Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images

Ultrasound imaging exhibits considerable difficulties for medical visual inspection and for the development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this work, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. Specifically, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Stable automatic envelope estimation for noisy doppler ultrasound

Doppler ultrasound technology is widespread in clinical applications and is principally used for blood flow measurements in the heart, arteries and veins. A commonly extracted parameter is the maximum velocity envelope. However, current methods of extracting it cannot produce stable envelopes in high noise conditions. This can limit clinical and research applications using the technology. In this article, a new method of automatic envelope estimation is presented. The method can handle challenging signals with high levels of noise and variable envelope shapes. Envelopes are extracted from a Doppler spectrogram image generated directly from the Doppler audio signal, making it less device-dependent than existing imageprocessing methods. The method’s performance is assessed using simulated pulsatile flow, a flow phantom and in-vivo ascending aortic flow measurements and is compared with three state-of-the-art methods. The proposed method is the most accurate in noisy conditions, achieving on average for phantom data with SNRs below 10 dB, a bias and standard deviation 0.7% and 3.3% lower than the next-best performing method. In addition, a new method for beat segmentation is proposed. When combined, the two proposed methods exhibited the best performance using invivo data, producing the least number of incorrectly segmented beats and 8.2% more correctly segmented beats than the next best performing method. The ability of the proposed methods to reliably extract timing indices for cardiac cycles across a range of signal quality is of particular significance for research and monitoring applications

医用超音波における散乱体分布の高解像かつ高感度な画像化に関する研究

Ultrasound imaging as an effective method is widely used in medical diagnosis andNDT (non-destructive testing). In particular, ultrasound imaging plays an important role in medical diagnosis due to its safety, noninvasive, inexpensiveness and real-time compared with other medical imaging techniques. However, in general the ultrasound imaging has more speckles and is low definition than the MRI (magnetic resonance imaging) and X-ray CT (computerized tomography). Therefore, it is important to improve the ultrasound imaging quality. In this study, there are three newproposals. The first is the development of a high sensitivity transducer that utilizes piezoelectric charge directly for FET (field effect transistor) channel control. The second is a proposal of a method for estimating the distribution of small scatterers in living tissue using the empirical Bayes method. The third is a super-resolution imagingmethod of scatterers with strong reflection such as organ boundaries and blood vessel walls. The specific description of each chapter is as follows: Chapter 1: The fundamental characteristics and the main applications of ultrasound are discussed, then the advantages and drawbacks of medical ultrasound are high-lighted. Based on the drawbacks, motivations and objectives of this study are stated. Chapter 2: To overcome disadvantages of medical ultrasound, we advanced our studyin two directions: designing new transducer improves the acquisition modality itself, onthe other hand new signal processing improve the acquired echo data. Therefore, the conventional techniques related to the two directions are reviewed. Chapter 3: For high performance piezoelectric, a structure that enables direct coupling of a PZT (lead zirconate titanate) element to the gate of a MOSFET (metal-oxide semiconductor field-effect transistor) to provide a device called the PZT-FET that acts as an ultrasound receiver was proposed. The experimental analysis of the PZT-FET, in terms of its reception sensitivity, dynamic range and -6 dB reception bandwidth have been investigated. The proposed PZT-FET receiver offers high sensitivity, wide dynamic range performance when compared to the typical ultrasound transducer. Chapter 4: In medical ultrasound imaging, speckle patterns caused by reflection interference from small scatterers in living tissue are often suppressed by various methodologies. However, accurate imaging of small scatterers is important in diagnosis; therefore, we investigated influence of speckle pattern on ultrasound imaging by the empirical Bayesian learning. Since small scatterers are spatially correlated and thereby constitute a microstructure, we assume that scatterers are distributed according to the AR (auto regressive) model with unknown parameters. Under this assumption, the AR parameters are estimated by maximizing the marginal likelihood function, and the scatterers distribution is estimated as a MAP (maximum a posteriori) estimator. The performance of our method is evaluated by simulations and experiments. Through the results, we confirmed that the band limited echo has sufficient information of the AR parameters and the power spectrum of the echoes from the scatterers is properly extrapolated. Chapter 5: The medical ultrasound imaging of strong reflectance scatterers based on the MUSIC algorithm is the main subject of Chapter 5. Previously, we have proposed a super-resolution ultrasound imaging based on multiple TRs (transmissions/receptions) with different carrier frequencies called SCM (super resolution FM-chirp correlation method). In order to reduce the number of required TRs for the SCM, the method has been extended to the SA (synthetic aperture) version called SA-SCM. However, since super-resolution processing is performed for each line data obtained by the RBF (reception beam forming) in the SA-SCM, image discontinuities tend to occur in the lateral direction. Therefore, a new method called SCM-weighted SA is proposed, in this version the SCM is performed on each transducer element, and then the SCM result is used as the weight for RBF. The SCM-weighted SA can generate multiple B-mode images each of which corresponds to each carrier frequency, and the appropriate low frequency images among them have no grating lobes. For a further improvement, instead of simple averaging, the SCM applied to the result of the SCM-weighted SA for all frequencies again, which is called SCM-weighted SA-SCM. We evaluated the effectiveness of all the methods by simulations and experiments. From the results, it can be confirmed that the extension of the SCM framework can help ultrasound imaging reduce grating lobes, perform super-resolution and better SNR(signal-to-noise ratio). Chapter 6: A discussion of the overall content of the thesis as well as suggestions for further development together with the remaining problems are summarized.首都大学東京, 2019-03-25, 博士（工学）首都大学東

An improved classification approach for echocardiograms embedding temporal information

Cardiovascular disease is an umbrella term for all diseases of the heart. At present, computer-aided echocardiogram diagnosis is becoming increasingly beneficial. For echocardiography, different cardiac views can be acquired depending on the location and angulations of the ultrasound transducer. Hence, the automatic echocardiogram view classification is the first step for echocardiogram diagnosis, especially for computer-aided system and even for automatic diagnosis in the future. In addition, heart views classification makes it possible to label images especially for large-scale echo videos, provide a facility for database management and collection. This thesis presents a framework for automatic cardiac viewpoints classification of echocardiogram video data. In this research, we aim to overcome the challenges facing this investigation while analyzing, recognizing and classifying echocardiogram videos from 3D (2D spatial and 1D temporal) space. Specifically, we extend 2D KAZE approach into 3D space for feature detection and propose a histogram of acceleration as feature descriptor. Subsequently, feature encoding follows before the application of SVM to classify echo videos. In addition, comparison with the state of the art methodologies also takes place, including 2D SIFT, 3D SIFT, and optical flow technique to extract temporal information sustained in the video images. As a result, the performance of 2D KAZE, 2D KAZE with Optical Flow, 3D KAZE, Optical Flow, 2D SIFT and 3D SIFT delivers accuracy rate of 89.4%, 84.3%, 87.9%, 79.4%, 83.8% and 73.8% respectively for the eight view classes of echo videos

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.publishedVersio