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Given its real-time capability to quantify mechanical tissue properties, ultrasound shear wave elastography holds significant promise in clinical musculoskeletal imaging. However, existing shear wave elastography methods fall short in enabling full-limb analysis of 3D anatomical structures under diverse loading conditions, and may introduce measurement bias due to sonographer-applied force on the transducer. These limitations pose numerous challenges, particularly for 3D computational biomechanical tissue modeling in areas like prosthetic socket design. In this feasibility study, a clinical linear ultrasound transducer system with integrated shear wave elastography capabilities was utilized to scan both a calibrated phantom and human limbs in a water tank imaging setup. By conducting 2D and 3D scans under varying compressive loads, this study demonstrates the feasibility of volumetric ultrasound shear wave elastography of human limbs. Our preliminary results showcase a potential method for evaluating 3D spatially varying tissue properties, offering future extensions to computational biomechanical modeling of tissue for various clinical scenarios.
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Diagnóstico por Imagen de Elasticidad , Estudios de Factibilidad , Imagenología Tridimensional , Fantasmas de Imagen , Diagnóstico por Imagen de Elasticidad/métodos , Humanos , Imagenología Tridimensional/métodosRESUMEN
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.
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Diagnóstico por Imagen de Elasticidad , Nódulo Tiroideo , Elasticidad , Femenino , Humanos , Masculino , Nódulo Tiroideo/diagnóstico por imagenRESUMEN
INTRODUCTION: Muscle echo intensity has been shown to correlate with disease status in muscle disorders, including Duchenne muscular dystrophy (DMD). We report the effect of sonographer-applied load on measurements of muscle echo intensity. METHODS: Quadriceps ultrasound scans were performed on 22 healthy boys and 16 boys with DMD between the ages of 2.2 and 15.3 years. Transducer contact force was increased linearly from 1.5 to 10 N, and echo intensity was measured throughout. RESULTS: Echo intensity increased linearly with strain at a rate of 42 (95% confidence interval [CI]: 21-63) and 74 (95% CI: 49-98) in the healthy and DMD populations, respectively. Echo intensity reliability was moderate at low strain (intraclass correlation coefficient [ICC] = 0.82) and was improved at high strain (ICC = 0.92). DISCUSSION: Sonographer-applied load introduces error in measurements of echo intensity, but it can be minimized by measuring echo intensity at near-maximal levels of compression. Muscle Nerve 57: 423-429, 2018.
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Distrofia Muscular de Duchenne/diagnóstico por imagen , Músculo Cuádriceps/diagnóstico por imagen , Ultrasonografía/métodos , Adolescente , Niño , Preescolar , Humanos , Masculino , Presión , Reproducibilidad de los ResultadosRESUMEN
OBJECTIVES: The purpose of this study was to investigate the ability of quantitative ultrasound (US) using edge detection analysis to assess patients with Duchenne muscular dystrophy (DMD). METHODS: After Institutional Review Board approval, US examinations with fixed technical parameters were performed unilaterally in 6 muscles (biceps, deltoid, wrist flexors, quadriceps, medial gastrocnemius, and tibialis anterior) in 19 boys with DMD and 21 age-matched control participants. The muscles of interest were outlined by a tracing tool, and the upper third of the muscle was used for analysis. Edge detection values for each muscle were quantified by the Canny edge detection algorithm and then normalized to the number of edge pixels in the muscle region. The edge detection values were extracted at multiple sensitivity thresholds (0.01-0.99) to determine the optimal threshold for distinguishing DMD from normal. Area under the receiver operating curve values were generated for each muscle and averaged across the 6 muscles. RESULTS: The average age in the DMD group was 8.8 years (range, 3.0-14.3 years), and the average age in the control group was 8.7 years (range, 3.4-13.5 years). For edge detection, a Canny threshold of 0.05 provided the best discrimination between DMD and normal (area under the curve, 0.96; 95% confidence interval, 0.84-1.00). According to a Mann-Whitney test, edge detection values were significantly different between DMD and controls (P < .0001). CONCLUSIONS: Quantitative US imaging using edge detection can distinguish patients with DMD from healthy controls at low Canny thresholds, at which discrimination of small structures is best. Edge detection by itself or in combination with other tests can potentially serve as a useful biomarker of disease progression and effectiveness of therapy in muscle disorders.
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Procesamiento de Imagen Asistido por Computador/métodos , Músculo Esquelético/diagnóstico por imagen , Músculo Esquelético/patología , Distrofia Muscular de Duchenne/diagnóstico por imagen , Distrofia Muscular de Duchenne/patología , Ultrasonografía/métodos , Adolescente , Algoritmos , Niño , Preescolar , Humanos , Masculino , Proyectos Piloto , Estudios ProspectivosRESUMEN
Ubiquitous applications in diverse fields motivate large-area sampling, super-resolution (SR) and image mosaicing. However, conventional translational sampling has drawbacks including laterally constrained variations between samples. Meanwhile, existing rotational sampling methods do not consider the uniformity of sampling points in Cartesian coordinates, resulting in additional distortion errors in sampled images. We design a novel optimized concentric circular trajectory sampling (OCCTS) method to acquire image information uniformly at fast sampling speeds. The sampling method allows multiple low-resolution images for conventional SR algorithms to be acquired by adding small variations in the angular dimension. Experimental results demonstrate that OCCTS can beat comparable CCTS methods that lack optimized sampling densities by reducing sampling time by more than 11.5% while maintaining 50% distortion error reduction. The SR quality of OCCTS has at least 5.2% fewer distortion errors than the comparable CCTS methods. This paper is the first, to the best of our knowledge, to present an OCCTS method for SR and image mosaicing.
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OBJECTIVE: Medical ultrasound is one of the most accessible imaging modalities, but is a challenging modality for quantitative parameters comparison across vendors and sonographers. B-Mode imaging, with limited exceptions, provides a map of tissue boundaries; crucially, it does not provide diagnostically relevant physical quantities of the interior of organ domains.This can be remedied: the raw ultrasound signal carries significantly more information than is present in the B-Mode image. Specifically, the ability to recover speed-of-sound and attenuation maps from the raw ultrasound signal transforms the modality into a tissue-property modality. Deep learning was shown to be a viable tool for recovering speed-of-sound maps. A major hold-back towards deployment is the domain transfer problem, i.e., generalizing from simulations to real data. This is due in part to dependence on the (hard-to-calibrate) system response. METHODS: We explore a remedy to the problem of operator-dependent effects on the system response by introducing a novel approach utilizing the phase information of the IQ demodulated signal. RESULTS: We show that the IQ-phase information effectively decouples the operator-dependent system response from the data, significantly improving the stability of speed-of-sound recovery. We also introduce an improvement to the network topology providing faster and improved results to the state-of-the-art. We present the first publicly available benchmark for this problem: a simulated dataset for raw ultrasound plane wave processing. CONCLUSION: The consideration of the phase of the IQ-signals presents a promising appeal to traversing the transfer learning problem, advancing the goal of real-time speed-of-sound imaging.
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Benchmarking , Sonido , Ultrasonografía/métodos , Ondas Ultrasónicas , Fantasmas de ImagenRESUMEN
In this manuscript, we develop a multi-party framework tailored for multiple data contributors seeking machine learning insights from combined data sources. Grounded in statistical learning principles, we introduce the Multi-Key Homomorphic Encryption Logistic Regression (MK-HELR) algorithm, designed to execute logistic regression on encrypted multi-party data. Given that models built on aggregated datasets often demonstrate superior generalization capabilities, our approach offers data contributors the collective strength of shared data while ensuring their original data remains private due to encryption. Apart from facilitating logistic regression on combined encrypted data from diverse sources, this algorithm creates a collaborative learning environment with dynamic membership. Notably, it can seamlessly incorporate new participants during the learning process, addressing the key limitation of prior methods that demanded a predetermined number of contributors to be set before the learning process begins. This flexibility is crucial in real-world scenarios, accommodating varying data contribution timelines and unanticipated fluctuations in participant numbers, due to additions and departures. Using the AI4I public predictive maintenance dataset, we demonstrate the MK-HELR algorithm, setting the stage for further research in secure, dynamic, and collaborative multi-party learning scenarios.
