Beamforming in plane-wave imaging (PWI) is an essential step in creating images with optimal quality. Adaptive methods estimate the apodization weights from echo traces acquired by several transducer elements. Herein, we formulate plane-wave beamforming as a blind source separation problem. The output of each transducer element is considered as a non-independent observation of the field. As such, beamforming can be formulated as the estimation of an independent component out of the observations. We then adapt the independent component analysis (ICA) algorithm to solve this problem and reconstruct the final image. The proposed method is evaluated on a set of simulation, real phantom, and in vivo data available from the PWI challenge in medical ultrasound. The performance of the proposed beamforming approach is also evaluated in different imaging settings. The proposed algorithm improves lateral resolution by as much as $36.5\%$ and contrast by $10\%$ as compared to the classical delay and sum. Moreover, results are compared with other well-known adaptive methods. Finally, the robustness of the proposed method to noise is investigated.
During the past few years, inverse problem formulations of ultrasound beamforming have attracted a growing interest. They usually pose beamforming as a minimization problem of a fidelity term resulting from the measurement model plus a regularization term that enforces a certain class on the resulting image. Herein, we take advantages of alternating direction method of multipliers to propose a flexible framework in which each term is optimized separately. Furthermore, the proposed beamforming formulation is extended to replace the regularization term by a denoising algorithm, based on the rece…
Beamforming is an essential step in the ultrasound image formation pipeline and has recently attracted growing interest. An important goal of beamforming is to increase the image spatial resolution, or in other words to narrow down the system point spread function. In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF. Unfortunately, these two steps have only been considered separately in a sequential approach. Herein, a novel framework for unifying beamforming and deconvolution in ultrasound image reco…
Medical ultrasound provides images which are the spatial map of the tissue echogenicity. Unfortunately, an ultrasound image is a low-quality version of the expected Tissue Reflectivity Function (TRF) mainly due to the non-ideal Point Spread Function (PSF) of the imaging system. This paper presents a novel beamforming approach based on deep learning to get closer to the ideal PSF in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct the desired TRF from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, an id…
Diagnostic imaging plays a critical role in healthcare, serving as a fundamental asset for timely diagnosis, disease staging and management as well as for treatment choice, planning, guidance, and follow-up. Among the diagnostic imaging options, ultrasound imaging is uniquely positioned, being a highly cost-effective modality that offers the clinician an unmatched and invaluable level of interaction, enabled by its real-time nature. Ultrasound probes are becoming increasingly compact and portable, with the market demand for low-cost pocket-sized and (in-body) miniaturized devices expanding. At…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here we propose a deep learning-based beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network i…
In the recent past, there have been many efforts to accelerate adaptive beamforming for ultrasound (US) imaging using neural networks (NNs). However, most of these efforts are based on static models, i.e., they are trained to learn a single adaptive beamforming approach (e.g., minimum variance distortionless response (MVDR)) assuming that they result in the best image quality. Moreover, the training of such NNs is initiated only after acquiring a large set of data that consumes several gigabytes (GBs) of storage space. In this study, an active learning framework for beamforming is described fo…
Wireless ultrasound (US) systems that produce high-quality images can improve current clinical diagnosis capabilities by making the imaging process much more efficient, affordable, and accessible to users. The most common technique for generating B-mode US images is delay and sum (DAS) beamforming, where an appropriate delay is introduced to signals sampled and processed at each transducer element. However, sampling rates that are much higher than the Nyquist rate of the signal are required for high resolution DAS beamforming, leading to large amounts of data, making transmission of channel da…
Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals. This results in large amounts of data to store and process, imposing hardware and software challenges on the development of ultrasound machinery and algorithms, and impacting the resulting performance. In light of the capabilities demonstrated by deep learning methods over the past years across a variety of fields, including medical imaging, it is natural to consider their ability to recover high-quality ultrasound images from partial data. He…
