Assessing Early Brain Development in Neonates by Segmentation of High-Resolution 3T MRI

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, USAgerig@cs.unc.edu / http://www.cs.unc.edu/~gerig

SUMMARY

METHODS

• 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.

CONCLUSIONS

RESULTS

T1-only segmentation

Building of Atlas Template

MICCAI Nov. 2003

• 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).

• 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

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Brain Tissue Volume Neonates

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FSE T2w1x1x3 mm3

FSE PDw1x1x3 mm3

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Template MRI white matter csfgray matter

Tissue Probability Maps

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earlymyelinatedcorticospinaltract

hyper-intensemotor cortex

NeonateAdult

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)

• Challenge: Very low CNR, heterogeneous tissue, early myelination regions, reverse contrast wm/gm.

• Standard brain tissue segmentation fails.

Preliminary Results UNC Neonate Study

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PD/T2 segmentation

High-resolution PD/T2 data courtesy of Petra Hueppi, Univ. of Geneva.

Supported by NIH Conte Center MH064065, Neurodevelopmental Disorders Research Center HD 03110 and the Theodore and Vada Stanley Foundation

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).

• 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