Misrepresentation of surface rendering of pediatric brain malformations performed following spatial normalization

Purpose: To evaluate the effects of spatial normalization on volume rendering in cases of pediatric brain malformation. Material and Methods: Three-dimensional (3D) T1-weighted volume datasets were acquired in three children, one with pachygyria, one with a Dandy-Walker malformation associated with polymicrogyria, and one with dysgenesis of the corpus callosum. On the non-normalized datasets, the skull margins were cropped and the remainder stripped with the brain extraction technique (BET). The data were also normalized into standard anatomic reference space using pediatric templates prior to the BET script. The surface constructions obtained by both techniques were then compared for geometric distortions. Results: Normalization of 3D datasets resulted in significant distortions in the shape of the brain, with increased anterior–posterior dimensions and narrower transverse diameter in all three cases. In two cases, there were alterations in the appearance of the gyri and sulci, leading to a potential misinterpretation of the volume-rendered surface when the gyri and sulci were in fact normal. Conclusion: In pediatric brain, particularly those with congenital brain anomalies, normalization as a post-processing step should be avoided as this may lead to misrepresentation of brain morphometry.

[1]  T. Park,et al.  MR imaging surface display of the cerebral cortex in children , 1997, Pediatric Radiology.

[2]  B. J. Casey,et al.  Structural and functional brain development and its relation to cognitive development , 2000, Biological Psychology.

[3]  J. Rapoport,et al.  Variability of human brain structure size: ages 4–20 years , 1997, Psychiatry Research: Neuroimaging.

[4]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[5]  Marko Wilke,et al.  Assessment of spatial normalization of whole‐brain magnetic resonance images in children , 2002, Human brain mapping.

[6]  Dominik S. Meier,et al.  Time-series analysis of MRI intensity patterns in multiple sclerosis , 2003, NeuroImage.

[7]  Alan C. Evans,et al.  Brain development during childhood and adolescence: a longitudinal MRI study , 1999, Nature Neuroscience.

[8]  James D Christensen,et al.  Normalization of brain magnetic resonance images using histogram even-order derivative analysis. , 2003, Magnetic resonance imaging.

[9]  Paul M. Thompson,et al.  The role of image registration in brain mapping , 2001, Image Vis. Comput..

[10]  M Wilke,et al.  Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data , 2003, Magnetic resonance in medicine.

[11]  Jagath C. Rajapakse,et al.  Quantitative Magnetic Resonance Imaging of Human Brain Development: Ages 4–18 , 1996 .

[12]  Ying Wu,et al.  Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI , 2006, NeuroImage.

[13]  D. Louis Collins,et al.  Tuning and Comparing Spatial Normalization Methods , 2003, MICCAI.

[14]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[15]  C. Rorden,et al.  Stereotaxic display of brain lesions. , 2000, Behavioural neurology.

[16]  O. Muzik,et al.  Statistical Parametric Mapping: Assessment of Application in Children , 2000, NeuroImage.

[17]  A Schulze-Bonhage,et al.  Automated detection of gray matter malformations using optimized voxel-based morphometry: a systematic approach , 2003, NeuroImage.