Respiratory acoustic thoracic imaging (RATHI): Assessing deterministic interpolation techniques

Abstract

As respiratory sounds contain mechanical and clinical pulmonary information, technical efforts have been devoted during the past decades to analysing, processing and visualising them. The aim of this work was to evaluate deterministic interpolating functions to generate surface respiratory acoustic thoracic images (RATHIs), based on multiple acoustic sensors. Lung sounds were acquired from healthy subjects through a 5 x 5 microphone array on the anterior and posterior thoracic surfaces. The performance of five interpolating functions, including the linear, cubic spline, Hermite, Lagrange and nearest neighbour method, were evaluated to produce images of lung sound intensity during both breathing phases, at low (approximately 0.5ls(-1)) and high (approximately 1.0ls(-1)) airflows. Performance indexes included the normalised residual variance nrv (i.e. inaccuracy), the prediction covariance cv (i.e. precision), the residual covariance rcv (i.e. bias) and the maximum squared residual error semax (i.e. tolerance). Among the tested interpolating functions and in all experimental conditions, the Hermite function (nrv=0.146 +/- 0.059, cv= 0.925 +/- 0.030, rcv = -0.073 +/- 0.068, semax = 0.005 +/- 0.004) globally provided the indexes closest to the optimum, whereas the nearest neighbour (nrv=0.339 +/- 0.023, cv = 0.870 +/- 0.033, rcv= 0.298 +/- 0.032, semax = 0.007 +/- 0.005) and the Lagrange methods (nrv = 0.287 +/- 0.148, cv = 0.880 +/- 0.039, rcv = -0.524 +/- 0.135, semax = 0.007 +/- 0.0001) presented the poorest statistical measurements. It is concluded that, although deterministic interpolation functions indicate different performances among tested techniques, the Hermite interpolation function presents a more confident deterministic interpolation for depicting surface-type RATHI.

Dynamic Visualization of Lung Sounds with a Vibration Response Device: A Case Series

“…Other researchers have developed lung analysis tools that can potentially be used in standard clinical practice from a practical point of view – ease of sensor placement [2], data display [2, 9, 15, 24] and detection of abnormal lung sounds [25, 26]. Those devices and the VRI device still require further evaluation regarding aspects of day-to-day practice: ease of use, duration of the overall process, display analysis and reading by nonresearchers of lung sounds, and reproducibility and reliability.…”

“…However, due to the imaging algorithm running on nonspecialized computer hardware, the computation load was too high for such an application. More recent work by Charleston-Villalobos et al [9] addressed the issue of low spatial resolution in previous studies of lung sound intensity mapping. These authors evaluated deterministic interpolating functions in generating acoustic thoracic images with higher spatial resolution.…”

A comprehensive computational model of sound transmission through the porcine lung

“…[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.…”

Comparison of Poroviscoelastic Models for Sound and Vibration in the Lungs

“…Lung functional and structural imaging based on an array of contact acoustic sensors placed on the back has been researched for the past decade or so [9][10][11] and has recently gained more prominence through the burgeoning success of such systems as Deep Breeze TM , a commercial product utilizing up to 40 vacuummounted contact acoustic sensors on the patient's back or integrated into their bed to provide a real-time assessment of lung sound strength, spectral content, and regional variation, all of which may be beneficial to diagnosis [12][13][14]. Beyond obtaining an image that depicts the distribution of lung sounds on the torso surface, if a better understanding of mechanical wave propagation within the lungs and torso were available, one may be able to reconstruct the wave field within the lungs and torso based on the noninvasive surface measurements.…”