Compressive 3D ultrasound imaging using a single sensor

Three-dimensional ultrasound is a powerful imaging technique, but it requires thousands of sensors and complex hardware. Very recently, the discovery of compressive sensing has shown that the signal structure can be exploited to reduce the burden posed by traditional sensing requirements. In this spirit, we have designed a simple ultrasound imaging device that can perform three-dimensional imaging using just a single ultrasound sensor. Our device makes a compressed measurement of the spatial ultrasound field using a plastic aperture mask placed in front of the ultrasound sensor. The aperture mask ensures that every pixel in the image is uniquely identifiable in the compressed measurement. We demonstrate that this device can successfully image two structured objects placed in water. The need for just one sensor instead of thousands paves the way for cheaper, faster, simpler, and smaller sensing devices and possible new clinical applications

Four-Dimensional Computational Ultrasound Imaging of Brain Haemodynamics

Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), a new imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain haemodynamics in awake and anesthetized mice

Localization of a Scatterer in 3D with a Single Measurement and Single Element Transducer

Conventionally an A-mode scan, a single measurement with a single element transducer, is only used to detect the depth of a reflector or scatterer. In this case, a single measurement reveals only one-dimensional information; the axial distance. However, if the number of scatterers in the ultrasonic field is sparse, it is possible to detect the location of the scatter in multiple spatial dimensions. In this study, we developed a method to find the location of a scatterer in 3-D with a single-element transducer and single measurement. The feasibility of the proposed method was verified in 2-D with experimental measurements

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Accuracy assessment of Tri-plane B-mode ultrasound for non-invasive 3D kinematic analysis of knee joints

BACKGROUND Currently the clinical standard for measuring the motion of the bones in knee joints with sufficient precision involves implanting tantalum beads into the bones. These beads appear as high intensity features in radiographs and can be used for precise kinematic measurements. This procedure imposes a strong coupling between accuracy and invasiveness. In this paper, a tri-plane B-mode ultrasound (US) based non-invasive approach is proposed for use in kinematic analysis of knee joints in 3D space. METHODS The 3D analysis is performed using image processing procedures on the 2D US slices. The novelty of the proposed procedure and its applicability to the unconstrained 3D kinematic analysis of knee joints is outlined. An error analysis for establishing the method's feasibility is included for different artificial compositions of a knee joint phantom. Some in-vivo and in-vitro scans are presented to demonstrate that US scans reveal enough anatomical details, which further supports the experimental setup used using knee bone phantoms. RESULTS The error between the displacements measured by the registration of the US image slices and the true displacements of the respective slices measured using the precision mechanical stages on the experimental apparatus is evaluated for translation and rotation in two simulated environments. The mean and standard deviation of errors are shown in tabular form. This method provides an average measurement precision of less than 0.1 mm and 0.1 degrees, respectively. CONCLUSION In this paper, we have presented a novel non-invasive approach to measuring the motion of the bones in a knee using tri-plane B-mode ultrasound and image registration. In our study, the image registration method determines the position of bony landmarks relative to a B-mode ultrasound sensor array with sub-pixel accuracy. The advantages of our proposed system over previous techniques are that it is non-invasive, does not require the use of ionizing radiation and can be used conveniently if miniaturized.This work has been supported by School of Engineering & IT, UNSW Canberra, under Research Publication Fellowship

Single-sensor and real-time ultrasonic imaging using an AI-driven disordered metasurface

Non-destructive testing and medical diagnostic techniques using ultrasound has become indispensable in evaluating the state of materials or imaging the internal human body, respectively. To conduct spatially resolved high-quality observations, conventionally, sophisticated phased arrays are used both at the emitting and receiving ends of the setup. In comparison, single-sensor imaging techniques offer significant benefits including compact physical dimensions and reduced manufacturing expenses. However, recent advances such as compressive sensing have shown that this improvement comes at the cost of additional time-consuming dynamic spatial scanning or multi-mode mask switching, which severely hinders the quest for real-time imaging. Consequently, real-time single-sensor imaging, at low cost and simple design, still represents a demanding and largely unresolved challenge till this day. Here, we bestow on ultrasonic metasurface with both disorder and artificial intelligence (AI). The former ensures strong dispersion and highly complex scattering to encode the spatial information into frequency spectra at an arbitrary location, while the latter is used to decode instantaneously the amplitude and spectral component of the sample under investigation. Thus, thanks to this symbiosis, we demonstrate that a single fixed sensor suffices to recognize complex ultrasonic objects through the random scattered field from an unpretentious metasurface, which enables real-time and low-cost imaging, easily extendable to 3D

