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

Analyze the Human Movements to Help CNS to Shape the Synergy using CNMF and Pattern Recognition

© 2017 The Authors. The Biomedical Signals have been studied for developing human control systems to improving the quality of life. The EMG signal is one of the main types of biomedical signals. It is a convoluted signal. This signal (EMG signal) controlled by the Central nervous system (CNS). It has been a long time expected that the human central nervous system (CNS) uses flexible combinations of some muscles synergy (MS) to solve and control redundant movements. Synergy muscles activities are different in a single muscle. In the concept of Synergy muscle, the CNS does not directly control the activation of a large number of muscles. There are two main movements can help CNS to shape the synergy. The automatic body response and the voluntary actions. These activities remain not too bright. Some studies support the hypothesis that the automatic body responses could be used as a reference to familiarize the voluntary efforts. It has been validating by analyzing the human voluntary movement and the automatic mechanical motions from the muscle synergy. Based on the validation, there was a proposition that the automatic synergy motion may express some features which could support the CNS to shape the voluntary synergy motion using the nonnegative matrix factorization (NMF). Thus the target of the presenting work is to analyses the human movements from the muscle synergy to help CNS shapes the synergy movement by suggestion using the concatenated non-negative matrix factorization (CNMF) method and the pattern recognition method. Then compare the two results and see if that help CNS to shape the synergy movements and which method has more accuracy

Characterization of anterior cerebral artery blood flow in resting state by using transcranial Doppler

Transcranial Doppler (TCD) recordings are used to monitor cerebral blood flow in main cerebral arteries. The resting state is usually characterized by using the mean velocity or the maximum Doppler shift frequency (an envelope signal) by insonating the middle cerebral arteries (MCAs). In this study, we characterized the cerebral blood flow in the anterior cerebral arteries (ACAs). We analyzed both the envelope signals and the raw signals obtained from bilateral insonation. We recruited 20 healthy subjects and conducted the data acquisition for 15 minutes. Features were extracted from the time domain, the frequency domain and the time-frequency domain. The results showed that gender-based statistical difference exists in the frequency domain and the time-frequency domain. However, no handedness effect was found. In the time domain, the information-theoretic features showed that the mutual dependence is higher in raw signals than in envelope signals. Finally, we concluded that insonating the ACA will serve as a complement of the MCA studies. Additionally, the investigation of the raw signals provided us with additional information that is not otherwise available from the envelope signals. The direct TCD raw-data utilization is therefore validated as a valuable resting-state characterization method

Design and Validation of an FPGA-Based Configurable Transcranial Doppler Neurofeedback System for Chronic Pain Patients

Neurofeedback is a self-regulation technique that can be applied to learn to voluntarily control cerebral activity in specific brain regions. In this work, a Transcranial Doppler-based configurable neurofeedback system is proposed and described. The hardware configuration is based on the Red Pitaya board, which gives great flexibility and processing power to the system. The parameter to be trained can be selected between several temporal, spectral, or complexity features from the cerebral blood flow velocity signal in different vessels. As previous studies have found alterations in these parameters in chronic pain patients, the system could be applied to help them to voluntarily control these parameters. Two protocols based on different temporal lengths of the training periods have been proposed and tested with six healthy subjects that were randomly assigned to one of the protocols at the beginning of the procedure. For the purposes of the testing, the trained parameter was the mean cerebral blood flow velocity in the aggregated data from the two anterior cerebral arteries. Results show that, using the proposed neurofeedback system, the two groups of healthy volunteers can learn to self-regulate a parameter from their brain activity in a reduced number of training sessions.This research was funded by Ministerio de Economía y Competitividad, Spain, grant number PSI2013-48260-C3-2-R. The APC was funded by Ministerio de Economía y Competitividad, Spain, grant number PSI2013-48260-C3-2-R

A Hybrid Brain-Computer Interface Based on Electroencephalography and Functional Transcranial Doppler Ultrasound

Hybrid brain computer interfaces (BCIs) combining multiple brain imaging modalities have been proposed recently to boost the performance of single modality BCIs. We advance the state of hybrid BCIs by introducing a novel system that measures electrical brain activity as well as cerebral blood flow velocity using Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD), respectively. The system we developed employs two different paradigms to induce changes simultaneously in EEG and fTCD and to infer user intent. One of these paradigms includes visual stimuli to simultaneously induce steady state visually evoked potentials (SSVEPs) and instructs users to perform word generation (WG) and mental rotation (MR) tasks, while the other paradigm instructs users to perform left and right arm motor imagery (MI) tasks through visual stimuli. To improve accuracy and information transfer rate (ITR) of the proposed system compared to those obtained through our preliminary analysis, using classical feature extraction approaches, we mainly contribute to multi-modal fusion of EEG and fTCD features. Specifically, we proposed a probabilistic fusion of EEG and fTCD evidences instead of simple concatenation of EEG and fTCD feature vectors that we performed in our preliminary analysis. Experimental results showed that the MI paradigm outperformed the MR/WG one in terms of both accuracy and ITR. In particular, 93.85%, 93.71%, and 100% average accuracies and 19.89, 26.55, and 40.83 bits/min v average ITRs were achieved for right MI vs baseline, left MI versus baseline, and right MI versus left MI, respectively. Moreover, for both paradigms, the EEG-fTCD BCI with the proposed analysis techniques outperformed all EEG- fNIRS BCIs in terms of accuracy and ITR. In addition, to investigate the feasibility of increasing the possible number of BCI commands, we extended our approaches to solve the 3-class problems for both paradigms. It was found that the MI paradigm outperformed the MR/WG paradigm and achieved 96.58% average accuracy and 45 bits/min average ITR. Finally, we introduced a transfer learning approach to reduce the calibration requirements of the proposed BCI. This approach was found to be very efficient especially with the MI paradigm as it reduced the calibration requirements by at least 60.43%

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Applications of Hybrid Diffuse Optics for Clinical Management of Adults After Brain injury

Information about cerebral blood flow (CBF) is valuable for clinical management of patients after severe brain injury. Unfortunately, current modalities for monitoring brain are often limited by hurdles that include high cost, low throughput, exposure to ionizing radiation, probe invasiveness, and increased risk to critically ill patients when transportation out of their room or unit is required. A further limitation of current technologies is an inability to provide continuous bedside measurements that are often desirable for unstable patients. Here we explore the clinical utility of diffuse correlation spectroscopy (DCS) as an alternative approach for bedside CBF monitoring. DCS uses the rapid intensity fluctuations of near-infrared light to derive a continuous measure of changes in blood flow without ionizing radiation or invasive probing. Concurrently, we employ another optical technique, called diffuse optical spectroscopy (DOS), to derive changes in cerebral oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) concentrations. Our clinical studies integrate DCS with DOS into a single hybrid instrument that simultaneously monitors CBF and HbO2/Hb in the injured adult brain. The first parts of this dissertation present the motivations for monitoring blood flow in injured brain, as well as the theory underlying diffuse optics technology. The next section elaborates on details of the hybrid instrumentation. The final chapters describe four human subject studies carried out with these methods. Each of these studies investigates an aspect of the potential of the hybrid monitor in clinical applications involving adult brain. The studies include: (1) validation of DCS-measured CBF against xenon-enhanced computed tomography in brain-injured adults; (2) a study of the effects of age and gender on posture-change-induced CBF variation in healthy subjects; (3) a study of the efficacy of DCS/DOS for monitoring neurocritical care patients during various medical interventions such as head-of-bed manipulation and induced hyperoxia; and (4) a first feasibility study for using DCS to study hemodynamics at high altitudes. The work presented in this dissertation thus further develops DCS/DOS technology and demonstrates its utility for monitoring the injured adult brain. It demonstrates the promise of this new clinical tool to help neurocritical care clinicians make more informed decisions and thereby improve patient outcome