Accurate, robust, and automated longitudinal and cross-sectional brain change analysis.
Quantitative measurement of brain size, shape, and temporal change (for example, in order to estimate atrophy) is increasingly important in biomedical image analysis applications. New methods of structural analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method of longitudinal (temporal change) analysis, SIENA, was presented previously. In this paper, improvements to this method are described, and also an extension of SIENA to a new method for cross-sectional (single time point) analysis. The methods are fully automated, robust, and accurate: 0.15% brain volume change error (longitudinal): 0.5-1% brain volume accuracy for single-time point (cross-sectional). A particular advantage is the relative insensitivity to differences in scanning parameters. The methods provide easy manual review of their output by the automatic production of summary images which show the results of the brain extraction, registration, tissue segmentation, and final atrophy estimation.
Smith
Stephen M
SM
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, FMRIB, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, United Kingdom.
Zhang
Yongyue
Y
Jenkinson
Mark
M
Chen
Jacqueline
J
Matthews
P M
PM
Federico
Antonio
A
De Stefano
Nicola
N
D000465:Algorithms; D001284:Atrophy; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D003430:Cross-Sectional Studies; D004827:Epilepsy / Q000473:pathology / Q000601:surgery; D005625:Frontal Lobe / Q000033:anatomy & histology / Q000601:surgery; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D019635:Neurosurgical Procedures; D015203:Reproducibility of Results; D012886:Skull / Q000033:anatomy & histology; D013997:Time Factors