Henri J M M Mutsaerts1,2, Jan Petr3, David L Thomas4, Enrico De Vita4, David M Cash4, Matthias J P van Osch5, Xavier Golay4, Paul F C Groot2, Sebastien Ourselin6, John van Swieten7, Robert Laforce8, Fabrizio Tagliavini9, Barbara Borroni10, Daniela Galimberti11, James B Rowe12, Caroline Graff13, Francesca B Pizzini14, Elizabeth Finger15, Sandro Sorbi16, Miguel Castelo Branco17,18, Jonathan D Rohrer4, Mario Masellis1,19,20,21, Bradley J MacIntosh1. 1. Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada. 2. Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands. 3. PET Center, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany. 4. Institute of Neurology, University College London, London, United Kingdom. 5. C.J. Gorter Center for High Field MRI, Dept. of Radiology, Leiden University Medical Center, Leiden, the Netherlands. 6. Translational Imaging Group, Centre for Medical Image Computing, University College London. 7. Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands. 8. Clinique Interdisciplinaire de Mémoire (CIME), CHU de Québec, Département des Sciences Neurologiques, Université Laval, Québec, Canada. 9. Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy. 10. Department of Medical and Experimental Sciences, University of Brescia, Brescia, Italy. 11. University of Milan, Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy. 12. Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom. 13. Department of Geriatric Medicine, Karolinska Institutet, Stockholm, Sweden. 14. Neuroradiology, Department of Diagnostics and Pathology, Verona University Hospital, Italy. 15. Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada. 16. Fondazione Don Carlo Gnocchi, Scientific Institute, Florence, Italy. 17. Neurology Department, Faculty of Medicine of Lisbon, Portugal. 18. Institute for Nuclear Sciences Applied to Health, Brain Imaging Network of Portugal, Coimbra, Portugal. 19. Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada. 20. Cognitive & Movement Disorders Clinic, Sunnybrook Health Sciences Centre, Toronto, Canada. 21. Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto.
Abstract
PURPOSE: To compare registration strategies to align arterial spin labeling (ASL) with 3D T1-weighted (T1w) images, with the goal of reducing the between-subject variability of cerebral blood flow (CBF) images. MATERIALS AND METHODS: Multi-center 3T ASL data were collected at eight sites with four different sequences in the multi-center GENetic Frontotemporal dementia Initiative (GENFI) study. In a total of 48 healthy controls, we compared the following image registration options: (I) which images to use for registration (perfusion-weighted images [PWI] to the segmented gray matter (GM) probability map (pGM) (CBF-pGM) or M0 to T1w (M0-T1w); (II) which transformation to use (rigid-body or non-rigid); and (III) whether to mask or not (no masking, M0-based FMRIB software library Brain Extraction Tool [BET] masking). In addition to visual comparison, we quantified image similarity using the Pearson correlation coefficient (CC), and used the Mann-Whitney U rank sum test. RESULTS: CBF-pGM outperformed M0-T1w (CC improvement 47.2% ± 22.0%; P < 0.001), and the non-rigid transformation outperformed rigid-body (20.6% ± 5.3%; P < 0.001). Masking only improved the M0-T1w rigid-body registration (14.5% ± 15.5%; P = 0.007). CONCLUSION: The choice of image registration strategy impacts ASL group analyses. The non-rigid transformation is promising but requires validation. CBF-pGM rigid-body registration without masking can be used as a default strategy. In patients with expansive perfusion deficits, M0-T1w may outperform CBF-pGM in sequences with high effective spatial resolution. BET-masking only improves M0-T1w registration when the M0 image has sufficient contrast. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:131-140.
PURPOSE: To compare registration strategies to align arterial spin labeling (ASL) with 3D T1-weighted (T1w) images, with the goal of reducing the between-subject variability of cerebral blood flow (CBF) images. MATERIALS AND METHODS: Multi-center 3T ASL data were collected at eight sites with four different sequences in the multi-center GENetic Frontotemporal dementia Initiative (GENFI) study. In a total of 48 healthy controls, we compared the following image registration options: (I) which images to use for registration (perfusion-weighted images [PWI] to the segmented gray matter (GM) probability map (pGM) (CBF-pGM) or M0 to T1w (M0-T1w); (II) which transformation to use (rigid-body or non-rigid); and (III) whether to mask or not (no masking, M0-based FMRIB software library Brain Extraction Tool [BET] masking). In addition to visual comparison, we quantified image similarity using the Pearson correlation coefficient (CC), and used the Mann-Whitney U rank sum test. RESULTS: CBF-pGM outperformed M0-T1w (CC improvement 47.2% ± 22.0%; P < 0.001), and the non-rigid transformation outperformed rigid-body (20.6% ± 5.3%; P < 0.001). Masking only improved the M0-T1w rigid-body registration (14.5% ± 15.5%; P = 0.007). CONCLUSION: The choice of image registration strategy impacts ASL group analyses. The non-rigid transformation is promising but requires validation. CBF-pGM rigid-body registration without masking can be used as a default strategy. In patients with expansive perfusion deficits, M0-T1w may outperform CBF-pGM in sequences with high effective spatial resolution. BET-masking only improves M0-T1w registration when the M0 image has sufficient contrast. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:131-140.
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