MEGMRI

Hybrid MEG-MRI Imaging System

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 Obiettivo del progetto (Objective)

'We will develop and validate hybrid magnetoencephalography (MEG) and magnetic resonance imaging (MRI) technology that will allow simultaneous structural (MRI) and functional (MEG) imaging of the human brain. MEG is a non-invasive 3D functional imaging with a high temporal resolution compared to other functional imaging but often suffers from a precise structural localization which will be solved by the dual modality approach of the MEGMRI hybrid scanner. In parallel, low field MRI, a new very promising alternative to conventional high field MRI will provide enhanced image contrast in certain applications, improved geometric accuracy, improve safety (for patient with pacemakers and other implants, for pregnant women, for infants), and reduce costs. These new opportunities are based on recent advances in ultra-sensitive magnetic sensors. Superconducting magnetometers based on quantum interference devices (SQUIDs) have been recently used to provide 2D-MRI images at very low fields by two US teams. In parallel, a new type of magnetometer, called mixed sensor, based on spin electronics devices, has been developed within our consortium and used for low-field NMR. The first part of the project will be focused on sensor optimization, and low-field MRI development. This covers the development of field-tolerant SQUIDs and optimized mixed sensors and 3D-MRI low-field hardware and software development. The second part of the project will be devoted to a prototype building with the best kind of sensor and extensive preclinical validation, covering major brain disorders for adults and children. The consortium of MEGMRI has the necessary skills to perform all the tasks: sensor developers, MRI experts, MEG developers and clinical validators. It contains 13 partners from 5 countries including 3 SMEs and one large medical device manufacturer.'

Introduzione (Teaser)

By combining functional and structural imaging technologies, European scientists wished to improve the sensitivity and accuracy of brain imaging. Apart from neuroscience, the resultant hybrid scanner could be used in surgery and cancer diagnosis.

Descrizione progetto (Article)

Magnetoencephalography (MEG) is a three-dimensional (3D) imaging technique that measures the magnetic fields generated by the neuronal signal transmission in the brain. This method can, therefore, map brain activity and generate functional images that could be utilised to study the cognitive and perceptual brain processes.

However, MEG does not offer any structural information, unlike magnetic resonance imaging (MRI) which provides visualisation of soft structures in the body, such as brain and muscle. Ultra-low field (ULF) MRI is a cost-effective alternative to traditional MRI, providing enhanced contrast and improved geometric accuracy of body tissues. Additionally, the low-intensity magnetic field makes the procedure suitable for pregnant women, children and patients with pacemakers.

The EU-funded 'Hybrid MEG-MRI imaging system' (MEGMRI) project wished to combine MEG and MRI technologies to develop a hybrid imaging scanner. This development would allow simultaneous structural and functional imaging of the human brain.

As a first step, the consortium set out to determine the most optimal sensor type for the hybrid scanner. To this end, partners optimised three different sensor types, low temperature and high temperature superconducting quantum interference devices (SQUIDs) as well as mixed sensors based on giant magnetoresistance (GMR). Three systems were produced, each with different sensors, geometry, coil system and electronics.

MEGMRI's final prototype scanner used an array of 72 sensors, significantly improving its performance compared with previously reported devices.

Applications for the project's hybrid scanner could include diagnostics prior to neurosurgery such as resection of tumors or epileptogenic cortex in patients with pharmacoresistant epilepsy. Providing enhanced functional and anatomical images would reduce the need for intraoperative recordings in the future.

Ultra-low-field MRI could also be exploited for cancer diagnosis. Improved anatomical accuracy in combined MEG and MRI studies may improve our understanding of the link between neuronal activity and behavioural performance.