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Objectives: This study aimed to compare the different respiratory rate (RR) monitoring methods used in the emergency department (ED): manual documentation, telemetry, and capnography. Methods: This is a retrospective study using recorded patient monitoring data. The study population includes patients who presented to a tertiary care ED between January 2020 and December 2022. Inclusion and exclusion criteria were patients with simultaneous recorded RR data from all three methods and less than 10 min of recording, respectively. Linear regression and Bland-Altman analysis were performed between different methods. Results: A total of 351 patient encounters met study criteria. Linear regression yielded an R-value of 0.06 (95% confidence interval [CI] 0.00-0.12) between manual documentation and telemetry, 0.07 (95% CI 0.01-0.13) between manual documentation and capnography, and 0.82 (95% CI 0.79-0.85) between telemetry and capnography. The Bland-Altman analysis yielded a bias of -0.8 (95% limits of agreement [LOA] -12.2 to 10.6) between manual documentation and telemetry, bias of -0.6 (95% LOA -13.5 to 12.3) between manual documentation and capnography, and bias of 0.2 (95% LOA -6.2 to 6.6) between telemetry and capnography. Conclusion: There is a poor correlation between manual documentation and both automated methods, while there is relatively good agreement between the automated methods. This finding highlights the need to further investigate the methodology used by the ED staff in monitoring and documenting RR and ways to improve its reliability given that many important clinical decisions are made based on these assessments.
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We estimate central venous pressure (CVP) with force-coupled ultrasound imaging of the internal jugular vein (IJV). We acquire ultrasound images while measuring force applied over the IJV by the ultrasound probe imaging surface. We record collapse force, the force required to completely occlude the vein, in 27 healthy subjects. We find supine collapse force and jugular venous pulsation height (JVP), the clinical noninvasive standard, have a linear correlation coefficient of r2 = 0.89 and an average absolute difference of 0.23 mmHg when estimating CVP. We perturb our estimate negatively by tilting 16 degrees above supine and observe decreases in collapse force for every subject which are predictable from our CVP estimates. We perturb venous pressure positively to values experienced in decompensated heart failure by having subjects perform the Valsalva maneuver while the IJV is being collapsed and observe an increase in collapse force for every subject. Finally, we derive a CVP waveform with an inverse three-dimensional finite element optimization that uses supine collapse force and segmented force-coupled ultrasound data at approximately constant force.
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Venas Yugulares , Maniobra de Valsalva , Humanos , Presión Venosa Central , Venas Yugulares/diagnóstico por imagen , Ultrasonografía/métodos , Presión VenosaRESUMEN
Finite element analysis (FEA) can be used to evaluate applied interface pressures and internal tissue strains for computational prosthetic socket design. This type of framework requires realistic patient-specific limb geometry and constitutive properties. In recent studies, indentations and inverse FEA with MRI-derived 3D patient geometries were used for constitutive parameter identification. However, long computational times and use of specialized equipment presents challenges for clinical, deployment. In this study, we present a novel approach for constitutive parameter identification using a combination of FEA, ultrasound indentation, and shear wave elastography. Local shear modulus measurement using elastography during an ultrasound indentation experiment has particular significance for biomechanical modeling of the residual limb since there are known regional dependencies of soft tissue properties such as varying levels of scarring and atrophy. Beyond prosthesis design, this work has broader implications to the fields of muscle health and monitoring of disease progression.
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Diagnóstico por Imagen de Elasticidad , Humanos , Análisis de Elementos Finitos , Diseño de Prótesis , Ultrasonografía , Progresión de la EnfermedadRESUMEN
We develop, automate and evaluate a calibration-free technique to estimate human carotid artery blood pressure from force-coupled ultrasound images. After acquiring images and force, we use peak detection to align the raw force signal with an optical flow signal derived from the images. A trained convolutional neural network selects a seed point within the carotid in a single image. We then employ a region-growing algorithm to segment and track the carotid in subsequent images. A finite-element deformation model is fit to the observed segmentation and force via a two-stage iterative non-linear optimization. The first-stage optimization estimates carotid artery wall stiffness parameters along with systolic and diastolic carotid pressures. The second-stage optimization takes the output parameters from the first optimization and estimates the carotid blood pressure waveform. Diastolic and systolic measurements are compared with those of an oscillometric brachial blood pressure cuff. In 20 participants, average absolute diastolic and systolic errors are 6.2 and 5.6 mm Hg, respectively, and correlation coefficients are r = 0.7 and r = 0.8, respectively. Force-coupled ultrasound imaging represents an automated, standalone ultrasound-based technique for carotid blood pressure estimation, which motivates its further development and expansion of its applications.
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Determinación de la Presión Sanguínea , Arterias Carótidas , Presión Sanguínea/fisiología , Determinación de la Presión Sanguínea/métodos , Arterias Carótidas/diagnóstico por imagen , Arterias Carótidas/fisiología , Humanos , Oscilometría , UltrasonografíaRESUMEN
This work presents the first quantitative ultrasonic sound speed images of ex vivo limb cross-sections containing both soft tissue and bone using Full Waveform Inversion (FWI) with level set (LS) and travel time regularization. The estimated bulk sound speed of bone and soft tissue are within 10% and 1%, respectively, of ground truth estimates. The sound speed imagery shows muscle, connective tissue and bone features. Typically, ultrasound tomography (UST) using FWI is applied to imaging breast tissue properties (e.g. sound speed and density) that correlate with cancer. With further development, UST systems have the potential to deliver volumetric operator independent tissue property images of limbs with non-ionizing and portable hardware platforms. This work addresses the algorithmic challenges of imaging the sound speed of bone and soft tissue by combining FWI with LS regularization and travel time methods to recover soft tissue and bone sound speed with improved accuracy and reduced soft tissue artifacts when compared to conventional FWI. The value of leveraging LS and travel time methods is realized by evidence of improved bone geometry estimates as well as promising convergence properties and reduced risk of final model errors due to un-modeled shear wave propagation. Ex vivo bulk measurements of sound speed and MRI cross-sections validates the final inversion results.
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Hueso Cortical , Sonido , Artefactos , Huesos/diagnóstico por imagen , Fantasmas de Imagen , UltrasonografíaRESUMEN
In this study, we compared multiple quantitative ultrasound metrics for the purpose of differentiating muscle in 20 healthy, 10 dystrophic and 10 obese mice. High-frequency ultrasound scans were acquired on dystrophic (D2-mdx), obese (db/db) and control mouse hindlimbs. A total of 248 image features were extracted from each scan, using brightness-mode statistics, Canny edge detection metrics, Haralick features, envelope statistics and radiofrequency statistics. Naïve Bayes and other classifiers were trained on single and pairs of features. The a parameter from the Homodyned K distribution at 40 MHz achieved the best univariate classification (accuracy = 85.3%). Maximum classification accuracy of 97.7% was achieved using a logistic regression classifier on the feature pair of a2 (K distribution) at 30 MHz and brightness-mode variance at 40MHz. Dystrophic and obese mice have muscle with distinct acoustic properties and can be classified to a high level of accuracy using a combination of multiple features.
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Distrofia Muscular de Duchenne , Animales , Teorema de Bayes , Ratones , Ratones Endogámicos mdx , Músculo Esquelético/diagnóstico por imagen , Obesidad/diagnóstico por imagenRESUMEN
Quantitative ultrasound (QUS) has emerged as a viable tool in diagnosing and staging the onset and progression of various diseases. Within the field of QUS, shear wave elastography (SWE) has emerged as the clinical standard for quantifying and correlating the stiffness of tissue to its underlying pathology. Despite its widespread use, SWE suffers from drawbacks that limit its widespread clinical use; among these are low-frame rates, long settling times, and high sensitivity to operating conditions. Longitudinal speed of sound (SOS) has emerged as a viable alternative to SWE. We propose a framework to obtain 2D sound speed maps using a commercial ultrasound probe. A commercial ultrasound probe is localized in space and used to scan a domain of interest from multiple vantage points; the use of a reflector at the far end of the domain allows us to measure the round trip travel times to and from it. The known locations of the probe and the measured travel times are used to estimate the depth and inclination of the reflector as well as the unknown sound speed map. The use of multiple looks increases the effective aperture of the ultrasound probe and allows for a higher fidelity reconstruction of sound speed maps. We validate the framework using simulated and experimental data and propose a rigorous framework to quantify the uncertainty of the estimated sound speed maps.
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Homes equipped with ambient sensors can measure physiological signals correlated with the resident's health without requiring a wearable device. Gait characteristics may reveal physical imbalances or recognize changes in cognitive health. In this paper, we use the physical interactions with floor to both localize the resident and monitor their gait. Accelerometers are placed at the corners of the room for sensing. Gradient boosting regression was used to perform localization with an accuracy of 82%, reasonably accounting for inhomogeneity in the floor with just 3 sensors. A method using step time variance is proposed to detect gait imbalances; results on induced limps are presented.
