In-Suit Doppler Technology Assessment

The objective of this program was to perform a technology assessment survey of non-invasive air embolism detection utilizing Doppler ultrasound methodologies. The primary application of this technology will be a continuous monitor for astronauts while performing extravehicular activities (EVA's). The technology assessment was to include: (1) development of a full understanding of all relevant background research; and (2) a survey of the medical ultrasound marketplace for expertise, information, and technical capability relevant to this development. Upon completion of the assessment, LSR was to provide an overview of technological approaches and R&D/manufacturing organizations

Volume Estimation by Wavelet Transform of Doppler Heart Sound During Venous Air-Embolism in Dogs

The Doppler heart sound signals detected by the precordial Doppler ultrasound method under simulated sub clinical and clinically significant venous air embolism were studied in anesthetized dogs. Signal processing using wavelet transform enhanced the contrast of embolic to normal signal, facilitating automatic detection and extraction of embolic signal simply by thresholding. Linear relationship of good correlation coefficient was obtained in log-log scale between the subclinical volume of injected air and the corresponding embolic signal power in all dogs. The calibration curve was found to be good estimate of the volume of embolic air during simulated clinically significant venous air embolism. Hence, we overcame the need of constant human attention for detecting venous air embolism and the lack of quantitative information on the volume of embolic air in the traditional precordial Doppler ultrasound method by the present approach.published_or_final_versio

Study of the Entropy as a Way to Detect Venous Air Embolism Using a Pre-cordial Doppler

Venous air embolism (VAE) is the air bubble accumulation in the right side of the heart, or in the pulmonary region. Pre-cordial Doppler allows a real-time monitoring of heart sound and is used to detect VAE episodes through changes in cardiac sound. Sometimes these changes are not detected by the operator, which reveals the importance of finding other robust methods for VAE detection. This work aims to study entropy as a feature of the heart sound that may provide useful information on VAE episodes. A clinical protocol was designed: Doppler Heart Sound (DHS) was collected at baseline, and following infusions of saline with 4 distinct volumes and 2 infusion rates, and given through 2 infusion vias, to 4 patients enrolled in the clinical study. Entropy of these segments was obtained, and relation between the extracted feature and saline infusions was studied. Entropy presents a good performance showing an increase in response to saline injections (increased blood flow turbulence)

An open-source framework for synthetic post-dive Doppler ultrasound audio generation

Doppler ultrasound (DU) measurements are used to detect and evaluate venous gas emboli (VGE) formed after decompression. Automated methodologies for assessing VGE presence using signal processing have been developed on varying real-world datasets of limited size and without ground truth values preventing objective evaluation. We develop and report a method to generate synthetic post-dive data using DU signals collected in both precordium and subclavian vein with varying degrees of bubbling matching field-standard grading metrics. This method is adaptable, modifiable, and reproducible, allowing for researchers to tune the produced dataset for their desired purpose. We provide the baseline Doppler recordings and code required to generate synthetic data for researchers to reproduce our work and improve upon it. We also provide a set of pre-made synthetic post-dive DU data spanning six scenarios representing the Spencer and Kisman-Masurel (KM) grading scales as well as precordial and subclavian DU recordings. By providing a method for synthetic post-dive DU data generation, we aim to improve and accelerate the development of signal processing techniques for VGE analysis in Doppler ultrasound

Transesophageal echocardiography for clinical decision making

Transesophageal echocardiography was initially developed to supplement an inadequate precordial echocardiographic examination. With high frequency transducers providing high resolution and detailed imaging, the technique has gained importance as a diagnostic tool in a considerable number of patients with cardiovascular disease, by providing unique information. In this study, the diagnostic utility and benefits of the application of transesophageal echocardiography in the clinical practice of cardiology are investigated. The first part of the study (Chapter 1) provides a review of the technological developments in transesophageal echocardiography. This is followed by a description of the comparative diagnostic value and limitations of precordial and transesophageal echocardiography. Subsequently, the transesophageal cross-sectional echocardiographic anatomy; the execution of the transesophageal procedure; the indications; the contraindications; the limitations; the technical perspectives and recommendations for training are described. Finally, a survey of the Thoraxcenter experience is presented. An overview of the diagnostic value of trans esophageal echocardiography in solving diverse clinical problems is discussed in Chapter 2. In Chapters 3 to 7 the unique advantages of transesophageal echocardiography are described for the diagnosis of thoracic aorta pathology, the assessment of native and Bjork Shiley mitral valve regurgitation by color Doppler flow imaging, for diagnosis of infective endocarditis, the detection of intracardiac thrombus, and visualization of the left coronary arter

Development of a graphical user interface for automatic separation of human voice from Doppler ultrasound audio in diving experiments

Doppler ultrasound (DU) is used in decompression research to detect venous gas emboli in the precordium or subclavian vein, as a marker of decompression stress. This is of relevance to scuba divers, compressed air workers and astronauts to prevent decompression sickness (DCS) that can be caused by these bubbles upon or after a sudden reduction in ambient pressure. Doppler ultrasound data is graded by expert raters on the Kisman-Masurel or Spencer scales that are associated to DCS risk. Meta-analyses, as well as efforts to computer-automate DU grading, both necessitate access to large databases of well-curated and graded data. Leveraging previously collected data is especially important due to the difficulty of repeating large-scale extreme military pressure exposures that were conducted in the 70-90s in austere environments. Historically, DU data (Non-speech) were often captured on cassettes in one-channel audio with superimposed human speech describing the experiment (Speech). Digitizing and separating these audio files is currently a lengthy, manual task. In this paper, we develop a graphical user interface (GUI) to perform automatic speech recognition and aid in Non-speech and Speech separation. This constitutes the first study incorporating speech processing technology in the field of diving research. If successful, it has the potential to significantly accelerate the reuse of previously-acquired datasets. The recognition task incorporates the Google speech recognizer to detect the presence of human voice activity together with corresponding timestamps. The detected human speech is then separated from the audio Doppler ultrasound within the developed GUI. Several experiments were conducted on recently digitized audio Doppler recordings to corroborate the effectiveness of the developed GUI in recognition and separations tasks, and these are compared to manual labels for Speech timestamps. The following metrics are used to evaluate performance: the average absolute differences between the reference and detected Speech starting points, as well as the percentage of detected

Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images

We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized