Translator Disclaimer
30 April 2004 Diffusion tensor imaging in newborns
Author Affiliations +
We have investigated the feasibility of obtaining high-quality Diffusion Tensor Magnetic Resonance Imaging (DTI) data in newborn humans. We show that the use of an MR-compatible incubator with customized RF headcoils can provide diffusion tensor maps of sufficient quality for quantitative DTI measurements and 3D fiber tracking. We have also investigated the effect of performing affine co-registration on the diffusion-weighted images, as is conventionally believed to be necessary to correct for eddy current distortion effects. We have found that co-registration indeed successfully eliminates the well-known bright band of high anisotropy that forms in the peripheral brain regions, and that such co-registration also reduces smaller interior regions of artifactually high diffusion anisotropy. In addition, we have investigated whether non-affine distortions exist in the diffusion-weighted images, as might be expected due to the existence of large susceptibility gradients. The results of performing 2nd order mutual information polynomial registration of the diffusion-weighted images to the non-diffusion-weighted (b=0) image in each slice show that subtle differences between affine and 2nd order co-registration do exist, which suggests that care must be taken when interpreting FA values in cortical brain regions. Finally, we present results of 3D white matter fiber tracking in the newborn brain. To preserve the full information content of the DTI data, we used simple Euler integration without noise filtering or fiber crossing detection. Our results show that the directionality of the major white matter pathways can be visualized in newborns.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jon F. Nielsen, Ashok Panigrahy M.D., and Stephan G. Erberich "Diffusion tensor imaging in newborns", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004);

Back to Top