Diffusion MRI. Outline. Biology: The Neuron. Brain connectivity. Biology: Brain Organization. Brain connections and fibers

Transcription

1 Outline Diffusion MRI Alfred Anwander Download of Slides: teaching/brainsignals1112 password: mpi-brain CBSWIKI: Cornet/DiffusionMRI Neuroanatomy Diffusion MRI Diffusion Tensor Imaging Quantitative analysis Net lecture: Deterministic and probabilistic Tractography Ma Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany Brain connectivity Why is it so important to know about connections in the human brain? The functions of a cortical area are determined by its etrinsic connections and intrinsic properties Biology: The Neuron The building block of the brain 100 Billion neurons in human brain Information flows from dendrites to synaptic terminals Aons are 2-20 µm in diameter and can be several inches long e.g. primary visual system Brain connections and fibers Biology: Brain Organization Brodmann, 1909 Corte White matter 1

2 Regional connectivity Assembling connectivity matrices: non-human primates (invasive) connections between segregated brain regions tract tracing long distance connections vast range of substances taken up by neurons and spread along their projections applied etracellularly to the living tissue by pressure injection Source: Schmahmann 07 Interregional connectivity Tract tracing in rats, cats monkeys Eample cocomac.org database (Stephan et al 2001, Kötter et al 2004) Knowledge about connectivity and location of fiber tracts Quantitative data rare no 3D representation not for humans Humans Isotropic diffusion of water Post-mortem dissection of fiber bundles qualitative data ( Ludwig & Klingler 1956) Polarized-light microscopy mean orientation of fibers in small regions (Aer 2007) In vivo: Diffusion MR can image the anisotropic microstructure of brain tissue regional connectivity indirect measurement indicating anatomical connections Brownian motion (Albert Einstein, 1905) Anisotropic diffusion of water in brain tissue Tissue Microstructure - Water diffusion Motion of water molecules White matter fiber bundles Water diffusion is sensitive to the underlying tissue microstructure and provides a unique method to assess its orientation Image from [Poupon PhD 1999] 2

3 How can we measure diffusion with MRI? Main magnetic field and field gradients Gradient orientation and brain anatomy Strong weak and negative gradient Eample of a gradient diagram Field homogeneity and signal frequency Dephase-rephase eperiment 3

4 Molecular motions: Diffusion process MRI: Signal sensitive to Diffusion Start Dephasing Stop Rephasing Diffusion Time Summary: Diffusion MRI Measuring diffusion with MR z Probes material microstructure, which determines particle mobility. A probability density p describes the displacement after time t. We use the shape of pto infer the microstructure of the material The Diffusion Tensor model Signal generation log(s 1 / s 0 ) = b 1 T D 1 log(s 2 / s 0 ) = b 2 T D 2... log(s m / s 0 ) = b m T D m Signal (distribution) Log(s/s 0 ) s = s 0 ep( b 1 T D 1 ) Distribution of the diffusion signal 4

5 Diffusion Tensor Imaging - DTI Gaussian water diffusion modeled with a diffusion tensor (Baser 1994) Diffusion tensor is aligned with the orientation of white matter fiber bundles. Maps of the diffusion tensor eleme The diffusion tensor describes boththe amount of diffusion, as well as the directions in which this diffusion is occurring. 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 ais. The second and third vectors are orthogonal to the first. Sphere: V1=V2=V3 Football: V1>V2 V1>V3 V3 = V2???: V1>V2>V3 Eigenvector analysis of the Diffusion Tensor Diagonalized Apparent Diffusion Coefficient / Mean Diffusivity The amount of diffusion occurring in one piel of a MR image is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD). MD = (λ1 + λ2 + λ3 )/3 Different Parts of the brain have different diffusion characteristics Parallel diffusivity λ = λ1 Radial diffusivity λ = (λ2 + λ3 )/2 Fractional Anisotropy The non-uniformity of diffusion with direction is usual described by the term Fractional Anisotropy (FA). FA encodes how strongly directional diffusion is (derived from diffusion tensor eigenvalues) Hence good marker for WM integrity i.e., good marker for disease, development, etc. Range: 0 (isotropic) to 1 (fully anisotropic) 5

6 Factors affecting FA values The Diffusion Tensor Water diffusion parallel to the fiber orientation Fiberorientation in every voel Basser et al, J Magn Reson 1994 Summary: Diffusion Tensor model Visualization: Glyphs For each point visualize diffusion through shape Anisotropy through colour Good shape, bad feature definition Heavy render workload Basser et al, J Magn Reson 1994 Orthogonal Tensor Invariants (Kindlmann, TMI 2007) Brain: A Network System Nice to have 3 orthogonal (independent) tensor-derived measures: MD, FA & Mode Mode: is the tensor tubular (one strong fiber) or flat-cylindrical (two strong fibers)? MD differs FA differs It s all about the flow of information. The first question is: How is everything wired up? How is information processed as it flows from point to point in the wiring diagram? Dynamic modeling using functional data 6

