Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging.
This paper examines an alternative approach to separating magnetic resonance imaging (MRI) intensity inhomogeneity from underlying tissue-intensity structure using a direct template-based paradigm. This permits the explicit spatial modeling of subtle intensity variations present in normal anatomy which may confound common retrospective correction techniques using criteria derived from a global intensity model. A fine-scale entropy driven spatial normalisation procedure is employed to map intensity distorted MR images to a tissue reference template. This allows a direct estimation of the relative bias field between template and subject MR images, from the ratio of their low-pass filtered intensity values. A tissue template for an aging individual is constructed and used to correct distortion in a set of data acquired as part of a study on dementia. A careful validation based on manual segmentation and correction of nine datasets with a range of anatomies and distortion levels is carried out. This reveals a consistent improvement in the removal of global intensity variation in terms of the agreement with a global manual bias estimate, and in the reduction in the coefficient of intensity variation in manually delineated regions of white matter.
Studholme
C
C
Department of Radiology, University of California San Francisco, VAMC 114Q, Bldg. 9, Room 200 4150, Clement Street, San Francisco, CA 94121, USA. studholme@ieee.org
Cardenas
V
V
Song
E
E
Ezekiel
F
F
Maudsley
A
A
Weiner
M
M
D000375:Aging; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D003704:Dementia / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique