Low-Field scanner for physiotherapy and rheumatology.

In this project funded by the European Research Council (ERC), we will increase the performance of a recently developed low-field MRI scanner to enhance its clinical value. This will be achieved by combining new hardware with newly developed methods (such as OverSampled MRI, OS-MRI1) which, in combination with iterative reconstructions, field maps and deep learning techniques, can boost he image quality of such low-cost MRI systems.

In particular, OS-MRI has been designed to accelerate MRI acquisitions by combining data sampling well beyond the traditional Nyquist-Shannon limit, with Algebraic Reconstruction Techniques (ART) which take as prior information the exquisitely known interactions between electromagnetic fields and sample spins in MRI setups. OS-MRI brings along a further advantage, which is of special relevance in low-field contexts: this method features a high immunity to noise effects (Figure 3).

We will also upgrade the current LUMC design with a new open-source Magnetic Resonance Control System, MaRCoS1 (Figure 3), which is currently under development at LUMC and CSIC (coordinator of Histo-MRI FET-Open project from which this Innovation Launchpad originates). MaRCoS is an open-source, fully flexible control system based on Red Pitayas and home-made electronics. This will give us access to the raw sampled data, as required by OS-MRI. We will also develop a Python GUI to interface with the system and the ART reconstruction modules, display the data and reconstructions, and post-process the images.

Our ultimate goal is to create the structure required to deploy PR scanners in clinics worldwide. By the end of the proyect we will have deployed the commercial plan we have devised for our new low-field affordable scanner for physiotherapy and rheumatology clinics.

 

1 T. O’Reilly, W.M. Teeuwisse and A.G. Webb, ‘Three-Dimensional MRI in a Homogenous 27 Cm Diameter Bore Halbach Array Magnet’, Journal of Magnetic Resonance, 307 (2019), 106578; Thomas O’Reilly and others, ‘In Vivo 3D Brain and Extremity MRI at 50 MT Using a Permanent Magnet Halbach Array’, Magnetic Resonance in Medicine, 2020, mrm.28396; Bart De Vos and others, ‘Gradient Coil Design and Realization for a Halbach-Based MRI System’, IEEE Transactions on Magnetics, 56.3 (2020); Itamar Ronen and others, ‘Proton Nuclear Magnetic Resonance J-Spectroscopy of Phantoms Containing Brain Metabolites on a Portable 0.05 T MRI Scanner’, Journal of Magnetic Resonance, 320 (2020), 106834.

2. Fernando Galve and others, ‘Model-Driven Reconstruction for Highly-Oversampled MRI’, ArXiv Preprint, 2020; Joseba Alonso and others, ‘Method and Apparatus for MRI Using Fast Magic-Angle Rotation of Spatial Encoding Magnetic Fields’ (Spain, 2019), p. P201930015; Joseba Alonso and others, ‘Method and Apparatus for MRI Using Time-Varying Inhomogeneous Magnetic Fields’ (Spain, 2019), p. P201930719; F. Galve and others, ‘Magnetic Resonance Imaging Method with Zero Echo Time and Slice Selection’ (Spain, 2020), p. P202030504; F. Galve and others, ‘Magnetic Resonance Imaging with Prior Knowledge and Oversampling’ (Spain, 2020), p. EP20382540.1.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101034644.

 

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