Abstract
Purpose
Fast Field-Cycling (FFC) MRI is a novel technology that allows varying the main magnetic field B0 during the pulse sequence, from the nominal field (usually hundreds of millitesla) down to Earth's field or below. This technique uses resistive magnets powered by fast amplifiers. One of the challenges with this method is to stabilise the magnetic field during the acquisition of the NMR signal. Indeed, a typical consequence of field instability is small, random phase variations between each line of k-space resulting in artefacts, similar to those which occur due to homogeneous motion but harder to correct as no assumption can be made about the phase error, which appears completely random. Here we propose an algorithm that can correct for the random phase variations induced by field instabilities without prior knowledge about the phase error.
Methods
The algorithm exploits the fact that ghosts caused by field instability manifest in image regions which should be signal free. The algorithm minimises the signal in the background by finding an optimum phase correction for each line of k-space and repeats the operation until the result converges, leaving the background free of signal.
Conclusion
We showed the conditions for which the algorithm is robust and successfully applied it on images acquired on FFC-MRI scanners. The same algorithm can be used for various applications other than Fast Field-Cycling MRI.
Fast Field-Cycling (FFC) MRI is a novel technology that allows varying the main magnetic field B0 during the pulse sequence, from the nominal field (usually hundreds of millitesla) down to Earth's field or below. This technique uses resistive magnets powered by fast amplifiers. One of the challenges with this method is to stabilise the magnetic field during the acquisition of the NMR signal. Indeed, a typical consequence of field instability is small, random phase variations between each line of k-space resulting in artefacts, similar to those which occur due to homogeneous motion but harder to correct as no assumption can be made about the phase error, which appears completely random. Here we propose an algorithm that can correct for the random phase variations induced by field instabilities without prior knowledge about the phase error.
Methods
The algorithm exploits the fact that ghosts caused by field instability manifest in image regions which should be signal free. The algorithm minimises the signal in the background by finding an optimum phase correction for each line of k-space and repeats the operation until the result converges, leaving the background free of signal.
Conclusion
We showed the conditions for which the algorithm is robust and successfully applied it on images acquired on FFC-MRI scanners. The same algorithm can be used for various applications other than Fast Field-Cycling MRI.
Original language | English |
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Pages (from-to) | 55-59 |
Number of pages | 5 |
Journal | Magnetic Resonance Imaging |
Volume | 44 |
Early online date | 24 Jul 2017 |
DOIs | |
Publication status | Published - Dec 2017 |
Bibliographical note
Grant support: This work was supported by EPSRC [grant numbers EP/E036775/1, EP/K020293/1] and received funding from the European Union's Horizon 2020 research and innovation programme [grant agreement No 668119, project “IDentIFY”]Keywords
- Fast field-cycling MRI
- Phase encode artefact
- Correction algorithm
- Post-processing
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Lionel Broche, M
- School of Medicine, Medical Sciences & Nutrition, Medical Sciences - The Hall Family Lecturer in Medical Physics
- School of Medicine, Medical Sciences & Nutrition, MRC/Versus Arthritis Centre for Musculoskeletal Health and Work
- School of Medicine, Medical Sciences & Nutrition, Medical Imaging Technologies
- School of Medicine, Medical Sciences & Nutrition, Institute of Medical Sciences
- School of Medicine, Medical Sciences & Nutrition, Aberdeen Biomedical Imaging Centre
- School of Medicine, Medical Sciences & Nutrition, Aberdeen Centre for Arthritis and Musculoskeletal Health (ACAMH)
Person: Academic
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David Lurie
- School of Medicine, Medical Sciences & Nutrition, Medical Sciences - Emeritus Professor
Person: Honorary