Author:

Keywords:

voxel-based morphometry, magnetic-resonance images, extremely preterm infants, automated segmentation, spatial normalization, mutual information, pet images, registration, childhood, model, PSI_MIC, Science & Technology, Life Sciences & Biomedicine, Neurosciences, Neuroimaging, Radiology, Nuclear Medicine & Medical Imaging, Neurosciences & Neurology, VOXEL-BASED MORPHOMETRY, MAGNETIC-RESONANCE IMAGES, EXTREMELY PRETERM INFANTS, AUTOMATED SEGMENTATION, SPATIAL NORMALIZATION, MUTUAL INFORMATION, PET IMAGES, REGISTRATION, CHILDHOOD, MODEL, Adult, Brain, Child, Child, Preschool, Female, Humans, Infant, Newborn, Leukomalacia, Periventricular, Magnetic Resonance Imaging, Male, 11 Medical and Health Sciences, 17 Psychology and Cognitive Sciences, Neurology & Neurosurgery, 32 Biomedical and clinical sciences, 42 Health sciences

Abstract:

The feasibility of linear normalization of child brain images with structural abnormalities due to periventricular leukomalacia (PVL) was assessed in terms of success rate and accuracy of the normalization algorithm. Ten T1-weighted brain images from healthy adult subject and 51 from children (411 years of age) were linearly transformed to achieve spatial registration with the standard MNI brain template. Twelve of the child brain images were radiologically normal, 22 showed PVL and 17 showed PVL with additional enlargement of the lateral ventricles. The effects of simple modifications to the normalization process were evaluated: changing the initial orientation and zoom parameters, masking non-brain areas, smoothing the images and using a pediatric template instead of the MNI template. Normalization failure was reduced by changing the initial zoom parameters and by removing background noise. The overall performance of the normalization algorithm was only improved when background noise was removed from the images. The results show that linear normatization of PVL affected brain images is feasible. (c) 2007 Elsevier Inc. All rights reserved.