937 resultados para Airway segmentation
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National Highway Traffic Safety Administration, Washington, D.C.
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"COO-2118-0028."
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Typescript.
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Includes bibliographies (p. 31).
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Mode of access: Internet.
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Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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Objective: To evaluate the effectiveness of continuous positive airway pressure (CPAP) therapy in the treatment of hypernasality following traumatic brain injury (17111). Design: An A-B-A experimental research design. Assessments were conducted prior to commencement of the program, midway, immediately posttreatment, and 1 month after completion of the CPAP therapy program. Participants: Three adults with dysarthria and moderate to severe hypernasality subsequent to TBI. Outcome Measures: Perceptual evaluation using the Frenchay Dysarthria Assessment, the Assessment of Intelligibility of Dysarthric Speech, and a speech sample analysis, and instrumental evaluation using the Nasometer. Results: Between assessment periods, varying degrees of improvement in hypernasality and sentence intelligibility were noted. At the 1-month post-CPAP assessment, all 3 participants demonstrated reduced nasalance values, and 2 exhibited increased sentence intelligibility. Conclusions: CPAP may be a valuable treatment of impaired velopharyngeal function in the TBI population.
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Background and purpose: Patients' knowledge and beliefs about their illnesses are known to influence a range of health related variables, including treatment compliance. It may, therefore, be important to quantify these variables to assess their impact on compliance, particularly in chronic illnesses such as Obstructive Sleep Apnea (OSA) that rely on self-administered treatments. The aim of this study was to develop two new tools, the Apnea Knowledge Test (AKT) and the Apnea Beliefs Scale (ABS), to assess illness knowledge and beliefs in OSA patients. Patients and methods: The systematic test construction process followed to develop the AKT and the ABS included consultation with sleep experts and OSA patients. The psychometric properties of the AKT and ABS were then investigated in a clinical sample of 81 OSA patients and 33 healthy, non-sleep disordered adults. Results: Results suggest both measures are easily understood by OSA patients, have adequate internal consistency, and are readily accepted by patients. A preliminary investigation of the validity of these tools, conducted by comparing patient data to that of the 33 healthy adults, revealed that apnea patients knew more about OSA, had more positive attitudes towards continuous positive airway pressure (CPAP) treatment, and attributed more importance to treating sleep disturbances than non-clinical groups. Conclusions: Overall, the results of psychometric analyses of these tests suggest these measures will be useful clinical tools with numerous beneficial applications, particularly in CPAP compliance studies and apnea education program evaluations. (C) 2004 Elsevier B.V. All rights reserved.
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Background and objective: There are no data about the influence of anaesthetics on cardiovascular variables during pressure support ventilation of the lungs through the laryngeal mask airway. We compared propofol, sevoflurane and isoflurane for maintenance of anaesthesia with the ProSeal (R) laryngeal mask airway during pressure support ventilation. Methods: Sixty healthy adults undergoing peripheral musculo-skeletal surgery were randomized for maintenance with sevoflurane end-tidal 29%, isoflurane end-tidal 1.1% or propofol 6 mg kg(-1) h(-1) in oxygen 33% and air. Pressure support ventilation comprised positive end-expiratory pressure set at 5 cmH(2)O, and pressure support set 5 cmH(2)O above positive end-expiratory pressure. Pressure support was initiated when inspiration produced a 2 cmH(2)O reduction in airway pressure. A blinded observer recorded cardiorespiratory variables (heart rate, mean blood pressure, oxygen saturation, air-way occlusion pressure, respiratory rate, expired tidal volume, expired minute volume and end-tidal CO2), adverse events and emergence times. Results: Respiratory rate and minute volume were 10-21% lower, and end-tidal CO2 6-11% higher with the propofol group compared with the sevoflurane or isoflurane groups, but otherwise cardiorespiratory variables were similar among groups. No adverse events occurred in any group. Emergence times were longer with the propofol group compared with the sevoflurane or isoflurane groups (10 vs. 7 vs. 7 min). Conclusion: Lung ventilation is less effective and emergence times are longer with propofol than sevoflurane or isoflurane for maintenance of anaesthesia during pressure support ventilation with the ProSeal (R) laryngeal mask airway. However, these differences are small and of doubtful clinical importance.
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This paper considers the problem of tissue classification in 3D MRI. More specifically, a new set of texture features, based on phase information, is used to perform the segmentation of the bones of the knee. The phase information provides a very good discrimination between the bone and the surrounding tissues, but is usually not used due to phase unwrapping problems. We present a method to extract textural information from the phase that does not require phase unwrapping. The textural information extracted from the magnitude and the phase can be combined to perform tissue classification, and used to initialise an active shape model, leading to a more precise segmentation.