3D Ultrasound Shear Wave Elastography for Musculoskeletal Tissue Assessment Under Compressive Load: A Feasibility Study.

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.

Modeling "wiggling" as a multi-path interference problem in AMCW ToF imaging.

Amplitude modulated continuous wave time-of-flight range cameras suffer from an inherent depth measurement error due to aliasing of the emitted signal vs reference signal correlation function. This is due to higher harmonics present in both signals which are not accounted for in the model or measurements. This "wiggling" error is generally corrected by employing a correction function based on frequency and depth dependent calibration data. This problem is shown to be equivalent to a multi-path interference problem. Casting the problem into the multi-path interference domain and utilizing multiple modulation frequencies provides tools for dealing with the depth error without calibration in a frequency independent way.

Pose estimation using time-resolved inversion of diffuse light.

We present a novel approach for evaluation of position and orientation of geometric shapes from scattered time-resolved data. Traditionally, imaging systems treat scattering as unwanted and are designed to mitigate the effects. Instead, we show here that scattering can be exploited by implementing a system based on a femtosecond laser and a streak camera. The result is accurate estimation of object pose, which is a fundamental tool in analysis of complex scenarios and plays an important role in our understanding of physical phenomena. Here, we experimentally show that for a given geometry, a single incident illumination point yields enough information for pose estimation and tracking after multiple scattering events. Our technique can be used for single-shot imaging behind walls or through turbid media.

Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization.

Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase shifts of amplitude-modulated signals. For broad illumination of transparent objects, reflections from multiple scene points can illuminate a given pixel, giving rise to an erroneous depth map. We report here a sparsity-regularized solution that separates K interfering components using multiple modulation frequency measurements. The method maps ToF imaging to the general framework of spectral estimation theory and has applications in improving depth profiles and exploiting multiple scattering.

Image formation of thick three-dimensional objects in differential-interference-contrast microscopy.

The differential-interference-contrast (DIC) microscope is of widespread use in life sciences as it enables noninvasive visualization of transparent objects. The goal of this work is to model the image formation process of thick three-dimensional objects in DIC microscopy. The model is based on the principles of electromagnetic wave propagation and scattering. It simulates light propagation through the components of the DIC microscope to the image plane using a combined geometrical and physical optics approach and replicates the DIC image of the illuminated object. The model is evaluated by comparing simulated images of three-dimensional spherical objects with the recorded images of polystyrene microspheres. Our computer simulations confirm that the model captures the major DIC image characteristics of the simulated object, and it is sensitive to the defocusing effects.

Computing Speed-of-Sound From Ultrasound: User-Agnostic Recovery and a New Benchmark.

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.

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound.

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.

Detecting muscle activation using ultrasound speed of sound inversion with deep learning.

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.

3D Ultrasound Imaging of Residual Limbs With Camera-Based Motion Compensation.

Ultrasound is a cost-effective, readily available, and non-ionizing modality for musculoskeletal imaging. Though some research groups have pursued methods that involve submerging the transducer and imaged body segment into a water bath, many limitations remain in regards to acquiring an unloaded volumetric image of an entire human limb in a fast, safe, and adequately accurate manner. A 3D dataset of a limb is useful in several rehabilitative applications including biomechanical modeling of soft tissue, prosthetic socket design, monitoring muscle condition and disease progression, bone health, and orthopedic surgery. This paper builds on previous work from our group and presents the design, prototyping, and preliminary testing of a novel multi-modal imaging system for rapidly acquiring volumetric ultrasound imagery of human limbs, with a particular focus on residual limbs for improved prosthesis design. Our system employs a mechanized water tank setup to scan a limb with a clinical ultrasound transducer and 3D optical imagery to track motion during a scan. The iterative closest point algorithm is utilized to compensate for motion and stitch the images into a final dataset. The results show preliminary 2D and 3D imaging of both a tissue-mimicking phantom and residual limbs. A volumetric error compares the ultrasound image data obtained to a previous MRI method. The results indicate potential for future clinical implementation. Concepts presented in this paper could reasonably transfer to other imaging applications such as acoustic tomography, where motion artifact may distort image reconstruction.

Time-of-Flight Microwave Camera.

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable "stealth" regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz-12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows "camera-like" behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum.

Motion compensation in a tomographic ultrasound imaging system: Toward volumetric scans of a limb for prosthetic socket design.

Current methods of prosthetic socket fabrication remain subjective and ineffective at creating an interface to the human body that is both comfortable and functional. Though there has been recent success using methods like magnetic resonance imaging and biomechanical modeling, a low-cost, streamlined, and repeatable process has not been fully demonstrated. Medical ultrasonography, which has significant potential to expand its clinical applications, is being pursued to acquire data that may quantify and improve the design process and fabrication of prosthetic sockets. This paper presents a new multi-modal imaging approach for acquiring volumetric images of a human limb, specifically focusing on how motion of the limb is compensated for using optical imagery.

Validity criterion for the Born approximation convergence in microscopy imaging.

The need for the reconstruction and quantification of visualized objects from light microscopy images requires an image formation model that adequately describes the interaction of light waves with biological matter. Differential interference contrast (DIC) microscopy, as well as light microscopy, uses the common model of the scalar Helmholtz equation. Its solution is frequently expressed via the Born approximation. A theoretical bound is known that limits the validity of such an approximation to very small objects. We present an analytic criterion for the validity region of the Born approximation. In contrast to the theoretical known bound, the suggested criterion considers the field at the lens, external to the object, that corresponds to microscopic imaging and extends the validity region of the approximation. An analytical proof of convergence is presented to support the derived criterion. The suggested criterion for the Born approximation validity region is described in the context of a DIC microscope, yet it is relevant for any light microscope with similar fundamental apparatus.