Vibration response imaging technology in healthy subjects.

OBJECTIVE: The vibration response imaging device that we studied (VRIxp) records the intensity and location of lung sounds during a cycle of breathing. The goals of this study were to describe the characteristic features and quantitative lung data recorded by the VRIxp device from healthy asymptomatic subjects. SUBJECTS AND METHODS: Breath sounds (frequency range, 150-250 Hz) recorded from the backs of 151 healthy asymptomatic subjects (96 nonsmokers and 55 smokers) by the VRIxp device were mapped to create a sequence of 2D images. Three raters interpreted and scored the images for predefined static and dynamic features. In addition, quantitative lung data were analyzed for characteristic regional distributions. RESULTS: The readers of the images had good inter- and intrarater agreement. Image development in 93% of the evaluations showed an inspiratory and expiratory phase with a progressive and regressive stage that developed bilaterally in a vertical and synchronized manner. Characteristic image features of the maximum energy frame included a smooth, rounded, uninterrupted contour and a planar distribution, area size, and intensity that had right-left symmetry. Quantitative lung data expressed as percentages of the total (100%) vibration energy were normally distributed with mean values (+/- SD) of 55% +/- 6% for the left lung and 45% +/- 6% for the right lung. Most of the subjects with images, quantitative lung data, or both lacking these typical features were cigarette smokers or had a history of smoking (p < 0.05). CONCLUSION: Breath sounds in healthy asymptomatic subjects can be recorded and displayed in a dynamic series of images that have predictable and characteristic features recognizable and complemented by quantitative lung data. Identification and description of these characteristic image features in this study will facilitate future studies of vibration imaging in specific pulmonary diseases.

Breath sound distribution images of patients with pneumonia and pleural effusion.

OBJECTIVE: To determine whether breath sound distribution maps can differentiate between patients with pneumonia or pleural effusion versus healthy controls. METHODS: We recorded breath sounds from 20 patients conventionally diagnosed as having pleural effusion, 20 patients conventionally diagnosed as having pneumonia, and 60 healthy controls, of whom 20 served as a learning sample. All subjects were examined with a computer-based multi-sensor breath sound mapping device that records, analyzes, and displays a dynamic map of breath sound distribution. The physicians who interpreted the breath sound images were first trained in identifying common characteristics of the images from the learning sample of normals. Then the images from the 40 patients and the 40 controls were interpreted as either normal or abnormal. RESULTS: In the normal images, the left and right lung images developed synchronously and had similar size, shape, and intensity. The sensitivity and specificity of blinded differentiation between normal and abnormal images when the physician interpreter did not know the patient's workup were 82.5% and 80%, respectively. The sensitivity and specificity of blinded detection of normal and abnormal images when the interpreter did know the patient' workup were 90% and 88%, respectively. CONCLUSIONS: Computerized dynamic imaging of breath sounds is a sensitive and specific tool for distinguishing pneumonia or pleural effusion from normal lungs. The role of computerized breath sound analysis for diagnosis and monitoring of lung diseases needs further evaluation.