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体声定位。

Localization of adventitious respiratory sounds.

机构信息

Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, 851 South Morgan Street, MC 063, Chicago, Illinois 60607, USA.

出版信息

J Acoust Soc Am. 2018 Mar;143(3):1297. doi: 10.1121/1.5025842.

Abstract

In a recent publication by Henry and Royston [J. Acoust. Soc. Am. 142, 1774-1783 (2017)], an algorithm was introduced to calculate the acoustic response to externally introduced and endogenous respiratory sounds within a realistic, patient-specific subglottal airway tree. This work is extended using an efficient numerical boundary element (BE) approach to calculate the resulting radiated sound field from the airway tree into the lung parenchyma taking into account the surrounding chest wall. Within the BE model of the left lung parenchyma, comprised of more than 6000 triangular surface elements, more than 30 000 monopoles are used to approximate complex airway-originated acoustic sources. The chest wall is modeled as a boundary condition on the parenchymal surface. Several cases were simulated, including a bronchoconstricted lung that had an internal acoustic source introduced in a bronchiole, approximating a wheeze. An acoustic source localization algorithm coupled to the BE model estimated the wheeze source location to within a few millimeters based solely on the acoustic field at the surface. Improved noninvasive means of locating adventitious respiratory sounds may enhance an understanding of acoustic changes correlated to pathology, and potentially provide improved noninvasive tools for the diagnosis of pulmonary diseases that uniquely alter acoustics.

摘要

在 Henry 和 Royston 的最近出版物中[J. Acoust. Soc. Am. 142, 1774-1783 (2017)],引入了一种算法,用于计算在真实、特定于患者的声门下气道树内外部引入和内源性呼吸声的声响应。这项工作通过使用高效的数值边界元 (BE) 方法进行扩展,以计算气道树进入肺实质的辐射声场,同时考虑到周围的胸壁。在由超过 6000 个三角形表面元素组成的左肺实质 BE 模型中,使用超过 30000 个单极子来近似复杂的气道起源声源。胸壁被建模为实质表面上的边界条件。模拟了几种情况,包括在支气管中引入内部声源的支气管收缩肺,模拟喘息声。声学源定位算法与 BE 模型耦合,仅根据表面的声场,将喘息源位置估计在几毫米内。改进的定位偶然呼吸声的非侵入性方法可以增强对与病理学相关的声学变化的理解,并可能为独特改变声学的肺部疾病提供改进的非侵入性诊断工具。

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