While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest Mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.

Background Tension in the spinal cord is a trademark of tethered cord syndrome. Unfortunately, existing tests cannot quantify tension across the bulk of the cord, making the diagnostic evaluation of stretch ambiguous. A potential non-destructive metric for spinal cord tension is ultrasound-derived shear wave velocity (SWV). The velocity is sensitive to tissue elasticity and boundary conditions including strain. We use the term Ultrasound Tensography to describe the acoustic evaluation of tension with SWV. Methods Our solution Tethered cord Assessment with Ultrasound Tensography (TAUT) was utilized in three sub-studies: finite element simulations, a cadaveric benchtop validation, and a neurosurgical case series. The simulation computed SWV for given tensile forces. The cadaveric model with induced tension validated the SWV-tension relationship. Lastly, SWV was measured intraoperatively in patients diagnosed with tethered cords who underwent treatment (spinal column shortening). The surgery alleviates tension by decreasing the vertebral column length. Results Here we observe a strong linear relationship between tension and squared SWV across the preclinical sub-studies. Higher tension induces faster shear waves in the simulation ( R 2 = 0.984) and cadaveric ( R 2 = 0.951) models. The SWV decreases in all neurosurgical procedures ( p < 0.001). Moreover, TAUT has a c-statistic of 0.962 (0.92-1.00), detecting all tethered cords. Conclusions This study presents a physical, clinical metric of spinal cord tension. Strong agreement among computational, cadaveric, and clinical studies demonstrates the utility of ultrasound-induced SWV for quantitative intraoperative feedback. This technology is positioned to enhance tethered cord diagnosis, treatment, and postoperative monitoring as it differentiates stretched from healthy cords.

Focused ultrasound (FUS) therapy is a promising tool for optimally targeted treatment of spinal cord injuries (SCI), offering submillimeter precision to enhance blood flow at injury sites while minimizing impact on surrounding tissues. However, its efficacy is highly sensitive to the placement of the ultrasound source, as the spinal cord's complex geometry and acoustic heterogeneity distort and attenuate the FUS signal. Current approaches rely on computer simulations to solve the governing wave propagation equations and compute patient-specific pressure maps using ultrasound images of the spinal cord anatomy. While accurate, these high-fidelity simulations are computationally intensive, taking up to hours to complete parameter sweeps, which is impractical for real-time surgical decision-making. To address this bottleneck, we propose a convolutional deep operator network (DeepONet) to rapidly predict FUS pressure fields in patient spinal cords. Unlike conventional neural networks, DeepONets are well equipped to approximate the solution operator of the parametric partial differential equations (PDEs) that govern the behavior of FUS waves with varying initial and boundary conditions (i.e., new transducer locations or spinal cord geometries) without requiring extensive simulations. Trained on simulated pressure maps across diverse patient anatomies, this surrogate model achieves real-time predictions with only a 2 nonlinear physical systems in heterogeneous domains. By facilitating rapid parameter sweeps in surgical settings, this work provides a crucial step toward precise and individualized solutions in neurosurgical treatments.

A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be quite effective at predicting these solution generators after training on high-fidelity ground truth data (e.g. numerical simulations). However, in order to generalize well to unseen spatial domains, neural operators must be trained on an extensive amount of geometrically varying data samples that may not be feasible to acquire or simulate in certain contexts (e.g., patient-specific medical data, large-scale computationally intensive simulations.) We propose that in order to learn a PDE solution operator that can generalize across multiple domains without needing to sample enough data expressive enough for all possible geometries, we can train instead a latent neural operator on just a few ground truth solution fields diffeomorphically mapped from different geometric/spatial domains to a fixed reference configuration. Furthermore, the form of the solutions is dependent on the choice of mapping to and from the reference domain. We emphasize that preserving properties of the differential operator when constructing these mappings can significantly reduce the data requirement for achieving an accurate model due to the regularity of the solution fields that the latent neural operator is training on. We provide motivating numerical experimentation that demonstrates an extreme case of this consideration by exploiting the conformal invariance of the Laplacian

