A Multi-Grid Iterative Method for Photoacoustic Tomography

Inspired by the recent advances on minimizing nonsmooth or bound-constrained convex functions on models using varying degrees of fidelity, we propose a line search multigrid (MG) method for full-wave iterative image reconstruction in photoacoustic tomography (PAT) in heterogeneous media. To compute the search direction at each iteration, we decide between the gradient at the target level, or alternatively an approximate error correction at a coarser level, relying on some predefined criteria. To incorporate absorption and dispersion, we derive the analytical adjoint directly from the first-order acoustic wave system. The effectiveness of the proposed method is tested on a total-variation penalized Iterative Shrinkage Thresholding algorithm (ISTA) and its accelerated variant (FISTA), which have been used in many studies of image reconstruction in PAT. The results show the great potential of the proposed method in improving speed of iterative image reconstruction

FPGA-Based Portable Ultrasound Scanning System with Automatic Kidney Detection

Bedsides diagnosis using portable ultrasound scanning (PUS) offering comfortable diagnosis with various clinical advantages, in general, ultrasound scanners suffer from a poor signal-to-noise ratio, and physicians who operate the device at point-of-care may not be adequately trained to perform high level diagnosis. Such scenarios can be eradicated by incorporating ambient intelligence in PUS. In this paper, we propose an architecture for a PUS system, whose abilities include automated kidney detection in real time. Automated kidney detection is performed by training the Viola–Jones algorithm with a good set of kidney data consisting of diversified shapes and sizes. It is observed that the kidney detection algorithm delivers very good performance in terms of detection accuracy. The proposed PUS with kidney detection algorithm is implemented on a single Xilinx Kintex-7 FPGA, integrated with a Raspberry Pi ARM processor running at 900 MHz

Ultrasound Imaging

In this book, we present a dozen state of the art developments for ultrasound imaging, for example, hardware implementation, transducer, beamforming, signal processing, measurement of elasticity and diagnosis. The editors would like to thank all the chapter authors, who focused on the publication of this book

Distributed Compressive Sensing Algorithm for Photoacoustic Tomography

Biomedical imaging techniques are playing an essential role in diagnosing different kinds of diseases, which always motivates the search for improving their sensitivity and accuracy. Photoacoustic Tomography (PAT) is one of the most powerful techniques. PAT has many advantages as it is less expensive and faster than Magnetic Resonance Imaging (MRI). It combines the advantages of optical imaging and ultrasound imaging as it provides high contrast, high penetration, and high-resolution images for biological tissues. Also, it uses non-ionizing radiation which is very safe for human health. The main challenge in PAT is that human tissues can be exposed only to a limited amount of radiation, so a full-view of PAT requires many transducers and a great number of measurements. This thesis aims to develop an efficient reconstruction algorithm of Photoacoustic (PA) images that uses a few number of transducers, a few number of measurements, and offers low computational complexity while maintaining a high quality of recovered images. The proposed reconstruction algorithm depends on the Compressive Sensing (CS) theory which is a signal processing technique that is capable of forming a full view PAT images (under certain prerequisites) with a few number of measurements. The proposed algorithm solves the CS problem using a distributed and parallel implementation of the Alternating Direction Method of Multipliers (ADMM). ADMM is a well-known method for solving convex optimization problems. A group of local processors that work in parallel with one global processor is used to form the images. The iterative algorithm of ADMM is distributed over local processors in such a way perfect reconstruction of images is possible. Simulation results show that the proposed algorithm is powerful and successful in reconstructing different kinds of PA images with very high quality and significantly reduced computational complexity. Reducing the computational complexity is reflected in a much lower reconstruction time. Also, the algorithm requires lower cost and shorter acquisition time since the CS theory is used which allows the recovery of images from a few number of samples and sensors. Although the idea of distributed ADMM has been introduced before in literature but to the best of our knowledge, this is the first work to apply distributed ADMM method in recovering photoacoustic images by distributing the iterative algorithm among multiple processors working in parallel

