Ultrasound Measurement of Pleural Line Sliding as a Surrogate Indicator of Lung Volume Change: A Comparative Study With Ventilator Volume Data

The pleural line is a commonly observed structure in clinical ultrasound, used to evaluate pulmonary pathophysiology such as pulmonary edema, pneumothorax, or pneumonia. During the respiratory cycle, the pleural line undergoes displacement. In the inspiratory phase, the tidal volume increases, leading to an increase in displacement, while in the expiratory phase, the tidal volume decreases, resulting in reduced displacement. Therefore, we hypothesize that this displacement could serve as an indicator of tidal volume changes during respiration. This study is a retrospective proof of concept study, reviewing cases from January to December 2012, involving 6 to 12 patients who were intubated and on mechanical ventilation with synchronized lung ultrasound recordings. The respiratory parameters and lung ultrasound recordings of the patients were anonymized after being downloaded for subsequent analysis. For lung ultrasound analysis, we selected ultrasound recordings of the right lower lung apex at the costophrenic angle along the right mid-axillary line. Using the open-source image processing tool ImageJ 1.4, the pleural line corresponding to the right lower lung apex was enhanced and its terminal coordinates extracted. During respiration, the right lower lung apex moves with the breathing cycle, and the pleural line exhibits corresponding motion. The terminal coordinates of the pleural line also shift during respiration, and a plot of this displacement against time is generated to create the pleural line sliding displacement curve. From the numerical data downloaded from the ventilator, we extracted the tidal volume curve against time during the patient's respiratory cycle. Based on the ventilator's time records, we selected the time segment that corresponded to the pleural line sliding displacement curve. The tidal volume curve and pleural line sliding displacement curve data for the same time period were extracted and synchronized using interpolation. Linear regres