Technion, Israel（以色列理工学院）；University of Waterloo, Canada（滑铁卢大学）
Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. In this paper, we demonstrate that replacing the traditional ultrasound processing pipeline with a data-driven, learnable counterpart leads to significant improvement in image quality. Moreover, we demonstrate that greater improvement can be achieved through a learning-based design of the transmitted beam patterns simultaneously with learning an image reconstruction pipeline. We evaluate our method on an in-vivo first-harmonic cardiac ult…
A shift of paradigm is currently underway in biomedical ultrasound thanks to plane and diverging waves for ultrafast imaging. One remaining challenge consists in the correction of phase and amplitude aberrations induced during propagation through complex layers. Unlike conventional line-per-line imaging, ultrafast ultrasound provides for each transmission, backscattering information from the whole imaged area. Here, we take benefit from this feature and propose an efficient approach to perform fast aberration correction based on the Singular Value Decomposition of an ultrafast compound matrix …
Ultrafast 3-D Super Resolution Ultrasound using Row-Column Array specific Coherence-based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, In Vivo and Clinical Study
For beamforming ultrasound (US) signals, typically a spatially constant speed-of-sound (SoS) is assumed to calculate delays. As SoS in tissue may vary relatively largely, this approximation may cause wavefront aberrations, thus degrading effective imaging resolution. In the literature, corrections have been proposed based on unidirectional SoS estimation or computationally-expensive a posteriori phase rectification. In this paper we demonstrate a direct delay correction approach for US beamforming, by leveraging 2D spatial SoS distribution estimates from plane-wave imaging. We show both in sim…
Ultrasound elastography is an emerging noninvasive imaging technique wherein pathological alterations can be visualized by revealing the mechanical properties of the tissue. Estimating tissue displacement in all directions is required to accurately estimate the mechanical properties. Despite capabilities of elastography techniques in estimating displacement in both axial and lateral directions, estimation of axial displacement is more accurate than lateral direction due to higher sampling frequency, higher resolution and having a carrier signal propagating in the axial direction. Among differe…
Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging
Netherlands Cancer Institute（荷兰癌症研究所）；University of Twente（特文特大学）；Centrum Wiskunde & Informatica（数学和计算机科学中心）；Utrecht University（乌得勒支大学）；Radboudumc（拉德堡德大学医学中心）
Adaptive beamforming can lead to substantial improvement in resolution and contrast of ultrasound images over standard delay and sum beamforming. Here we introduce the adaptive time-channel (ATC) beamformer, a data-driven approach that combines spatial and temporal information simultaneously, thus generalizing minimum variance beamformers. Moreover, we broaden the concept of apodization to the temporal dimension. Our approach reduces noises by allowing for the weights to adapt in both the temporal and spatial dimensions, thereby reducing artifacts caused by the media's inhomogeneities. We appl…
Eindhoven University of Technology（埃因霍温理工大学）；Technion（以色列理工学院）；Weizmann Institute of Science（魏茨曼科学研究所）
We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive …
Most ultrasound imaging techniques necessitate the fundamental step of converting temporal signals received from transducer elements into a spatial echogenecity map. This beamforming (BF) step requires the knowledge of speed-of-sound (SoS) value in the imaged medium. An incorrect assumption of BF SoS leads to aberration artifacts, not only deteriorating the quality and resolution of conventional brightness mode (B-mode) images, hence limiting their clinical usability, but also impairing other ultrasound modalities such as elastography and spatial SoS reconstructions, which rely on faithfully b…
A Wavefield Correlation Approach to Improve Sound Speed Estimation in Ultrasound Autofocusing
Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）；Department of Radiology, School of Medicine, Stanford University（放射学系，医学院，斯坦福大学）；Department of Applied Physics, Stanford University（应用物理学系，斯坦福大学）；Department of Biomedical Engineering, Columbia University（生物医学工程系，哥伦比亚大学）
Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review
Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg（卢森堡大学生命科学与医学系，科学、技术与医学学院）；Department of Biostatistics and Medical Informatics, Institute of Health Sciences, Ege University（伊兹密尔大学健康科学学院生物统计学与医学信息学系）
