Assessing Early Brain Development in Neonates
by Segmentation of High-Resolution 3T MRI
1,2G
Gerig, 2M Prastawa, 3W Lin, 1John Gilmore
Departments of 1Psychiatry, 2Computer Science, 3Radiology
University of North Carolina, Chapel Hill,NC 27614, USA
gerig@cs.unc.edu / http://www.cs.unc.edu/~gerig
SUMMARY
RESULTS
• Research: Quantitative MRI to study
unsedated newborns at risk for
neurodevelopmental disorders.
• Clinical Study: 120 newborns
recruited at UNC, age at MRI about 2
weeks
• Motivation: Early detection of
abnormalities  Possibility for early
intervention and therapy.
• Imaging: High field (3T Siemens
Allegra), high resolution (T1 1mm3,
FSE 0.9x0.9x3mm3), high-speed
imaging (12’ for T1, FSE and DTI).
T1-only segmentation
gray
csf
myelin.
hyperintense
motor
cortex
Adult
Neonate
• Challenge: Very
low CNR,
heterogeneous
tissue, early
myelination
regions, reverse
contrast wm/gm.
• Standard brain
tissue
segmentation fails.
FSE PDw
1x1x3 mm3
METHODS
Approach:
• Atlas-moderated EM segmentation (cf.
Leemput and Warfield)
• Tissue intensity model for white matter
(non-myelinated and myelinated wm form
bimodal distribution) (cf. Cocosco,
Prastawa)
PD/T2 segmentation
High-resolution PD/T2 data courtesy of Petra Hueppi, Univ. of Geneva.
Preliminary Results UNC Neonate Study
400.00
wm-myel
300.00
csf
200.00
gm
100.00
n40
n33
n32
n31
0.00
wm-nonmyel
n26
Building of Atlas Template
500.00
n25
Int
600.00
n23
wm myl
n18
gm
n10
wm
Brain Tissue Volume Neonates
n0002
• So far: 20 normal neonates (10 males,
10 females)
• Age 16 ± 4 days
• Siemens 3T head-only scanner
• Neonates were fed prior to scanning,
swaddled, fitted with ear protection and
had their heads fixed in a vac-fix device
• A pulse oximeter was monitored by a
physician or research nurse
• Most neonates slept during the scan
• Motion-free scans in 13-15 infants
#
n0001
FSE T2w
1x1x3 mm3
early
myelinated
corticospinal
tract
volume (ml)
T1 3D MPRage
1x1x1 mm3
white
cases
CONCLUSIONS
• It is feasible to study brain development in unsedated newborns using 3T MRI
• Study will likely provide a vastly improved understanding of early brain
development and its relationship to neuropsychiatric disorders.
• Novelty: Tissue model for segmentation of myelinated/nonmyel. white matter.
Literature
• Gilmore JH, Gerig G, Specter B, Charles HC, Wilber JS, Hertzberg BS, Kliewer MA (2001a): Neonatal cerebral ventricle
volume: a comparison of 3D ultrasound and magnetic resonance imaging. Ultrasound Med and Biol 27:1143-1146.
• Huppi PS, Warfield S, Kikinis R, Barnes PD, Zientara GP, Jolesz FA, Tsuji MK, Volpe JJ (1998b): Quantitative magnetic
resonance imaging of brain development in premature and normal newborns. Ann Neurol 43: 224-235.
• Zhai G, Lin W, Wilber K, Gerig G, Gilmore JH (2003): Comparisons of regional white matter fractional anisotrophy in healthy
neonates and adults using a 3T head-only scanner. Radiology (in press).
Template MRI
white matter
gray matter
csf
• Warfield, S., Kaus, M., Jolesz, F., Kikinis, R.: Adaptive template moderated spa-tially varying statistical classification. In
Wells, W.M.e.a., ed.: Medical Image Computing and Computer-Assisted Intervention (MICCAI’98). Volume 1496 of LNCS.,
Springer 1998
• Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of
the brain. IEEE Transactions on Medical Imaging 18 (1999) 897–908
• Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: Automatic generation of training data for brain tissue classification from mri. In
Dohi, T., Kikinis, R., eds.: Medical Image Computing and Computer-Assisted Intervention MICCAI 2002. Volume 2488 of
LNCS., Springer Verlag (2002) 516–523
• Prastawa, M., Bullitt, E., Gerig, G., Robust Estimation for Brain Tumor Segmentation, MICCIA 2003, Nov. 2003
Here Text
Here Text
Tissue Probability Maps
Supported by NIH Conte Center MH064065, Neurodevelopmental Disorders
Research Center HD 03110 and the Theodore and Vada Stanley Foundation
MICCAI Nov. 2003