title: A Neonatal Bimodal MR-CT Head Template.
abstract: Neonatal MR templates are appropriate for brain structural analysis and spatial normalization. However, they do not provide the essential accurate details of cranial bones and fontanels-sutures. Distinctly, CT images provide the best contrast for bone definition and fontanels-sutures. In this paper, we present, for the first time, an approach to create a fully registered bimodal MR-CT head template for neonates with a gestational age of 39 to 42 weeks. Such a template is essential for structural and functional brain studies, which require precise geometry of the head including cranial bones and fontanels-sutures. Due to the special characteristics of the problem (which requires inter-subject inter-modality registration), a two-step intensity-based registration method is proposed to globally and locally align CT images with an available MR template. By applying groupwise registration, the new neonatal CT template is then created in full alignment with the MR template to build a bimodal MR-CT template. The mutual information value between the CT and the MR template is 1.17 which shows their perfect correspondence in the bimodal template. Moreover, the average mutual information value between normalized images and the CT template proposed in this study is 1.24±0.07. Comparing this value with the one reported in a previously published approach (0.63±0.07) demonstrates the better generalization properties of the new created template and the superiority of the proposed method for the creation of CT template in the standard space provided by MR neonatal head template. The neonatal bimodal MR-CT head template is freely downloadable from https://www.u-picardie.fr/labo/GRAMFC.
authors.affiliation: Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.|Inserm UMR 1105, Faculté de Médecine, Université de Picardie Jules Verne, Amiens, France.
authors.forename: Sona
authors.identifier: 0000-0001-8612-4757
authors.initials: S
authors.lastname: Ghadimi
authors.affiliation: Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
authors.forename: Mehrana
authors.initials: M
authors.lastname: Mohtasebi
authors.affiliation: Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.|Inserm UMR 1105, Faculté de Médecine, Université de Picardie Jules Verne, Amiens, France.
authors.forename: Hamid
authors.initials: H
authors.lastname: Abrishami Moghaddam
authors.affiliation: Inserm UMR 1105, Faculté de Médecine, Université de Picardie Jules Verne, Amiens, France.
authors.forename: Reinhard
authors.initials: R
authors.lastname: Grebe
authors.affiliation: Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Masoumeh
authors.initials: M
authors.lastname: Gity
authors.affiliation: Inserm UMR 1105, Faculté de Médecine, Université de Picardie Jules Verne, Amiens, France.|Inserm UMR 1105, Centre Hospitalier Universitaire d'Amiens, Amiens, France.
authors.forename: Fabrice
authors.initials: F
authors.lastname: Wallois
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging* / Q000254:growth & development; D005865:Gestational Age; D006257:Head / Q000000981:diagnostic imaging / Q000254:growth & development; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging; D012886:Skull / Q000000981:diagnostic imaging* / Q000254:growth & development; D014057:Tomography, X-Ray Computed