7 The Tractographic Problem Investigating human brain connectivity Fibre architecture water diffusion diffusion weighted MRI Diffusion-weighted MR imaging Fractional anisotropy Principal diffusion direction Local modelling of tissue directions Tractography DTI Fiber Tracking Stopping criteria Xue et al. MRM (1999) Fiber Assignment by Continuous Tracking (FACT) Vector directions of largest principal aes Tracking is started from the center of a selected voel Line is propagated by observing the vector direction of each voel fractional anisotropy angle both Mori et al. Introduction to Diffusion Tensor, Tractography Investigating human brain connectivity SHORT DTI can be used for tractography. This can identify whether pathways are abnormal. FRONTAL FIBRES FIBRES PARIETAL FIBRES OCCIPITA LONG FIBRES L FIBRES TEMPORAL FIBRES Inferior frontal occipital tract Cingulum Catani et al, Neuroimage,

8 Fiber tracts match prior dissection results Streamlines integration and interpolation Created by integratingan approimate path through the field Tensor interpolation Need a continuous tensor field Different methods Component-wise interpolation Eigenvector interpolation Mori et al. Introduction to Diffusion Tensor, Tracking tensors or vectors? Tensor contains more information Main direction Shape Tensor Line Approach Use not only the main direction, but the whole tensor Deflect the incoming direction by the tensor Anisotropic tensor deflects the streamline more than an isotropic tensor Isotropic tensor ( random main direction) has no influence v out =D*v in Whole Brain Tractography Fiber selection In vivo DWI with high spatial and angular resolution White matter fiber tracking: Tracks Visualization as lines or tubes together with anatomical slices Mori et al. Introduction to Diffusion Tensor,

9 Interactive Fiber Selection Selected Tracks Interactive placement of navigation tools Efficient robust fiber bundle selection Combination AND NOT Selection of bundle sub-components T1 MRT DTI MRT Selection and quantitative analysis of all major bundles Eact anatomical contet not visible Only a small part of all brain fibers selected : Large part of the white matter not analyzed Identification of Bundle Subcomponents Superior longitudinal fasciculus / Acruate fasciculus can be further subdivided Direct connection Indirect connection via the parietal lobe Different roles in functional processing of language DWI data and preprocessing 3 Tesla TIM TRIO/VERIO Scanner with 12/32 channel array coil. ma. gradient strength 40 mt/m. Min. 72 slices, TE 120 ms, mm. b-value = 1000, 60 dir. 1/3 ac., 15/45 min. Registration on 3D T1 via T2 images. Catani et al. Ann Neurol, Registration of b0 images Acquisition and motion correction Long acquisition time: 60 directions*72 slices*(3 av) -> 15/45 min: motion correction unavoidable. MPIL sequence with interspersed high contrast images Use of non diffusion weighted images for motion correction: Interpolation of reg. param. Reg. param forall images Registration: alignment to standard space 3D T1 images in AC-PC orientation from our database Linear registration of b0 to T1 images Registration matri is combined with the MoCo matri and applied to the gradient images. Gradient directions are rotated corresponding to the registration matri. AC DTI T2 T1 9

10 Eample: preprocessing Copy data: /bin/tar vfz /a/probands/bdb/ / _090223_s6_ep2d_diff.tar.gz mv DICOMDIR _090223_S6_ep2d_diff Convert DICOM data /a/sw/misc/linu/diffusion/dictov _090223_S6_ep2d_diff/DICOMDIR _dmri.v Fit diffusion tensor /a/sw/misc/linu/diffusion/vdtensor _dmri.v fa_rgb.v vlv fa_rgb.v Check data quality /a/sw/misc/linu/diffusion/vimage2nifti _dmri.v _dmri.nii fslview _dmri.nii Automatic pre-processing mkdir mv _090223_S6_ep2d_diff/ / _ep2d_diff cp /home/newbdb/1014/mr1014_t1_*p*.v /a/sw/misc/linu/diffusion/dti_prepro_no_t1.sh Net Lectures Deterministic and probabilistic tractography: Crossing fibers in the brain Quantify brain connectivity Corte parcellation Literature Cbswiki: DiffusionMRI S. Mori "Introduction to Diffusion Tensor Imaging" H. Johansen-Berg and T. E.J. Behrens: Diffusion MRI" 10