Marco Battiston1, Francesco Grussu1, Andrada Ianus2,3, Torben Schneider4, Ferran Prados1,5, James Fairney1,6, Sebastien Ourselin5, Daniel C Alexander2, Mara Cercignani7, Claudia A M Gandini Wheeler-Kingshott1,8,9, Rebecca S Samson1. 1. Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom. 2. Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom. 3. Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal. 4. Philips Healthcare, Guilford, Surrey, United Kingdom. 5. Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. 6. UCL Department of Medical Physics and Bioengineering, University College London, London, United Kingdom. 7. CISC, Department of Neuroscience, Brighton & Sussex Medical School, Brighton, Sussex, United Kingdom. 8. Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy. 9. Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy.
Abstract
PURPOSE: To develop a framework to fully characterize quantitative magnetization transfer indices in the human cervical cord in vivo within a clinically feasible time. METHODS: A dedicated spinal cord imaging protocol for quantitative magnetization transfer was developed using a reduced field-of-view approach with echo planar imaging (EPI) readout. Sequence parameters were optimized based in the Cramer-Rao-lower bound. Quantitative model parameters (i.e., bound pool fraction, free and bound pool transverse relaxation times [ T2F, T2B], and forward exchange rate [kFB ]) were estimated implementing a numerical model capable of dealing with the novelties of the sequence adopted. The framework was tested on five healthy subjects. RESULTS: Cramer-Rao-lower bound minimization produces optimal sampling schemes without requiring the establishment of a steady-state MT effect. The proposed framework allows quantitative voxel-wise estimation of model parameters at the resolution typically used for spinal cord imaging (i.e. 0.75 × 0.75 × 5 mm3 ), with a protocol duration of ∼35 min. Quantitative magnetization transfer parametric maps agree with literature values. Whole-cord mean values are: bound pool fraction = 0.11(±0.01), T2F = 46.5(±1.6) ms, T2B = 11.0(±0.2) µs, and kFB = 1.95(±0.06) Hz. Protocol optimization has a beneficial effect on reproducibility, especially for T2B and kFB . CONCLUSION: The framework developed enables robust characterization of spinal cord microstructure in vivo using qMT. Magn Reson Med 79:2576-2588, 2018.
PURPOSE: To develop a framework to fully characterize quantitative magnetization transfer indices in the human cervical cord in vivo within a clinically feasible time. METHODS: A dedicated spinal cord imaging protocol for quantitative magnetization transfer was developed using a reduced field-of-view approach with echo planar imaging (EPI) readout. Sequence parameters were optimized based in the Cramer-Rao-lower bound. Quantitative model parameters (i.e., bound pool fraction, free and bound pool transverse relaxation times [ T2F, T2B], and forward exchange rate [kFB ]) were estimated implementing a numerical model capable of dealing with the novelties of the sequence adopted. The framework was tested on five healthy subjects. RESULTS: Cramer-Rao-lower bound minimization produces optimal sampling schemes without requiring the establishment of a steady-state MT effect. The proposed framework allows quantitative voxel-wise estimation of model parameters at the resolution typically used for spinal cord imaging (i.e. 0.75 × 0.75 × 5 mm3 ), with a protocol duration of ∼35 min. Quantitative magnetization transfer parametric maps agree with literature values. Whole-cord mean values are: bound pool fraction = 0.11(±0.01), T2F = 46.5(±1.6) ms, T2B = 11.0(±0.2) µs, and kFB = 1.95(±0.06) Hz. Protocol optimization has a beneficial effect on reproducibility, especially for T2B and kFB . CONCLUSION: The framework developed enables robust characterization of spinal cord microstructure in vivo using qMT. Magn Reson Med 79:2576-2588, 2018.
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