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Análisis de la Marcha , Dispositivos Electrónicos Vestibles , Marcha , Humanos , Aprendizaje Automático , Monitoreo FisiológicoRESUMEN
Designed or patterned structured surfaces, metasurfaces, enable the miniaturization of complex arrangements of optical elements on a plane. Most of the existing literature focuses on miniaturizing the optical detection; little attention is directed to on-chip optical excitation. In this work, we design a metasurface to create a planar integrated photonic source beam collimator for use in on-chip optofluidic sensing applications. We use an iterative inverse design approach in order to optimize the metasurface to achieve a target performance using gradient descent method. We then fabricate beam collimators and experimentally compare performance characteristics with conventional uniform binary grating-based photonic beam diffractors. The optimal design enhances the illumination power by a factor of 5. The reinforced beam is more uniform with 3 dB beam spot increased almost ~ 3 times for the same device footprint area. The design approach will be useful in on-chip applications of fluorescence imaging, Raman, and IR spectroscopy and will enable better multiplexing of light sources for high throughput biosensing.
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OBJECTIVE: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for things such as, cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state-of-the-art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound hardware due to high-power requirements, and are extremely sensitive to patient and sonographer motion, and generally suffer from low frame rates. Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single-sided pressure-wave sound speed measurements from channel data. METHODS: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data. RESULTS: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data. CONCLUSION: Sound speed inversion on channel data has made significant potential possible in real time with deep learning technologies. SIGNIFICANCE: Specialized shear wave ultrasound systems remain inaccessible in many locations. Longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are also possible.
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Aprendizaje Profundo , Diagnóstico por Imagen de Elasticidad , Humanos , Fantasmas de Imagen , Sonido , UltrasonografíaRESUMEN
Nanophotonics is a rapidly emerging field in which complex on-chip components are required to manipulate light waves. The design space of on-chip nanophotonic components, such as an optical meta surface which uses sub-wavelength meta-atoms, is often a high dimensional one. As such conventional optimization methods fail to capture the global optimum within the feasible search space. In this manuscript, we explore a Machine Learning (ML)-based method for the inverse design of the meta-optical structure. We present a data-driven approach for modeling a grating meta-structure which performs photonic beam engineering. On-chip planar photonic waveguide-based beam engineering offers the potential to efficiently manipulate photons to create excitation beams (Gaussian, focused and collimated) for lab-on-chip applications of Infrared, Raman and fluorescence spectroscopic analysis. Inverse modeling predicts meta surface design parameters based on a desired electromagnetic field outcome. Starting with the desired diffraction beam profile, we apply an inverse model to evaluate the optimal design parameters of the meta surface. Parameters such as the repetition period (in 2D axis), height and size of scatterers are calculated using a feedforward deep neural network (DNN) and convolutional neural network (CNN) architecture. A qualitative analysis of the trained neural network, working in tandem with the forward model, predicts the diffraction profile with a correlation coefficient as high as 0.996. The developed model allows us to rapidly estimate the desired design parameters, in contrast to conventional (gradient descent based or genetic optimization) time-intensive optimization approaches.
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Functional muscle imaging is essential for diagnostics of a multitude of musculoskeletal afflictions such as degenerative muscle diseases, muscle injuries, muscle atrophy, and neurological related issues such as spasticity. However, there is currently no solution, imaging or otherwise, capable of providing a map of active muscles over a large field of view in dynamic scenarios.In this work, we look at the feasibility of applying longitudinal sound speed measurements to the task of dynamic muscle imaging of contraction or activation. We perform the assessment using a deep learning network applied to prebeamformed ultrasound channel data for sound speed inversion.Preliminary results show that dynamic muscle contraction can be detected in the calf and that this contraction can be positively assigned to the operating muscles. Potential frame rates in the hundreds to thousands of frames per second are necessary to accomplish this.
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Aprendizaje Profundo , Músculos , Contracción Muscular , Músculos/diagnóstico por imagen , Sonido , UltrasonografíaRESUMEN
Wireless capsule endoscopy (WCE) has revolutionized the capacity for evaluation of the gastrointestinal (GI) tract, but its evaluation is limited to the mucosal surface. To overcome this, ultrasound capsule endoscopy (UCE) that can evaluate the deeper structures beyond the mucosal surface has been proposed and several studies focusing on technology development have demonstrated promising results. However, investigations of the potential for clinical utility of this technology are lacking. This work had two main goals: perform ex vivo and in vivo imaging studies in a swine model to (1) evaluate if acoustic coupling between a capsule with a specific size and GI tract can be achieved only through peristalsis autonomously without any human control and (2) identify key issues and challenges to help guide further research. The images acquired in these studies were able to visualize the wall of the GI tract as well as the structures within demonstrating that achieving adequate acoustic coupling through peristalsis is possible. Critical challenges were identified including level of visualization and area of coverage; these require further in-depth investigation before potential clinical utility of UCE technology can be concluded.