DIFFUSION TENSOR IMAGING: A CLINICAL DTI PROTOCOL

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The goal of this research continues to refine and improve the methodology of diffusion tensor imaging (DTI) for the investigators and collaborators on the P41 RR09784 ?Center for Advanced MR Technology at Stanford? effort. Diffusion tensor imaging (DTI) is a promising new technique for the assessment of white matter (WM) structural integrity and connectivity. We have added DTI to a wide variety of brain MR protocols such as acute and chronic stroke, trauma, multiple sclerosis, premature babies as well as to a number of psychiatric protocols. Preliminary results from a variety of studies suggest that DTI may predict the cognitive and motor performances of a patient from this 4-minute scan. Diffusion is measured along six non-collinear directions. For each gradient direction, four images were acquired and averaged. Two images with no diffusion weighting (b = 0s/mm2) were acquired and a set of Inversion Recovery (IR) images for CSF nulling were acquired with b = 0s/mm2; these images were used to unwarp the diffusion weighted images, which resulted in a more robust unwarping than using the non-IR b=0 images. The DTI sequence requires 4 minutes. One of the more interesting aspects of DTI involves the 3-D visualization of the white matter orientation in the brain. While the fractional anisotropy maps are typically displayed in a gray scale, the actual water proton diffusion vectors may be overlaid onto a color map. We have been evaluating various ways of displaying the complex proton diffusion information in a meaningful and informative format. One such approach involves oversampling of the original data followed by a vector stream placed within a 3-D color scheme and in models for fiber tracking algorithms.