Peng Ying, Dai Zoujun, Mansy Hansen A, Sandler Richard H, Balk Robert A, Royston Thomas J
Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, 842 W. Taylor St, 2039 ERF, Chicago, IL, 60607, USA,
Med Biol Eng Comput. 2014 Aug;52(8):695-706. doi: 10.1007/s11517-014-1172-8. Epub 2014 Jul 8.
Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest, while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions.
胸部体格检查通常包括进行胸部叩诊,即向胸壁施加声音刺激并检测可听变化。这种方法基于这样的观察:由于病理状况导致的胸部结构变化会改变潜在的声学传播、耦合和共振模式。对这些声学改变进行更准确的检测和量化可能会提供更多有用的诊断信息。为了阐明其中涉及的物理过程,一个逼真的胸部声音传播计算机模型会有所帮助。在本研究中,开发了一个计算模型,并通过将其预测结果与动物和人体实验结果进行比较来进行验证,这些实验包括向前胸部施加声学激励,同时在后胸部检测皮肤振动。为了研究病理状况对声音传播的影响,使用该计算模型模拟气胸对在前胸部引入并在后胸部检测到的声音的影响。模型预测结果与实验结果显示出相似的趋势。该模型还预测了胸部内部的波形,可用于评估弹性成像测量结果。未来的动物和人体测试可能会扩大该模型的预测能力,使其涵盖更广泛肺部状况下的声学行为。