# Analysis and Tracking of Intra-Needle Ultrasound Pleural Signals for Improved Anesthetic Procedures in the Thoracic Region

## TL;DR

This study improves thoracic anesthetic procedures by analyzing ultrasound signals from inside the needle to better identify the pleura during breathing.

## Contribution

A novel automated tool using time-frequency analysis to identify pleural signals during thoracic procedures.

## Key findings

- The pleural signal shows flickering, speckle-like patterns, and multi-band spectra due to reflections from the sliding visceral pleura and alveoli.
- Time-frequency analysis can distinguish the pleural signal from other tissues during the breathing cycle.
- Automated identification of the pleura can improve safety in thoracic regional anesthesia.

## Abstract

Background: Ultrasonography is commonly employed during thoracic regional anesthesia; however, its accuracy can be affected by factors such as obesity and poor penetration through the rib window. Needle-sized ultrasound transducers, known as intra-needle ultrasound (INUS) transducers, have been developed to detect the pleura and fascia using a one-dimensional radio frequency mode ultrasound signal. In this study, we aimed to use time-frequency analysis to characterize the pleural signal and develop an automated tool to identify the pleura during medical procedures. Methods: We developed an INUS system and investigated the pleural signal it measured by establishing a phantom study, and an in vivo animal study. Signals from the pleura, endothoracic fascia, and intercostal muscles were analyzed. Additionally, we conducted time- and frequency-domain analyses of the pleural and alveolar signals. Results: We identified the unique characteristics of the pleura, including a flickering phenomenon, speckle-like patterns, and highly variable multi-band spectra in the ultrasound signal during the breathing cycle. These characteristics are likely due to the multiple reflections from the sliding visceral pleura and alveoli. This automated identification of the pleura can enhance the safety for thoracic regional anesthesia, particularly in difficult cases. Conclusions: The unique flickering pleural signal based on INUS can be processed by time-frequency domain analysis and further tracked by an auto-identification algorithm. This technique has potential applications in thoracic regional anesthesia and other interventions. However, further studies are required to validate this hypothesis. Key Points Summary: Question: How can the ultrasound pleural signal be distinguished from other tissues during breathing? Findings: The frequency domain analysis of the pleural ultrasound signal showed fast variant and multi-band characteristics. We suggest this is due to ultrasound distortion caused by the interface of multiple moving alveoli. The multiple ultrasonic reflections from the sliding pleura and alveoli returned in variable and multi-banded frequency. Meaning: The distinguished pleural signal can be used for the auto-identification of the pleura for further clinical respiration monitoring and safety during regional anesthesia. Glossary of Terms: intra-needle ultrasound (INUS); radio frequency (RF); short-time Fourier transform (STFT); intercostal nerve block (ICNB); paravertebral block (PVB); pulse repetition frequency (PRF).