Jonas Villadsen1, Hanne D Hansen1, Louise M Jørgensen2, Sune H Keller3, Flemming L Andersen3, Ida N Petersen4, Gitte M Knudsen2, Claus Svarer5. 1. Center for Integrated Molecular Brain Imaging (Cimbi) and Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark. 2. Center for Integrated Molecular Brain Imaging (Cimbi) and Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 3. Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark. 4. Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 5. Center for Integrated Molecular Brain Imaging (Cimbi) and Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark. Electronic address: claus.svarer@nru.dk.
Abstract
BACKGROUND: The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. COMPARISON WITH EXISTING METHODS: Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. NEW METHOD: A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. RESULTS: MRI and [11C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [11C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. CONCLUSION: We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration.
BACKGROUND: The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. COMPARISON WITH EXISTING METHODS: Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. NEW METHOD: A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. RESULTS: MRI and [11C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [11C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. CONCLUSION: We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration.
Authors: Aage Ko Alstrup; Pia Mt Afzelius; Svend B Jensen; Páll S Leifsson; Karen M Wegener; Ole L Nielsen Journal: J Am Assoc Lab Anim Sci Date: 2019-12-17 Impact factor: 1.232
Authors: Vicki J Swier; Katherine A White; Tyler B Johnson; Jessica C Sieren; Hans J Johnson; Kevin Knoernschild; Xiaojun Wang; Frank A Rohret; Christopher S Rogers; David A Pearce; Jon J Brudvig; Jill M Weimer Journal: Neurotherapeutics Date: 2022-09-13 Impact factor: 6.088
Authors: Stephano J Chang; Andrea J Santamaria; Francisco J Sanchez; Luz M Villamil; Pedro Pinheiro Saraiva; Jose Rodriguez; Yohjans Nunez-Gomez; Ioan Opris; Juan P Solano; James D Guest; Brian R Noga Journal: Front Neuroanat Date: 2020-11-13 Impact factor: 3.856
Authors: Olav M Andersen; Nikolaj Bøgh; Anne M Landau; Gro G Pløen; Anne Mette G Jensen; Giulia Monti; Benedicte P Ulhøi; Jens R Nyengaard; Kirsten R Jacobsen; Margarita M Jørgensen; Ida E Holm; Marianne L Kristensen; Aage Kristian O Alstrup; Esben S S Hansen; Charlotte E Teunissen; Laura Breidenbach; Mathias Droescher; Ying Liu; Hanne S Pedersen; Henrik Callesen; Yonglun Luo; Lars Bolund; David J Brooks; Christoffer Laustsen; Scott A Small; Lars F Mikkelsen; Charlotte B Sørensen Journal: Cell Rep Med Date: 2022-09-12
Authors: Cornelius K Donat; Henrik H Hansen; Hanne D Hansen; Ronnie C Mease; Andrew G Horti; Martin G Pomper; Elina T L'Estrade; Matthias M Herth; Dan Peters; Gitte M Knudsen; Jens D Mikkelsen Journal: Molecules Date: 2020-03-20 Impact factor: 4.411