title: Diffeomorphic brain mapping based on T1-weighted images: improvement of registration accuracy by multichannel mapping.
abstract: OBJECTIVE
  To improve image registration accuracy in neurodegenerative populations.
  
  
  METHODS
  This study used primary progressive aphasia, aged control, and young control T1-weighted images. Mapping to a template image was performed using single-channel Large Deformation Diffeomorphic Metric Mapping (LDDMM), a dual-channel method with ventricular anatomy in the second channel, and a dual-channel with appendage method, which utilized a priori knowledge of template ventricular anatomy in the deformable atlas.
  
  
  RESULTS
  Our results indicated substantial improvement in the registration accuracy over single-contrast-based brain mapping, mainly in the lateral ventricles and regions surrounding them. Dual-channel mapping significantly (P < 0.001) reduced the number of misclassified lateral ventricle voxels (based on a manually defined reference) over single-channel mapping. The dual-channel (w/appendage) method further reduced (P < 0.001) misclassification over the dual-channel method, indicating that the appendage provides more accurate anatomical correspondence for deformation.
  
  
  CONCLUSIONS
  Brain anatomical mapping by shape normalization is widely used for quantitative anatomical analysis. However, in many geriatric and neurodegenerative disorders, severe tissue atrophy poses a unique challenge for accurate mapping of voxels, especially around the lateral ventricles. In this study we demonstrate our ability to improve mapping accuracy by incorporating ventricular anatomy in LDDMM and by utilizing a priori knowledge of ventricular anatomy in the deformable atlas.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
authors.forename: Aigerim
authors.initials: A
authors.lastname: Djamanakova
authors.forename: Andreia V
authors.initials: AV
authors.lastname: Faria
authors.forename: John
authors.initials: J
authors.lastname: Hsu
authors.forename: Can
authors.initials: C
authors.lastname: Ceritoglu
authors.forename: Kenichi
authors.initials: K
authors.lastname: Oishi
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.forename: Argye E
authors.initials: AE
authors.lastname: Hillis
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D018888:Aphasia, Primary Progressive / Q000175:diagnosis / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008875:Middle Aged; D015233:Models, Statistical; D019636:Neurodegenerative Diseases / Q000175:diagnosis* / Q000473:pathology; D015203:Reproducibility of Results