The development of an adaptive and reactive interface system for lower limb prosthetic application

Deep tissue injury (DTI) is a known problem correlating to the use of a prosthetic by a transtibial amputee (TTA), causing ulcer-like wounds on the residual limb caused by stress-induced cell necrosis. The magnitude of these stresses at the bone tissue interface has been identified computationally, far exceeding those measured at the skin's surface. Limited technology is available to directly target and reduce such cellular loading and actively reduce the risk of DTI from below-knee use. The primary aim of this project was to identify whether a bespoke prosthetic socket system could actively stiffen the tissues of the lower limb. Stabilising the residual tibia during ambulation and reducing stress concentrations on the cells. To achieve this, a proof-of-concept device was designed and manufactured, a system that allowed the change in displacement of a magnet to be responded to by counterbalancing load. The device was evaluated through experimentation on an able-bodied subject wearing an orthotic device designed to replicate the environment of a prosthetic socket. The chosen sensor effector system was validated against vector data generated by the Motek Medical Computer Assisted Rehabilitation Environment (CAREN.) The project explored a new concept of reactive loading of a below-knee prosthesis to reduce tibial/socket oscillation. The evaluation of the device indicated that external loading of the residual limb in such a manner could reduce the magnitude of rotation about the tibia and therefore minimise the conditions by which DTIs are known to occur. Efforts were made to move the design to the next iteration, focusing on implementing the target demographic.Deep tissue injury (DTI) is a known problem correlating to the use of a prosthetic by a transtibial amputee (TTA), causing ulcer-like wounds on the residual limb caused by stress-induced cell necrosis. The magnitude of these stresses at the bone tissue interface has been identified computationally, far exceeding those measured at the skin's surface. Limited technology is available to directly target and reduce such cellular loading and actively reduce the risk of DTI from below-knee use. The primary aim of this project was to identify whether a bespoke prosthetic socket system could actively stiffen the tissues of the lower limb. Stabilising the residual tibia during ambulation and reducing stress concentrations on the cells. To achieve this, a proof-of-concept device was designed and manufactured, a system that allowed the change in displacement of a magnet to be responded to by counterbalancing load. The device was evaluated through experimentation on an able-bodied subject wearing an orthotic device designed to replicate the environment of a prosthetic socket. The chosen sensor effector system was validated against vector data generated by the Motek Medical Computer Assisted Rehabilitation Environment (CAREN.) The project explored a new concept of reactive loading of a below-knee prosthesis to reduce tibial/socket oscillation. The evaluation of the device indicated that external loading of the residual limb in such a manner could reduce the magnitude of rotation about the tibia and therefore minimise the conditions by which DTIs are known to occur. Efforts were made to move the design to the next iteration, focusing on implementing the target demographic

Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) compression and reconstruction techniques has attracted considerable attention in recent years. Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements. However, typical compressed sensing designs measure a (non)linearly weighted combination of all input signal elements, which poses practical challenges. These designs are also not necessarily task-optimal. In addition, real-time recovery is hampered by the iterative and time-consuming nature of sparse recovery algorithms. Recently, deep learning methods have shown promise for fast recovery from compressed measurements, but the design of adequate and practical sensing strategies remains a challenge. Here, we propose a deep learning solution termed Deep Probabilistic Sub-sampling (DPS), that learns a task-driven sub-sampling pattern, while jointly training a subsequent task model. Once learned, the task-based sub-sampling patterns are fixed and straightforwardly implementable, e.g. by non-uniform analog-to-digital conversion, sparse array design, or slow-time ultrasound pulsing schemes. The effectiveness of our framework is demonstrated in-silico for sparse signal recovery from partial Fourier measurements, and in-vivo for both anatomical image and tissue-motion (Doppler) reconstruction from sub-sampled medical ultrasound imaging data