Registration of challenging pre-clinical brain images

TY - JOUR

T1 - Registration of challenging pre-clinical brain images

AU - Crum, William R

AU - Modo, Michel

AU - Vernon, Anthony C

AU - Barker, Gareth J

AU - Williams, Steven C R

N1 - Copyright © 2013 Elsevier B.V. All rights reserved.

PY - 2013/4

Y1 - 2013/4

N2 - The size and complexity of brain imaging studies in pre-clinical populations are increasing, and automated image analysis pipelines are urgently required. Pre-clinical populations can be subjected to controlled interventions (e.g., targeted lesions), which significantly change the appearance of the brain obtained by imaging. Existing systems for registration (the systematic alignment of scans into a consistent anatomical coordinate system), which assume image similarity to a reference scan, may fail when applied to these images. However, affine registration is a particularly vital pre-processing step for subsequent image analysis; it is assumed to be an effective procedure in recent literature describing sophisticated techniques such as manifold learning. Therefore, in this paper, we present an affine registration solution that uses a graphical model of a population to decompose difficult pairwise registrations into a composition of clearly defined steps using members of the population. We developed this methodology in the context of a pre-clinical model of stroke in which large, variable hyper-intense lesions significantly impact registration. We tested this technique systematically in a simulated human population of brain tumour images before applying it to models of pre-clinical Parkinson's disease and stroke populations.

AB - The size and complexity of brain imaging studies in pre-clinical populations are increasing, and automated image analysis pipelines are urgently required. Pre-clinical populations can be subjected to controlled interventions (e.g., targeted lesions), which significantly change the appearance of the brain obtained by imaging. Existing systems for registration (the systematic alignment of scans into a consistent anatomical coordinate system), which assume image similarity to a reference scan, may fail when applied to these images. However, affine registration is a particularly vital pre-processing step for subsequent image analysis; it is assumed to be an effective procedure in recent literature describing sophisticated techniques such as manifold learning. Therefore, in this paper, we present an affine registration solution that uses a graphical model of a population to decompose difficult pairwise registrations into a composition of clearly defined steps using members of the population. We developed this methodology in the context of a pre-clinical model of stroke in which large, variable hyper-intense lesions significantly impact registration. We tested this technique systematically in a simulated human population of brain tumour images before applying it to models of pre-clinical Parkinson's disease and stroke populations.

KW - Parkinson's disease

KW - Stroke

KW - Magnetic resonance imaging

KW - Chain graph

KW - Image registration

KW - Acknowledged-BRC

KW - Acknowledged-BRC-13/14

U2 - 10.1016/j.jneumeth.2013.03.015

DO - 10.1016/j.jneumeth.2013.03.015

M3 - Article

C2 - 23558335

SN - 0165-0270

VL - 216

SP - 62

EP - 77

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

IS - 1

ER -