EdgeEcho: An Architecture for Echocardiology at the Edge

Edge computing technologies have improved delays and privacy of several applications, including in medical imaging and eHealth. In this paper, we consider ultrasound technology and echocardiology (echo) and empower it with edge computing. Despite the many advances that ultrasound technology has seen recently, e.g., it is possible to perform echo scans using wireless ultrasound probes, the use of Artificial Intelligence (AI) techniques is becoming a necessity, for faster and more accurate echo diagnosis (not limited to heart diseases). While a few proprietary solutions exist that embed AI within echo devices, none of them uses resource-intensive tasks on handheld devices, and none of them is open-source. To this end, we propose EdgeEcho, an architecture that captures ultrasound data originated from handheld ultrasound probes and tags it using semantic segmentation performed on edge cloud. Our prototype focuses on optimizing the management of edge resources to address the specific requirements of echocardiology and the challenges of serving AI algorithms responsively. As a use case, we focus on a ventricular volume detection operation. Our performance evaluation results show that EdgeEcho can support multiple parallel medical video processing streaming sessions for continuing medical education, demonstrating a promising edge computing application with life-saving potential

A Bayesian approach for energy-based estimation of acoustic aberrations in high intensity focused ultrasound treatment

High intensity focused ultrasound is a non-invasive method for treatment of diseased tissue that uses a beam of ultrasound to generate heat within a small volume. A common challenge in application of this technique is that heterogeneity of the biological medium can defocus the ultrasound beam. Here we reduce the problem of refocusing the beam to the inverse problem of estimating the acoustic aberration due to the biological tissue from acoustic radiative force imaging data. We solve this inverse problem using a Bayesian framework with a hierarchical prior and solve the inverse problem using a Metropolis-within-Gibbs algorithm. The framework is tested using both synthetic and experimental datasets. We demonstrate that our approach has the ability to estimate the aberrations using small datasets, as little as 32 sonication tests, which can lead to significant speedup in the treatment process. Furthermore, our approach is compatible with a wide range of sonication tests and can be applied to other energy-based measurement techniques

Ultrasound Guided Robot for Human Liver Biopsy using High Intensity Focused Ultrasound for Hemostasis

Percutaneous liver biopsy is the gold standard among clinician\u27s tool to diagnose and guide subsequent therapy for liver disease. Ultrasound image guidance is being increasingly used to reduce associated procedural risks but post–biopsy complications still persist. The major and most common complication is hemorrhage, which is highly unpredictable and may sometimes lead to death. Though the risk of mortality is low, it is too high for a diagnostic procedure. Post-biopsy care and additional surgical intervention to arrest hemorrhage make liver biopsy a costly procedure for health care delivery systems. Non-invasive methods to stop bleeding exist like electro–cautery, microwave, lasers, radio frequency, argon–beam, and High Intensity Focused Ultrasound (HIFU). All the methods except HIFU require direct exposure of the needle puncture site for hemostasis. HIFU is an ultrasound modality and uses mechanical sound waves for focused energy delivery. Ultrasound waves are minimally affected by tissue attenuation and focus internal targets without direct exposure. Human error in focusing HIFU renders it unusable for a medical procedure especially when noninvasive. In this project we designed and developed an ultrasound guided prototype robot for accurate HIFU targeting to induce hemostasis. The robotic system performs percutaneous needle biopsy and a 7.5 cm focal length HIFU is fired at the puncture point when the needle tip retracts to the liver surface after sample collection. The robot has 4 degrees of freedom (DOF) for biopsy needle insertion, HIFU positioning, needle angle alignment and US probe image plane orientation. As the needle puncture point is always in the needle path, mechanically constraining the HIFU to focus on the needle reduced the required functionality significantly. Two mini c-arms are designed for needle angle alignment and US probe image plane orientation. This reduced the contact foot print of the robot over the patient providing a greater dexterity for positioning the robot. The robot is validated for HIFU hemostasis by a series of experiments on chicken breasts. HIFU initiated hemorrhage control with robotic biopsy ensures arrest of post-biopsy hemorrhage and decreases patient anxiety, hospital stay, morbidity, time of procedure, and cost. This can also be extended to other organs like kidneys, lungs etc. and has widespread implications such as control of hemorrhage in post-biopsies in patients with reduced ability for hemostasis. This research opens a greater scope for research for automation and design making it a physician friendly tool for eventual clinical use

Focal Spot, Summer 1988

https://digitalcommons.wustl.edu/focal_spot_archives/1049/thumbnail.jp