Delay-and-sum (DAS) is the most widespread digital beamformer in high-frame-rate ultrasound imaging. Its implementation is simple and compatible with real-time applications. In this viewpoint article, we describe the fundamentals of DAS beamforming. The underlying theory and numerical approach are detailed so that users can be aware of its functioning and limitations. In particular, we discuss the importance of the f-number and speed of sound on image quality, and propose one solution to set their values from a physical viewpoint. We suggest determining the f-number from the directivity of the…
We present an adaptation of the pixel-reassignment technique from confocal fluorescent microscopy to coherent ultrasound imaging. The method, Ultrasound Pixel-Reassignment (UPR), provides a resolution and signal to noise (SNR) improvement in ultrasound imaging by computationally reassigning off-focus signals acquired using traditional plane-wave compounding ultrasonography. We theoretically analyze the analogy between the optical and ultrasound implementations of pixel reassignment, and experimentally evaluate the imaging quality on tissue-mimicking acoustic phantoms. We demonstrate that UPR p…
Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；University College London（伦敦大学学院）
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inac…
Pose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation
Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（牛津大学纳菲尔德妇女与生殖健康系）
We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the worldwide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane…
This is the first article in a series of two dealing with a matrix approach for aberration quantification and correction in ultrasound imaging. Advanced synthetic beamforming relies on a double focusing operation at transmission and reception on each point of the medium. Ultrasound matrix imaging (UMI) consists in decoupling the location of these transmitted and received focal spots. The response between those virtual transducers form the so-called focused reflection matrix that actually contains much more information than a confocal ultrasound image. In this paper, a time-frequency analysis o…
University College London（伦敦大学学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College London（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）
During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a uni…
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models
Concordia University（康考迪亚大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；INSA-Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Université de Lyon（里昂大学）
Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
Amrita School of Artificial Intelligence（阿米特学校人工智能学院）；Amrita Vishwa Vidyapeetham（阿米特世界大学）；Center for Artificial Intelligence（人工智能中心）；Prince Mohammed Bin Fahd University（普里姆·穆罕默德·本·法赫德大学）
School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University（深圳大学医学超声图像计算实验室）；CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, School of Computing, University of Leeds（利兹大学计算学院生物医学计算成像与仿真技术中心）
3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differe…
Shenzhen University（深圳大学）；University of Leeds（利兹大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳睿影医疗科技有限公司）
Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the…
Ultrasound imaging is the most popular medical imaging modality for point-of-care bedside imaging. However, 2D ultrasound imaging provides only limited views of the organ of interest, making diagnosis challenging. To overcome this, 3D ultrasound imaging was developed, which uses 2D ultrasound images and their orientation/position to reconstruct 3D volumes. The accurate position estimation of the ultrasound probe at low cost has always stood as a challenging task in 3D reconstruction. In this study, we propose a novel approach of using a mechanical track for ultrasound scanning, which restricts…
Shenzhen University（深圳大学）；Shenzhen Luohu People’s Hospital, the Third Affiliated Hospital of Shenzhen University（深圳市罗湖区人民医院（深圳大学第三附属医院））
Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagnosis. 2D US has to perform scanning for each SP, which is time-consuming and operator-dependent. While 3D US containing multiple SPs in one shot has the inherent advantages of less user-dependency and more efficiency. Automatically locating SP in 3D US is very challenging due to the huge search space and large fetal posture variations. Our previous study proposed a deep reinforcement learning (RL) framework with an al…
Sano Centre for Computational Medicine（萨诺计算医学中心）；Warsaw University of Technology（华沙理工大学）；Fetai Health Ltd.（Fetai健康有限公司）；Medical University of Warsaw（华沙医科大学）；Tooploox（Tooploox公司）
In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis. Fetal biometric measurement is a standard examination during pregnancy used for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Due to natural high speckle noise and shadows in ultrasound data, medical expertise and sonographic experience are requ…
