Joint multi‐field T1 quantification for fast field‐cycling MRI
Open Access
- 10 June 2021
- journal article
- research article
- Published by Wiley in Magnetic Resonance in Medicine
- Vol. 86 (4), 2049-2063
- https://doi.org/10.1002/mrm.28857
Abstract
Purpose Recent developments in hardware design enable the use of fast field-cycling (FFC) techniques in MRI to exploit the different relaxation rates at very low field strength, achieving novel contrast. The method opens new avenues for in vivo characterizations of pathologies but at the expense of longer acquisition times. To mitigate this, we propose a model-based reconstruction method that fully exploits the high information redundancy offered by FFC methods. Methods The proposed model-based approach uses joint spatial information from all fields by means of a Frobenius - total generalized variation regularization. The algorithm was tested on brain stroke images, both simulated and acquired from FFC patients scans using an FFC spin echo sequences. The results are compared to three non-linear least squares fits with progressively increasing complexity. Results The proposed method shows excellent abilities to remove noise while maintaining sharp image features with large signal-to-noise ratio gains at low-field images, clearly outperforming the reference approach. Especially patient data show huge improvements in visual appearance over all fields. Conclusion The proposed reconstruction technique largely improves FFC image quality, further pushing this new technology toward clinical standards.Keywords
Funding Information
- Österreichischen Akademie der Wissenschaften (DOC Fellowship 24966)
This publication has 50 references indexed in Scilit:
- Model‐based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin‐echo MRIJournal of Magnetic Resonance Imaging, 2011
- The use of contrast agents with fast field-cycling magnetic resonance imagingPhysics in Medicine & Biology, 2010
- Wavelet domain non-linear filtering for MRI denoisingMagnetic Resonance Imaging, 2010
- Compressed sensing reconstruction for magnetic resonance parameter mappingMagnetic Resonance in Medicine, 2010
- Fast field-cycling magnetic resonance imagingComptes Rendus Physique, 2010
- Convergence rates for the iteratively regularized Gauss–Newton method in Banach spacesInverse Problems, 2010
- Delta relaxation enhanced MR: Improving activation‐specificity of molecular probes through R1 dispersion imagingMagnetic Resonance in Medicine, 2009
- Sparse MRI: The application of compressed sensing for rapid MR imagingMagnetic Resonance in Medicine, 2007
- Magnetic resonance imaging with T1 dispersion contrastMagnetic Resonance in Medicine, 2006
- Nonlinear total variation based noise removal algorithmsPhysica D: Nonlinear Phenomena, 1992