MRT sequences as the key to better imaging

Magnetic resonance imaging (MRI) is one of the most important imaging techniques in modern medicine. The quality and informative value of the images depend largely on what are known as MR sequences. This control software determines how high-frequency pulses are transmitted, magnetic fields are switched, and signals are read during the examination.

Researchers at the Fraunhofer Institute for Digital Medicine MEVIS in Bremen are working on bringing new MR sequences into clinical use more quickly. The basis for this is the gammaSTAR software platform and the SpinIt project. The aim is to develop innovative imaging technologies independently of manufacturers and to facilitate their approval as medical devices.

"An MR sequence precisely defines how the various components must be controlled over time to create the desired image contrast," explains Dr. Daniel Hoinkiss, Principal Scientist at Fraunhofer MEVIS. "This fine control must operate with extremely high accuracy and is therefore very challenging to develop."

New MR sequences can help to significantly reduce examination times in MRI or enable new image contrasts for more precise diagnoses. "One focus of current developments is on sequences that accelerate the scanning process and reduce examination times from half an hour to just a few minutes," says Hoinkiss.

A key problem in transferring new MR sequences into clinical practice is the complexity of the approval processes. Sequences are often programmed directly in the device manufacturers' proprietary development environments and approved together with the respective hardware.

"For specialized or novel approaches, this barrier is often difficult to overcome," says project manager Hoinkiss. "Our aim is to simplify and, in a sense, democratize the certification process, making it more accessible for third-party providers."

This is where the gammaSTAR platform comes in. It enables MR sequences to be developed, simulated, and reconstructed in a manufacturer-neutral manner. The sequences can then be executed on different MRI scanners via standardized interfaces.

"We designed the platform in such a way that MR sequences already meet a quality standard that fundamentally allows for subsequent medical device approval," explains Dr. Nora-Josefin Breutigam, responsible for quality and partner management at Fraunhofer MEVIS.

The SpinIt project is being implemented as part of the EXIST funding program of the Federal Ministry for Economic Affairs and Energy. The aim is to support start-ups and research groups in developing and certifying new MR sequences.

An important role in this is played by the collaboration with the medical technology company Siemens Healthineers, which provides a certified interface for external software solutions.

"This is pioneering work," emphasizes Breutigam. "To date, there has been no case in which a third-party MR sequence has been approved independently of the manufacturer."

In addition to the actual sequence, reconstruction algorithms for the image data must also be carefully tested. Only then can raw data from the scanner be transformed into medically interpretable images. "An MR sequence is always a package consisting of imaging and reconstruction," explains Dr. Simon Konstandin, who is responsible for technical development.

The new approach could also facilitate multicenter clinical trials. Until now, comparable MRI data could often only be obtained if identical scanners were used or measurement protocols were laboriously coordinated.

With manufacturer-independent sequences and identical reconstruction, different MRI systems could be used in the future without losing the comparability of the data.

After the 18-month EXIST funding phase has been completed, a decision will be made as to whether the SpinIt project will be spun off into a separate company. This could offer services related to the approval and quality assurance of MR sequences in the future, thereby supporting the transfer of new MRI technologies into clinical practice.

MEDICA-tradefair.com; Source: Fraunhofer Institute for Digital Medicine MEVIS