A framework for using diffusion weighted imaging to improve cortical parcellation.
Cortical parcellation refers to anatomical labelling of every point in the cortex. An accurate parcellation is useful in many analysis techniques including the study of regional changes in cortical thickness or volume in ageing and neurodegeneration. Parcellation is also key to anatomic apportioning of functional imaging changes. We present preliminary work on a novel algorithm that takes an entire cortical parcellation and iteratively updates it to better match connectivity information derived from diffusion weighted imaging. We demonstrate the algorithm on a cohort of 17 healthy controls. Initial results show the algorithm recovering artificially induced mis-registrations of the parcellation and also converging to a group-wise average. This work introduces a framework to investigate the relationship between structure and function, with no a-priori knowledge of specific regions of interest.
Clarkson
Matthew J
MJ
Centre for Medical Image Computing, University College London, WC1E 6BT, UK.
Malone
Ian B
IB
Modat
Marc
M
Leung
Kelvin K
KK
Ryan
Natalie
N
Alexander
Daniel C
DC
Fox
Nick C
NC
Ourselin
Sébastien
S
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity