Multi-frequency electrical impedance tomography and neuroimaging data in stroke patients

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Neurology

Background:

• Electrical Impedance Tomography (EIT) shows promise for differentiating stroke types non-invasively.
• Current limitations in applying EIT to human stroke data stem from a lack of quality Multi-Frequency EIT (MFEIT) datasets.
• Accurate stroke classification is crucial for timely and appropriate treatment.

Purpose of the Study:

• To address the scarcity of quality MFEIT data for stroke research.
• To establish a comprehensive MFEIT dataset from stroke patients and healthy controls.
• To facilitate the development of advanced imaging and classification methods for stroke.

Main Methods:

• Collected MFEIT data from 23 stroke patients and 10 healthy volunteers.
• Acquired data across 17 frequencies (5 Hz to 2 kHz) with 31 current injections per frequency.
• Ensured data quality through a clinical trial in collaboration with University College London Hospital.

Main Results:

• Generated the most comprehensive MFEIT dataset to date for stroke research.
• The dataset comprises 930 measurements per frequency for each participant.
• Enables integrated analysis of MFEIT, EEG, CT, and MRI data.

Conclusions:

• The created MFEIT dataset is a valuable resource for advancing stroke classification.
• This dataset will support the development of novel EIT-based diagnostic tools for stroke.
• Facilitates future research combining MFEIT with other neuroimaging modalities for improved stroke management.