Acoustic Imaging of the Human Chest

doi:10.1378/chest.120.4.1309

Chest

Volume 120, Issue 4, October 2001, Pages 1309-1321

https://doi.org/10.1378/chest.120.4.1309 Get rights and content

Study objectives

A novel method for acoustic imaging of the human respiratory system is proposed and evaluated.

Design

The proposed imaging system uses simultaneous multisensor recordings of thoracic sounds from the chest wall, and digital, computer-based postprocessing. Computer simulations and recordings from a life-size gelatin model of the human thorax are used to evaluate the system in vitro. Spatial representations of thoracic sounds from 8-microphone and 16-microphone recordings from five subjects (four healthy male adults and one child with lung consolidation) are used to evaluate the system in vivo.

Results

Results of the in vitro studies show that sound sources can be imaged to within 2 cm, and that the proposed algorithm is reasonably robust with respect to changes in the assumed sound speed within the imaged volume. The images from recordings from the healthy volunteers show distinct patterns for inspiratory breath sounds, expiratory breath sounds, and heart sounds that are consistent with the assumed origin of the respective sounds. Specifically, the images support the concept that inspiratory sounds are produced predominantly in the periphery of the lung while expiratory sounds are generated more centrally. Acoustic images from the subject with lung consolidation differ substantially from the images of the healthy subjects, and localize the abnormality.

Conclusions

Acoustic imaging offers new perspectives to explore the acoustic properties of the respiratory system and thereby reveal structural and functional properties for diagnostic purposes.

Section snippets

Acoustics of the Human Thorax

The acoustic properties of the human thorax are complex and only partly understood. The chest consists of at least three components of substantially different acoustic qualities: solid tissue, airways, and lung parenchyma. Acoustic properties of the solid components of the thorax, such as the chest wall and the heart, are relatively well-known. Sound speeds in these tissues are approximately 1,500 m/s,¹⁰ and damping is relatively low. In the larger airways (ie, diameter of ≥ 1 mm) of animal

Requirements for an Acoustic Imaging System for the Human Thorax

Because of the complexity of the acoustic properties of the human thorax and practical limitations, a useful imaging system for the human lung and the underlying imaging algorithm should meet several goals: (1) the algorithm must be robust with respect to the acoustic properties within the thorax, most notably to changes in sound speed; (2) the algorithm should not rely on the measurement of the time of arrival of lung sound components; (3) the algorithm should provide three-dimensional data

Materials and Methods

Three methods were used to evaluate the acoustic imaging algorithm: computer simulations, imaging of a gelatin model of the human thorax, and imaging of human subjects using different numbers of microphones.

Computer Simulations

In these experiments, microphone signals were simulated by a computer program rather than recorded by microphones. Sixteen microphones were assumed to be in the same positions as were used later in multisite recordings from human subjects: 8 microphones in the front in two rows of 4

Imaging of Sound Sources

Figure 7 shows a spatial view of acoustic images from the computer simulations, showing the three cases of source point 1 active only (Fig 7, left, a), source point 2 active only (Fig 7, middle, b), and both sources active (Fig 7, right, c). Each image represents the view of a volume of 30 × 30 × 15 cm, overlying a (simulated) thorax. Data points are displayed on a regular three-dimensional grid with a spacing of 1 cm, and represented by spheres. Each data point can have a value between 0 and

Discussion

The results of this investigation suggest that the proposed algorithm can image sound sources adequately, both in the computer simulations (accuracy better than 1 cm) and in a life-size gelatin model of the human thorax (accuracy of 2 cm). Due to the heterogeneity of lung tissue and person-to-person differences, eg, in lung-muscle-fat ratios the same accuracy is not expected for humans. Nevertheless, these findings are encouraging, as they document that useful spatial information can be

Summary

A novel method for acoustic imaging of the human respiratory system was developed and evaluated. The system uses simultaneous multimicrophone recordings of thoracic sounds from the chest wall and a digital, computer-based postprocessing system. Computer simulations and recordings from a life-size gelatin model of the human thorax indicated that sound sources can be imaged to within 2 cm, and that the proposed algorithm is reasonably robust with respect to changes in the assumed sound speed

Acknowledgment

We thank Professor D. A. Rice, Department of Biomedical Engineering, Tulane University, for his help and advice concerning the gelatin model of the thorax.

References (22)

M Kompis et al.
A novel real-time noise reduction system for the assessment of evoked otoacoustic emissions
Comput Biol Med
(2000)
GR Wodicka et al.
Measurement of respiratory acoustic signals: effect of microphone air cavity depth
Chest
(1994)
JD Bronzino
The biomedical engineering handbook
(1995)
GR Wodicka et al.
A model of acoustic transmission in the respiratory system
IEEE Trans Biomed Eng
(1989)
RTH Laënnec
De l'auscultation médiate ou trité du diagnostic de maldies des poumons et du coeur, fondé principalement sur ce nouveau moyen d'exploration
(1819)
H Pasterkamp et al.
Respiratory sounds: advances beyond the stethoscope
Am J Respir Crit Care Med
(1997)
SS Kraman
Determination of the site of production of respiratory sounds by subtraction phonopneumography
Am Rev Respir Dis
(1980)
M Kompis et al.
Coherence of inspiratory and expiratory breath sounds as a function of inter-microphone distance
Proc Annu Int Conf IEEE Eng Med Biol Soc
(1995)
G Benedetto et al.
Surface distribution of crackling sounds
IEEE Trans Biomed Eng
(1988)
M Kompis et al.
Distribution of inspiratory and expiratory respiratory sound intensity on the surface of the human thorax
Proc Annu Int Conf IEEE Eng Med Biol Soc
(1997)

D Leong-Kon et al.

A system for real-time cardiac acoustic mapping

Proc Annu Int Conf IEEE Eng Med Biol Soc

(1998)

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: This work has been supported by the Swiss and the US National Science Foundations, the Ciba-Geigy-Jubiläums-Foundation, and the Children's Hospital of Winnipeg Foundation.

View full text

Chest

Laboratory and Animal InvestigationsAcoustic Imaging of the Human Chest

Study objectives

Design

Results

Conclusions

Section snippets

Acoustics of the Human Thorax

Requirements for an Acoustic Imaging System for the Human Thorax

Materials and Methods

Computer Simulations

Imaging of Sound Sources

Discussion

Summary

Acknowledgment

Comput Biol Med

Chest

The biomedical engineering handbook

A model of acoustic transmission in the respiratory system

IEEE Trans Biomed Eng

De l'auscultation médiate ou trité du diagnostic de maldies des poumons et du coeur, fondé principalement sur ce nouveau moyen d'exploration

Respiratory sounds: advances beyond the stethoscope

Am J Respir Crit Care Med

Determination of the site of production of respiratory sounds by subtraction phonopneumography

Am Rev Respir Dis

Coherence of inspiratory and expiratory breath sounds as a function of inter-microphone distance

Proc Annu Int Conf IEEE Eng Med Biol Soc

Surface distribution of crackling sounds

IEEE Trans Biomed Eng

Distribution of inspiratory and expiratory respiratory sound intensity on the surface of the human thorax

Proc Annu Int Conf IEEE Eng Med Biol Soc

A system for real-time cardiac acoustic mapping

Proc Annu Int Conf IEEE Eng Med Biol Soc

Laboratory and Animal Investigations
Acoustic Imaging of the Human Chest