to measure in SWEI imaging; 2) as µT increases, the SV wave speeds increase; 3) as χE increases, the SV waves increase in speed and separate from the SH waves; 4) as fiber tilt angle [Formula: see text] increases, the measurable SV waves remain approximately the same speed, but change in strength and in rotational distribution; and 5) as the push beam geometry changes with F-number, the measurable SV waves remain approximately the same speed, but change in strength and rotational distribution. While specific SV mode speeds depend on the combinations of all parameters considered, measurable SV waves can be generated and characterized across the range of parameters considered. To maximize measurable SV waves separate from the SH waves, it is recommended to use an F/1 push geometry and [Formula: see text].

Full Characterization of in vivo Muscle as an Elastic, Incompressible, Transversely Isotropic Material Using Ultrasonic Rotational 3D Shear Wave Elasticity Imaging.

Using a 3D rotational shear wave elasticity imaging (SWEI) setup, 3D shear wave data were acquired in the vastus lateralis of a healthy volunteer. The innate tilt between the transducer face and the muscle fibers results in the excitation of multiple shear wave modes, allowing for more complete characterization of muscle as an elastic, incompressible, transversely isotropic (ITI) material. The ability to measure both the shear vertical (SV) and shear horizontal (SH) wave speed allows for measurement of three independent parameters needed for full ITI material characterization: the longitudinal shear modulus µL , the transverse shear modulus µT , and the tensile anisotropy χE . Herein we develop and validate methodology to estimate these parameters and measure them in vivo, with µL = 5.77±1.00 kPa, µT = 1.93±0.41 kPa (giving shear anisotropy χµ = 2.11±0.92 ), and χE = 4.67±1.40 in a relaxed vastus lateralis muscle. We also demonstrate that 3D SWEI can be used to more accurately characterize muscle mechanical properties as compared to 2D SWEI.

Semi-automated weak annotation for deep neural network skin thickness measurement.

Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin thickness on ultrasound images. Here, we also present a framework for weakly annotating an unlabeled dataset in a time-effective manner to train the deep neural network. Segmentation labels for training were proposed using the thresholding method and validated with visual inspection by a human expert reader. We reduced decision ambiguity by only inspecting segmentations at the center A-line. This weak annotation approach facilitated validation of over 1000 segmentation labels in 2 hours. A lightweight deep neural network that segments entire 2D images was designed and trained on this weakly-labeled dataset. Averaged over six folds of cross-validation, segmentation accuracy was 57% for the thresholding method and 78% for the neural network. In particular, the network was better at finding the distal skin margin, which is the primary challenge for skin segmentation. Both algorithms have been made publicly available to aid future applications in skin characterization and elastography.

Regulation of SRF protein stability by an autophagy-dependent pathway.

Serum response factor (SRF), a key transcription factor, plays an important role in regulating cell functions such as proliferation and differentiation. Most proteins are unstable, and protein stability is regulated through the ubiquitin-proteasome system (UPS) or the autophagy lysosome pathway (ALP). Whether SRF is degraded and what mechanisms control SRF protein stability remain unexplored. Western blot analyses of cells treated with cycloheximide (CHX), a protein synthesis inhibitor, showed that SRF was degraded in a time-dependent manner. Moreover, we observed that SRF undergoes autophagy-dependent destruction, which is accelerated by serum deprivation. Through bioinformatics screening, we found that SRF contains the GSK3ß phosphorylation motif (T/SPPXS): SPDSPPRSDPT, which is conserved from zebrafish to humans. Serum deprivation stimulated GSK3ß activation that then potentiates SRF degradation through the autophagy lysosome pathway. Since SRF is important for numerous cellular activities, our results suggest that the autophagy-dependent SRF degradation pathway may provide a new avenue to modulate SRF-mediated cell functions.

PECAM1 regulates flow-mediated Gab1 tyrosine phosphorylation and signaling.

Endothelial dysfunction, characterized by impaired activation of endothelial nitric oxide (NO) synthase (eNOS) and ensued decrease of NO production, is a common mechanism of various cardiovascular pathologies, including hypertension and atherosclerosis. Laminar blood flow-mediated specific signaling cascades modulate vascular endothelial cells (ECs) structure and functions. We have previously shown that flow-stimulated Gab1 (Grb2-associated binder-1) tyrosine phosphorylation mediates eNOS activation in ECs, which in part confers laminar flow atheroprotective action. However, the molecular mechanisms whereby flow regulates Gab1 tyrosine phosphorylation and its downstream signaling events remain unclear. Here we show that platelet endothelial cell adhesion molecule-1 (PECAM1), a key molecule in an endothelial mechanosensing complex, specifically mediates Gab1 tyrosine phosphorylation and its downstream Akt and eNOS activation in ECs upon flow rather than hepatocyte growth factor (HGF) stimulation. Small interfering RNA (siRNA) targeting PECAM1 abolished flow- but not HGF-induced Gab1 tyrosine phosphorylation and Akt, eNOS activation as well as Gab1 membrane translocation. Protein-tyrosine phosphatase SHP2, which has been shown to interact with Gab1, was involved in flow signaling and HGF signaling, as SHP2 siRNA diminished the flow- and HGF-induced Gab1 tyrosine phosphorylation, membrane localization and downstream signaling. Pharmacological inhibition of PI3K decreased flow-, but not HGF-mediated Gab1 phosphorylation and membrane localization as well as eNOS activation. Finally, we observed that flow-mediated Gab1 and eNOS phosphorylation in vivo induced by voluntary wheel running was reduced in PECAM1 knockout mice. These results demonstrate a specific role of PECAM1 in flow-mediated Gab1 tyrosine phosphorylation and eNOS signaling in ECs.