Software and Operational Concept for EIT-Based regional lung Function monitoring

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique suitable for medical application. Most of the previous investigations have been done under laboratory conditions and only few projects under real hospital environment. This paper presents a novel concept of EIT application for clinical lung ventilation monitoring. A virtual EIT lung ventilation monitor suitable for application in clinical environment was designed and successfully implemented into the Goe-MF II EIT device developed in our lab. The software together with its operational concept is described here. The problems of device handling, extraction of physiological relevant information from measurements, and data storage and retrieval are addressed. During data acquisition, comprehensive status information is displayed: the contact of all electrodes to the patient's body, the correctness of the wiring of the electrodes, and properties of electric or magnetic fields that produce noise at electrodes or cables. To give a quick overview about the patients ' lung function, dynamic functional images are introduced for the first time with the essential property that their screen display only changes if the patient’s physiological status changes, but not during the breathing steady state condition

Dynamic functional EIT imaging (df-EIT) - a new concept for monitoring effects on regional lung function induced by respiratory therapy

EIT can potentially offer useful information for monitoring regional lung function directly at the bedside. However it is not evident which presentation of this information is most suitable for monitoring and how this can be efficiently achieved. Using a conventional EIT movie interpretation is inherently difficult due to the continuously changing image. We propose a novel monitoring method of dynamic functional EIT image generation. This method is a modification of methods used for offline generation of functional EIT images for quantification of different ventilatory conditions. In the current work it has been extended to work online on the last acquired images in such a way that the functional image is continuously updated during data acquisition. The advantage is that for steady state physiological condition the dynamic functional EIT image does not alter thus facilitating visual interpretation. Different dynamic functional EIT images acquired under laboratory and clinical conditions in healthy volunteers and mechanically ventilated patient with the Goe-MF II system will be presented and compared with the information obtained from a sequence of single EIT images