Focused ultrasound (FUS) is an increasingly prevalent therapeutic tool used in medicine to treat patients non-invasively, cost-effectively, and without any ionizing radiation. Depending on the clinical application, therapeutic ultrasound can be used for tissue ablation, physiotherapy, lithotripsy, and drug delivery, among other therapies. For the treatment to be effective, it is important to understand the acoustic pressure profile in the tissue to ensure the focal point intensity is sufficient to achieve therapy without damaging the tissue. In spinal cord injury (SCI), where FUS is hypothesized to promote blood flow to the site of injury to facilitate healing, it can be difficult to predict the expected pressure wavefields in real-time. This challenge is due to the complex structure of the spinal cord and variations in acoustic properties within the field of view, leading to distortion of the ultrasound wave front. To address this issue, we present an automated approach to visualize beam propagation and acoustic pressure distribution to aid with placement of the FUS probe. By simulating propagation of the ultrasound beams through patient-specific Digital Imaging and Communications in Medicine (DICOM) images, therapy can be optimized to minimize tissue damage while providing adequate focusing and focal intensity. In this study, porcine spinal cord images acquired after acute SCI are used to determine optimal FUS transducer placement.

Tissue elasticity remains an essential biomarker of health and is indicative of irregularities such as tumors or infection. The timely detection of such abnormalities is crucial for the prevention of disease progression and complications that arise from late-stage illnesses. However, at both the bedside and the operating table, there is a distinct lack of tactile feedback for deep-seated tissue. As surgical techniques advance toward remote or minimally invasive options to reduce infection risk and hasten healing time, surgeons lose the ability to manually palpate tissue. Furthermore, palpation of deep structures results in decreased accuracy, with the additional barrier of needing years of experience for adequate confidence of diagnoses. This review delves into the current modalities used to fulfill the clinical need of quantifying physical touch. It covers research efforts involving tactile sensing for remote or minimally invasive surgeries, as well as the potential of ultrasound elastography to further this field with non-invasive real-time imaging of the organ's biomechanical properties. Elastography monitors tissue response to acoustic or mechanical energy and reconstructs an image representative of the elastic profile in the region of interest. This intuitive visualization of tissue elasticity surpasses the tactile information provided by sensors currently used to augment or supplement manual palpation. Focusing on common ultrasound elastography modalities, we evaluate various sensing mechanisms used for measuring tactile information and describe their emerging use in clinical settings where palpation is insufficient or restricted. With the ongoing advancements in ultrasound technology, particularly the emergence of micromachined ultrasound transducers, these devices hold great potential in facilitating early detection of tissue abnormalities and providing an objective measure of patient health.

Abstract Background Tension in the spinal cord is a trademark of tethered cord syndrome. Unfortunately, existing tests cannot quantify tension across the bulk of the cord, making the diagnostic evaluation of stretch ambiguous. A potential non-destructive metric for spinal cord tension is ultrasound-derived shear wave velocity (SWV). The velocity is sensitive to tissue elasticity and boundary conditions including strain. We use the term Ultrasound Tensography to describe the acoustic evaluation of tension with SWV. Methods Our solution “Tethered cord Assessment with Ultrasound Tensography (TAUT)” was utilized in three sub-studies: finite element simulations, a cadaveric benchtop validation, and a neurosurgical case series. The simulation computed SWV for given tensile forces. The induced tension cadaveric model validated the SWV-tension relationship. Lastly, SWV was measured intraoperatively in patients diagnosed with tethered cord who underwent surgical treatment (spinal column shortening). The surgery alleviates tension by decreasing the vertebral column length. Results Here we observe a strong linear relationship between tension and squared SWV across the preclinical sub-studies. Higher tension induces faster shear waves in the simulation (R2 = 0.984) and cadaveric (R2 = 0.951) models. The SWV decreases in all neurosurgical procedures (p<0.001). Moreover, TAUT has a c-statistic of 0.962 (0.92-1.00), detecting all tethered cords. Conclusions This study presents the first clinical metric of spinal cord tension. Strong agreement among computational, cadaveric, and clinical studies demonstrates the utility of ultrasound-induced SWV for quantitative intraoperative feedback. This technology is positioned to enhance tethered cord diagnosis, treatment, and post-operative monitoring as it differentiates stretched from healthy cords.

Ultrasound holds promise for use in spinal cord injury cases for both diagnostic and therapeutic purposes. Focused ultrasound applications demand an added threshold of study to ensure the safety and efficacy of the therapy. For optimal treatment outcomes, it is crucial to understand whether relevant structures are being targeting with sufficient energy without damaging neighboring tissue and vasculature. However, it is difficult to predict the expected displacement and pressure profile of the ultrasound wavefront due to challenges with visualizing an acoustic beam in real-time and complex patient-specific anatomy. This challenge is particularly prominent in anatomies with varying medium acoustic properties that cause reflection and distortion of the signal, which is inherent to the composition of the spinal cord and is exacerbated by the formations of injury-induced hematomas. Incorrect placement of focused ultrasound transducers can be detrimental to patient health, specifically if therapeutic ultrasound is used at higher intensities, as the beam propagation can target healthy tissue and important structures that could lead to tissue damage and death. We study how computational tools can be leveraged to aid placement of the transducer using an ultrasound simulation software, Wave 3000 Plus, that allows for the visualization of ultrasound propagation through anatomical structures. By simulating the propagation of ultrasound beams through patient-specific Digital Imaging and Communications in Medicine (DICOM) images, we study computational approaches to determine the optimal placement of devices. In this study, we use in vivo porcine spinal cord images following spinal cord injury (as an example medical use case) to determine if the injury site is being targeted appropriately and to visualize the distribution of pressure throughout the simulation. We demonstrate that Wave 3000 Plus is a viable approach for visualizing ultrasound propagation through patient-specific anatomies.

While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored. We propose an artificial neural network dubbed VISION, an acronym for "Visual Interface System for Imaging Output of Neural activity," to mimic the human brain and show how it can foster neuroscientific inquiries. Using visual and contextual inputs, this multimodal model predicts the brain's functional magnetic resonance imaging (fMRI) scan response to natural images. VISION successfully predicts human hemodynamic responses as fMRI voxel values to visual inputs with an accuracy exceeding state-of-the-art performance by 45%. We further probe the trained networks to reveal representational biases in different visual areas, generate experimentally testable hypotheses, and formulate an interpretable metric to associate these hypotheses with cortical functions. With both a model and evaluation metric, the cost and time burdens associated with designing and implementing functional analysis on the visual cortex could be reduced. Our work suggests that the evolution of computational models may shed light on our fundamental understanding of the visual cortex and provide a viable approach toward reliable brain-machine interfaces.

This paper provides an overview of Alpha, a rapidly developed, low-cost CubeSat mission to verify the performance of a highly retroreflective material for light-sail propulsion. Designed, integrated, and tested by students of the Space Systems Design Studio at Cornell University, this mission demonstrates a number of key technologies that enable next-generation capabilities for space exploration. In particular, this paper focuses on the novel application of ChipSats (gram scale spacecraft-on-a-chip technology) as a means of verifying Alpha's sail orbit and attitude dynamics. Other innovations include an entirely 3D-printed structure to enable quick and inexpensive prototyping, an onboard Iridium modem that bypasses the need for ground-station radio equipment, retroreflective sail material that provides more deterministic thrust from laser illumination, and an attitude-control subsystem that provides full attitude and angular-rate control using magnetorquers only. In addition to these near-term technology demonstrations, Alpha is among the first exhibitions of holography in space, a medium that shows longer-term promise in several roles for interstellar travel.