Chest surface mapping of lung sounds during methacholine challenge

Pasterkamp, Hans; Consunji-Araneta, Raquel; Oh, Yong─Geun; Holbrow, Jessica

doi:10.1002/(sici)1099-0496(199701)23:1<21::aid-ppul3>3.0.co;2-s

Cited by 54 publications

(36 citation statements)

References 22 publications

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“…In recent years, many researchers have applied more quantitative measurement and analysis techniques to increase the diagnostic utility of this approach, utilizing electronic sensors and applying computational signal processing and statistical analyses to the measured signals to discern trends or biases correlated with pathologies. [2][3][4][5][6][7][8][9][10][11][12][13][14] To reap the full potential of the inherently rich source of diagnostic information within the audible frequency regime will require a better fundamental understanding of: ͑1͒ the acoustic source and its relation to pathology, ͑2͒ the acoustic path from the source to the sensor, which can be far more complex at sonic than ultrasonic frequencies due to the potential for multiple reflections, multiple propagating wave types, and multipath behavior, and ͑3͒ the use of more accurate and multiple measurement sensors. ͑4͒ It could also require more sophisticated and spatially resolved computational processing of the measured signals that considers multipath propagation of the acoustic event from its source to the sensor location to reconstruct a sonic image ingrained with quantitative information.…”

Section: A Backgroundmentioning

confidence: 99%

Boundary element model for simulating sound propagation and source localization within the lungs

Özer

Acikgoz

Royston

et al. 2007

The Journal of the Acoustical Society of America

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An acoustic boundary element (BE) model is used to simulate sound propagation in the lung parenchyma. It is computationally validated and then compared with experimental studies on lung phantom models. Parametric studies quantify the effect of different model parameters on the resulting acoustic field within the lung phantoms. The BE model is then coupled with a source localization algorithm to predict the position of an acoustic source within the phantom. Experimental studies validate the BE-based source localization algorithm and show that the same algorithm does not perform as well if the BE simulation is replaced with a free field assumption that neglects reflections and standing wave patterns created within the finite-size lung phantom. The BE model and source localization procedure are then applied to actual lung geometry taken from the National Library of Medicine's Visible Human Project. These numerical studies are in agreement with the studies on simpler geometry in that use of a BE model in place of the free field assumption alters the predicted acoustic field and source localization results. This work is relevant to the development of advanced auscultatory techniques that utilize multiple noninvasive sensors to construct acoustic images of sound generation and transmission to identify pathologies.

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Section: A Backgroundmentioning

confidence: 99%

Boundary element model for simulating sound propagation and source localization within the lungs

Özer

Acikgoz

Royston

et al. 2007

The Journal of the Acoustical Society of America

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show abstract

“…[3][4][5][6] Indeed simultaneous, multi-sensor auscultation methods have been developed to "map" sounds on the thoracic surface by several groups. 5,[7][8][9][10][11] Also recently the phase contrast-based technique known as magnetic resonance elastography (MRE) has been applied to the lungs in pilot studies with limited success. [12][13][14][15] MRE seeks to provide a map of the viscoelastic properties within the region of interest that will affect the shear wave motion that MRE measures.…”

Section: Introduction a Motivationmentioning

confidence: 99%

A comprehensive computational model of sound transmission through the porcine lung

Dai

Peng

Henry

et al. 2014

The Journal of the Acoustical Society of America

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A comprehensive computational simulation model of sound transmission through the porcine lung is introduced and experimentally evaluated. This "subject-specific" model utilizes parenchymal and major airway geometry derived from x-ray CT images. The lung parenchyma is modeled as a poroviscoelastic material using Biot theory. A finite element (FE) mesh of the lung that includes airway detail is created and used in COMSOL FE software to simulate the vibroacoustic response of the lung to sound input at the trachea. The FE simulation model is validated by comparing simulation results to experimental measurements using scanning laser Doppler vibrometry on the surface of an excised, preserved lung. The FE model can also be used to calculate and visualize vibroacoustic pressure and motion inside the lung and its airways caused by the acoustic input. The effect of diffuse lung fibrosis and of a local tumor on the lung acoustic response is simulated and visualized using the FE model. In the future, this type of visualization can be compared and matched with experimentally obtained elastographic images to better quantify regional lung material properties to noninvasively diagnose and stage disease and response to treatment.

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“…Considering the diagnosis of airway hyperresponsivity or asthma, decrease in intensity of lung sounds has been found to accompany induced bronchial narrowing, and more consistently than wheeze [7,19]. However, objective measurement of changes in lung sounds without adventitious sounds generally necessitates at least two recordings, e.g.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic potential in state space parameters of lung sounds

2007

Self Cite

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The goal of this study was to investigate state space parameters of the lung sounds of healthy subjects and subjects with symptoms of asthma under different respiratory conditions. Our main objective was to elucidate the diagnostic potential of these parameters, which included embedding dimension (m), time delay (tau) and Lyapunov exponents (lambda). Lung sounds were acquired over the right lower lobe from six healthy subjects, ages 10-26 years, and from eight children with symptoms of asthma recorded pre- and post-bronchial provocation via methacholine challenge (MCh) and post-bronchial dilation (BD). Inspiratory air flows during recordings were 7.5, 15, or 22.5 mL/s per kg (+/-20%). With increasing flow for sounds recorded from healthy subjects, mean values of tau decreased. Percent of breaths with positive lambda decreased when heart sounds were excluded. For the patients who exhibited bronchoconstriction, values of tau increased and percent of positive lambda decreased post-MCh, and returned to pre-MCh values post-BD. Thus, both tau and presence of positive lambda may prove valuable in developing a model that will predict changes in respiratory status using lung sounds.

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Chest surface mapping of lung sounds during methacholine challenge

Cited by 54 publications

References 22 publications

Boundary element model for simulating sound propagation and source localization within the lungs

Boundary element model for simulating sound propagation and source localization within the lungs

A comprehensive computational model of sound transmission through the porcine lung

Diagnostic potential in state space parameters of lung sounds

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