Non-Invasive RF Technique for Detecting Different Stages of Alzheimer's Disease and Imaging Beta-Amyloid Plaques and Tau Tangles in the Brain

• Published in: IEEE transactions on medical imaging Vol. 39; no. 12; pp. 4060 - 4070
• Main Authors: Saied, Imran, Arslan, Tughrul, Chandran, Siddharthan, Smith, Colin, Spires-Jones, Tara, Pal, Suvankar
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Amyloid beta-Peptides - metabolism; Brain; Brain - diagnostic imaging; Brain - metabolism; Brain modeling; Computational modeling; Computational neuroscience; Dielectric measurement; dielectric measurements; Dielectric properties; Dielectrics; Electrical properties; Frequency ranges; Humans; Image reconstruction; Medical diagnosis; Medical imaging; microwave imaging; Network analysers; Neurodegenerative diseases; Neuroimaging; Pathology; Permittivity; Plaque, Amyloid - diagnostic imaging; Radio frequency; Reflectance; Senile plaques; Sensors; Substantia alba; Substantia grisea; Tau protein; tau Proteins; Tissues; β-Amyloid
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2020.3011359

This paper describes a novel approach of detecting different stages of Alzheimer's disease (AD) and imaging beta-amyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD.