title: Deformation field correction for spatial normalization of PET images.
abstract: Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.
authors.affiliation: Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA. Electronic address: murat.bilgel@nih.gov.
authors.forename: Murat
authors.initials: M
authors.lastname: Bilgel
authors.affiliation: Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Electrical and Computer Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Computer Science, Johns Hopkins University School of Engineering, Baltimore, MD, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Carass
authors.affiliation: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Resnick
authors.affiliation: Dept. of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Dean F
authors.initials: DF
authors.lastname: Wong
authors.affiliation: Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Dept. of Electrical and Computer Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000814:Aniline Compounds; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging / Q000378:metabolism*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D013844:Thiazoles
chemical_list: C475519:2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole; D000814:Aniline Compounds; D013844:Thiazoles
keywords: Deformation field; PET registration; Pittsburgh compound B (PiB); Ridge regression