A spiky pattern in the course of electrical thoracic impedance as a very early sign of a developing pneumothorax

Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.

Bedside electrical impedance tomography in early diagnosis of pneumothorax in mechanically ventilated ICU patients — a single-center retrospective cohort study

PURPOSE

This study aimed to evaluate the routine use of electrical impedance tomography (EIT) to diagnose pneumothorax (PTX) in mechanically ventilated patients in the intensive care unit (ICU).

METHODS

A retrospective cohort study was conducted including mechanically ventilated supine patients who received EIT examinations. The EIT-based tidal variation was divided into ventral and dorsal regions of interest (ROIs): upper right (UR, ROI1), upper left (UL, ROI2) lower right (LR, ROI3), and lower left (LL, ROI4), and the ventilation defect score (DS) was calculated in each quadrant. Furthermore, horizontal ventral ventilation index (HVVI) was defined as ROI1_% / ROI2_% in the two ventral quadrants if ROI1_% > ROI2_%, otherwise HVVI = ROI2_% / ROI1_%.

RESULTS

A total of 203 patients were included, 25 of them with confirmed PTX. In the PTX patients, preceding cardiac surgery was the most common cause of PTX. Compared with the patients without PTX, the PTX patients had a higher DS in the ventral quadrants [median and interquartile range (IQR): 1.00 (0.00, 2.00) vs. 0.00 (0.00, 0.00), P < 0.001] respectively, but similar in the dorsal quadrants [median and IQR: 1.00 (0.00, 1.00) vs. 0.00 (0.00, 1.00), P = 0.722]. Moreover, a higher HVVI was found in the PTX group [median and IQR: 2.51 (1.58, 3.52) vs. 1.36 (1.15, 1.77), P < 0.001]. The area under the receiver operating characteristic curve of the HVVI to differentiate PTX from non-PTX was 0.88, with a sensitivity of 70% and a specificity of 90% when the cut-off value was 2.57.

CONCLUSION

The ventilation defect in the ventral regions and a high HVVI on EIT were observed in mechanically ventilated patients with PTX, which should trigger further diagnostics to confirm it.

A low-cost, portable, two-dimensional bioimpedance distribution estimation system based on the AD5933 impedance converter

This study proposes a low-cost, portable, eight-channel electrical impedance tomograph based on the AD5933 impedance converter. The patterns for current injection and voltage measurement are managed by an Arduino Mega 2560 board and four 74HC4067 Texas Instruments multiplexers. Regarding the experimental results, the errors in the impedance estimates of an electrical circuit that represents a Cole model were less than 1.14% for the magnitude and 4.15% for the phase. Furthermore, the signal-to-noise ratio measured in a resistive phantom was 55.23 dB. Additional experiments consisted of placing five spheres of different size and conductivity in a saline tank, measuring their impedance through eight electrodes, and then generating impedance maps using the Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS). These maps were different for each sphere, suggesting the proposed prototype as a promising alternative for medical applications.

Detection of posttraumatic pneumothorax using electrical impedance tomography-An observer-blinded study in pigs with blunt chest trauma

INTRODUCTION

Posttraumatic pneumothorax (PTX) is often overseen in anteroposterior chest X-ray. Chest sonography and Electrical Impedance Tomography (EIT) can both be used at the bedside and may provide complementary information. We evaluated the performance of EIT for diagnosing posttraumatic PTX in a pig model.

METHODS

This study used images from an existing database of images acquired from 17 mechanically ventilated pigs, which had sustained standardized blunt chest trauma and had undergone repeated thoracic CT and EIT. 100 corresponding EIT/CT datasets were randomly chosen from the database and anonymized. Two independent and blinded observers analyzed the EIT data for presence and location of PTX. Analysis of the corresponding CTs by a radiologist served as reference.

RESULTS

87/100 cases had at least one PTX detected by CT. Fourty-two cases showed a PTX > 20% of the sternovertebral diameter (PTXtrans20), whereas 52/100 PTX showed a PTX>3 cm in the craniocaudal diameter (PTXcc3), with 20 cases showing both a PTXtranscc and a PTXcc3. We found a very low agreement between both EIT observers considering the classification overall PTX/noPTX (κ = 0.09, p = 0.183). For PTXtrans20, sensitivity was 59% for observer 1 and 17% for observer 2, with a specificity of 48% and 50%, respectively. For PTXcc3, observer 1 showed a sensitivity of 60% with a specificity of 51% while the sensitivity of observer 2 was 17%, with a specificity of 89%. By programming a semi-automatized detection algorithm, we significantly improved the detection rate of PTXcc3, with a sensitivity of 73% and a specificity of 70%. However, detection of PTXtranscc was not improved.

CONCLUSION

In our analysis, visual interpretation of EIT without specific image processing or comparison with baseline data did not allow clinically useful diagnosis of posttraumatic PTX. Multimodal imaging approaches, technical improvements and image postprocessing algorithms might improve the performance of EIT for diagnosing PTX in the future.