David T. Porembka

Knowledge-based image analysis for automated boundary extraction of transesophageal echocardiographic left-ventricular images

A system for automatically determining the contour of the left ventricle (LV) and its bounded area, from transesophageal echocardiographic (TEE) images is presented. It uses knowledge of both heart anatomy and echocardiographic imaging to guide the selection of image processing methodologies for thresholding, edge detection, and contour following and the center-based boundary-finding technique to extract the contour of the LV region. To speed up the processing a rectangular region of interest from a TEE picture is first isolated and then reduced to a coarse version, one-ninth original size. All processing steps, except the final contour edge extraction, are performed on this reduced image. New methods developed for automatic threshold selection, region segmentation, noise removal, and region center determination are described.

A suggested curriculum in echocardiography for critical care physicians

Echocardiography is a powerful diagnostic and monitoring tool of cardiac performance, cardiac pathology, and extracardiac intrathoracic abnormalities. Numerous investigations in intensive care have shown its merit, being efficacious and safe. Because many obvious and/or unsuspected conditions can impact the hemodynamic status of critically ill patients, echocardiography is becoming an integral part of an intensivist’s diagnostic and monitoring armamentarium. However, significant background information, cognitive, and technical skills are required to properly perform and interpret echocardiography images. Some education and training guidelines for echocardiography have been developed while others remain “in progress.” This manuscript suggests a core curriculum and necessary training elements for intensivists. This curriculum does not segregate portable handheld surface echocardiography from the typical platforms of transthoracic echocardiography and transesophageal echocardiography, because hardware and software developments have bridged these technologies.

Penetrating Cardiac Trauma: A Perioperative Role for Transesophageal Echocardiography

A previously healthy, 36-yr-old male sustained a stab wound to the chest. In the emergency room the patient’s blood pressure was 100/80 mm Hg. The pulse rate was 86 beats/min, and the respiratory rate was 20 breaths/min. Physical examination revealed a 3.0-cm wound located approximately 2.0 cm laterally to the left nipple. Breath sounds were clear and equal bilaterally, and heart sounds were regular but slightly decreased without any obvious murmur, rubs, or gallops. The jugular veins were distended bilaterally. A diagnostic peritoneal lavage was negative. The chest radiograph revealed an enlarged cardiac silhouette. The electrocardiogram was unremarkable. A technically adequate transthoracic echocardiogram revealed normal chamber sizes and normal systolic function. There was a moderate amount of pericardial fluid visualized without any echocardiographic evidence of tamponade (i.e., right atrial and right ventricular diastolic collapse). Doppler color flow study showed no defects, intracardiac shunts, or regurgitant lesions. A subxiphoid pericardiotomy was performed, yielding about 80-100 mL of blood-tinged fluid. A median sternotomy was performed, and a 2.0-cm stab wound to the right ventricle was oversewn. A new thrill was palpated intraoperatively. Also, a new murmur was auscultated in the intensive care unit. A transesophageal echocardiography examination was then performed in the intensive care unit, showing a 2-mm area of echo dropout in the ascending aorta located approximately 5 mm laterally to the origin of the right coronary artery. The following day, a technically limited transthoracic echocardiographic examination revealed no defect. A repeat biplane transesophageal echocardiogram was performed,

Analysis of catecholamine and vasoactive peptide release in intracranial arterial venous malformations

Craniotomy for resection of cerebral arterial venous malformation has been associated with postoperative hypertension, which necessitates administration of large doses of antihypertensive medications to control blood pressure. Controlling blood pressure is essential because hypertensive episodes can lead to postoperative cerebral hemorrhage with increases in morbidity and mortality. We measured vasoactive peptide and catecholamine release in 13 patients who underwent resection of an arterial venous malformation and in a control group of 6 patients who presented for clipping of unruptured cerebral aneurysms. Plasma renin activity, angiotensin I and II, vasopressin, aldosterone, epinephrine, and norepinephrine levels were measured intraoperatively and for 36 h postoperatively. Analysis of variance was used to assess sample and group effects. A significant interaction between sample and groups was found for norepinephrine (p < 0.001) and renin (p = 0.002). Our data suggest that elevated plasma renin and norepinephrine levels are in part responsible for postoperative hypertension in patients undergoing resection of arterial venous malformations. Blocking the release of these hormones may help control blood pressure after surgery for removal of arterial venous malformations.

ANN approach for 2-D echocardiographic image processing: application of neocognitron model to LV boundary formation

An application of neocognitron to the formation of a closed boundary contour of the left ventricle (LV) from line segments in echocardiographic images is presented. In echocardiographic image processing, the extraction of a closed boundary contour is a very difficult but important task for both automatic diagnosing and monitoring of heart functions. The two major problems are the extraction of complex contour edge pattern structures from noisy edge streaks and the reconstruction of missing edges. The first problem can be cast as a feature extraction problem, and the second one can be solved by the top-down model-based approach using the existing contextual information. The neocognitron model which has a hierarchical structure and forward and backward paths provides solutions to these two problems. The recognition of an input pattern is carried out sequentially, layer by layer along the forward path. The forward signals route is later retraced by the backward signals. The noise edges in the input pattern, which are not retraced in the backward route, are eliminated by the gain control mechanism. The incomplete features, which appear in the backward route, but might not be extracted in the forward route, also can be extracted by lowering the threshold value. The missing edge in the initial pattern can be filled back once the feature corresponding to that edge has been reconstructed. This neocognitron network has three layers: the lower, the intermediate, and the highest, containing the templates for basic geometrical features, basic curve features, and whole boundary contour, respectively. The models of the left ventricle boundary are created semi-automatically from real echocardiographic images. A model is established for each class of images based on the shape of the boundary contour which is defined according to age, gender, disease, probe position, and the time of contraction or expansion. The curve features are extracted from the boundary contour model semi-automatically.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.