Raimundo Sierra    DTI

Title:Nonrigid registration of diffusion tensor images
10.1999-3.2001

Full Report:HTML Version, PDF Version
Abstract:This thesis discusses diffusion tensor imaging as a Magnetic Resonance Imaging modality. Diffusion tensor imaging allows the observation of molecular diffusion in tissues in vivo and therefore the molecular organization in tissues. The main interest in this case is the observation of myelinated fibertracts in the brain of premature born babies. Myelination of fibers in the white matter of the brain is a fast process in the last few weeks preterm and the observation of this process gives an insight in the development of the human brain and allows a better and earlier detection of small injuries or abnormalities.

The goal is to match diffusion tensor images of neonates, and to build an enabling technology to ultimately generate a statistical atlas of the development of the brain in babies between 28 and 40 weeks postconceptional age. While it was not possible to build the statistical atlas in the time given, the complete process from preprocessing of the data to nonrigid alignment of diffusion tensor images has been implemented and successfully applied on some exemplary cases.

To better understand the characteristics of diffusion tensors and to be able to prove the correctness of the algorithms, a new way of displaying diffusion tensors was implemented. This method visualizes the diffusion tensor as ellipsoids in a voxel raster.

The following report outlines the medical background, the imaging acquisition process and the data processing path. The reader should be able to understand diffusion tensor imaging and the matching principles used and expand the provided software to fit specific and further needs.

To successfully build a meaningful atlas of the development of the brain in neonates, a number of three-dimensional cases needs to be processed and a statistical analysis of the results has to be performed. Therefore a correct incorporation of the different voxel dimensions has to be implemented. As the data quality of the diffusion tensor images in baby scans is very low, the incorporation and combination with other scanning modalities should be considered.In the past few years virtual reality based systems have been proposed and realized for many medical interventions. These simulators have the potential to provide training on a wide variety of pathologies. So far, realistic generation of anatomical variance and pathologies have not been treated as a specific issue. It has to be possible for a physician to generate an individual surgical scene for every training session.