Presentation on theme: "1 Detecting Subtle Changes in Structure Chris Rorden –Diffusion Tensor Imaging Measuring white matter integrity Tractography and analysis."— Presentation transcript:
1 Detecting Subtle Changes in Structure Chris Rorden –Diffusion Tensor Imaging Measuring white matter integrity Tractography and analysis.
2 Diffusion Weighted Imaging Diffusion weighted images measures random motion of water molecules. –In ventricles, CSF is unconstrained, so high velocity diffusion –In brain tissue, water more constrained, so less diffusion. DWI
3 Diffusion Tensor Imaging (DTI) DTI is an extension of DWI that allows us to measure direction of motion. DTI allows us to measure both the velocity and preferred direction of diffusion –In gray matter, diffusion is isotropic (similar in all directions) –In white matter, diffusion is anisotropic (prefers motion along fibers).
4 DTI The amount of diffusion occurring in one pixel of a MR image is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD). The non-uniformity of diffusion with direction is usually described by the term Fractional Anisotropy (FA). MD differsFA differs
5 Raw DTI data DTI scans apply gradients of different strengths (b-values) and directions (b-vectors). These highlight different diffusion directions. Simplest DTI is shown below: one b=0 image (no directionality), plus six images with b=1000 but each with a different b-vector.
6 Eddy current correction Gradients for DTI data cause spatial distortions. Different directions have different distortions. The B0 image has little distortion (it is a T2 weighted Image) Eddy current correction aligns all directions to the b0 (reference) image. Analogous to motion correction for fMRI data.
7 What is a tensor? A tensor is composed of three vectors. –Think of a vector like an arrow in 3D space – it points in a direction and has a length. The first vector is the longest – it points along the principle axis. The second and third vectors are orthogonal to the first. Sphere: V1=V2=V3 Football: V1>V2 V1>V3 V3 = V2 ???: V1>V2>V3
8 DTI MD FA V1 modulated by FA Color shows principle tensor direction, brightness shows FA
9 The crossing fibers problem Tensor (DTI) will have trouble distinguishing voxels with crossing fibers from isotropic regions. Tensors Reality CSF (isotropic) Crossing fibers
10 Tractography Programs like medInria allow us to measure integrity of connections between different regions.
11 Statistics – MD and FA The FA and MD maps from each individual can be normalized to standard space. Standard voxelwise statistics applied (like VBM) Allows us to infer differences in white matter integrity between groups. Group mean images based on normalized data.
12 Statistics – tensors You can measure fiber strength connectivity brain regions and see if this differs between groups. Traditionally, tensor maps are very difficult to normalize – we do not want to squish relative shape of tensor. – Recent advances are addressing this (DTITK)
13 TBSS - Tract-Based Spatial Statistics TBSS is a FSL approach to conduct between group comparisons of DTI data. projects data onto group-mean tract skeleton, allowing voxelwise analysis addresses alignment problems unsolved by nonlinear registration Overview www.fmrib.ox.ac.uk/fsl/tbss/index.htm l Tutorial www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/fdt/index.htm
14 Sample application Young adults vary in their ability to recollect information. TBSS shows that Fornix integrity predicts this variability (but not variability in familiarity). Rudebeck (2009) JoN