Charting the human thalamus - basic contepts and recent developments

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"Charting the human thalamus - basic contepts and recent developments". Copyright (2011, András Jakab, ETHZ, University of Debrecen).

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Charting the human thalamus - basic contepts and recent developments

  1. 1. Charting the human thalamus: probabilistic tractography and segmentation <ul><li>A. JAKAB 1,3 , R. BLANC 1 , A. MOREL 2 , E. BERENYI 3 , G. SZEKELY 1 </li></ul><ul><li>Computer Vision Laboratory, ETH, Zurich </li></ul><ul><li>Center for Clinical Research, University Hospital Zurich </li></ul><ul><li>Department of Biomedical Laboratory and Imaging Science, University of Debrecen </li></ul>
  2. 2. OUTLINE <ul><li>Biological diffusion, diffusion-tensor imaging, tractography </li></ul><ul><li>Imaging of the thalamus: new possibilities </li></ul><ul><li>Neurosurgical targeting in the thalamus </li></ul><ul><li>Statistical shape modeling of the mean thalamus atlas: principles and validation </li></ul><ul><li>Further research topics </li></ul>Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY
  3. 3. What is so special about biological diffusion ? <ul><li>The average displacements are not equal for every direction in space = anisotropic diffusion </li></ul>
  4. 4. Donnerstag, 20. Oktober 2011
  5. 5. Models of biological diffusion <ul><li>Free diffusion ( isotropic ) </li></ul><ul><li>R estricted diffusion </li></ul><ul><li>H indered diffusion </li></ul>
  6. 6. What really causes anisotropic diffusion? Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed 2002;15:435-455 .
  7. 7. Diffusion weighted imaging 1. B0 image 2. DWI image(s) 3. ADCx,y,z… 4. ADC (average)
  8. 8. Donnerstag, 20. Oktober 2011 Departement/Institut/Gruppe
  9. 9. Diffusion tensor imaging
  10. 10. Deterministic fibertracking <ul><li>Diffusion follows the main directions of axonal population </li></ul><ul><li>Information pathways go where axons go (mostly..) </li></ul><ul><li>Diffusion direction creates “tracks” for neuronal connections </li></ul>
  11. 12. Deterministic fiber tracking: art or science?
  12. 13. Deterministic fiber tracking: art or science?
  13. 14. Deterministic fiber tracking: art or science? Right handed Left handed NeuroImage 35 (2007) 1064–1076
  14. 15. … so? <ul><li>Deterministic fiber tracking visualizes major tracts </li></ul><ul><li>Artistic approach: many degrees of freedom, initial setup, just visual control (qualitative) of results </li></ul><ul><li>False positive results </li></ul><ul><ul><li>Splitting/joining tracts, unreal, false connections </li></ul></ul><ul><ul><li>Tubes are not real tracts, false pictorization </li></ul></ul><ul><li>False negative results </li></ul><ul><ul><li>Fiber crossing appears as low anisotropy, tracking stopped or continued in wrong direction </li></ul></ul><ul><ul><li>Uncertainity is not modelled </li></ul></ul>Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY
  15. 16. Tracking neuronal pathways: other solutions
  16. 17. Tracking neuronal pathways: other solutions <ul><li>Modeling intravoxel diffusion </li></ul><ul><ul><li>Waive the simplification of the tensor </li></ul></ul><ul><ul><li>Use a partial volume model of N fibers for each voxel </li></ul></ul><ul><ul><li>= isotropic component + N (1-2-3) isotropic components with different orientation </li></ul></ul><ul><ul><li>Sample and build up distribution until it predicts the diffusion signal (D directions) </li></ul></ul><ul><ul><li>BedpostX: Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques + crossing fiber modeling </li></ul></ul><ul><ul><li>Markov Chain Monte Carlo sampling to build up distributions on diffusion parameters at each voxel </li></ul></ul><ul><li>Model the distribution of tracing “particles” through their trajectory modelled by this diffusion model </li></ul><ul><li>Cumulate the trajectories of tracing particles: probability of brain voxels to be connected to the seed voxel </li></ul>
  17. 18. <ul><li>ProbtrackX: probabilistic framework + crossing fiber resolution </li></ul><ul><li>Basics </li></ul><ul><ul><li>Characterize the uncertainity in fibre orientation at each point in the brain </li></ul></ul><ul><ul><li>Generate PDFs – probability density functions </li></ul></ul><ul><ul><li>Repeat a tracking (streamline) process many times using randomly sampled directions from an initial point of space (seed) </li></ul></ul><ul><ul><li>Determine the voxel frequency of connection to define the probability of connection </li></ul></ul><ul><ul><li>Simply: emit “tracing samples” that propagate through the brain and count the traversed sampled per voxel </li></ul></ul>Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY
  18. 19. Further implications of probabilistic tracking Behrens et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging Nature Neuroscience 6 , 750 - 757 (2003) Jakab et al. Connectivity-based parcellation reveals interhemispheric differences in the insula Brain Topography – in press - (2011) “ Based on connection targets” “ Clustering based on similarities of connectome”
  19. 20. Donnerstag, 20. Oktober 2011 Departement/Institut/Gruppe MTT Sat., Gringel et al. 2009 7T T1-w Zürich Morel et al. 1997 fMRI - Hulme et al. 2010 DTI+SSM Atlas- Jakab et al. 2011?
  20. 21. Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Transcranial MR-guided Focused Ultrasound Surgery (1 st Study on TcMRgFUS thalamotomy – Kinderspital, Zurich) Deep brain stimulation <ul><ul><li>New demands </li></ul></ul>Image-guided interventions in the thalamus Higher precision of structural imaging, intraprocedural New information on internal structure (nuclei) and function (connection) Direct targeting, individual maps
  21. 22. State of the art imaging of the thalamus with DTI Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Diffusion tensor imaging and probabilistic tractography visualizes cortico-thalamic connections Top right images> Behrens et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging Nature Neuroscience 6 , 750 - 757 (2003) Clinical, 1.5T 3DT1 Gross, structural information
  22. 23. STUDY OBJECTIVES <ul><li>To develop a target map generation tool for image-guided neurosurgery </li></ul><ul><li>Fit a 3D thalamus atlas to the patient’s geometry, refined by functional information (e.g. locations of specific cortico-thalamic connectivities) </li></ul><ul><li>Assess the feasibility by: </li></ul><ul><ul><li>observing ultrahigh-field MRI and postmortem MRI images with intrathalamic contrast </li></ul></ul><ul><ul><li>Use DTI corticothalmic tractgraphy to observe specific connections </li></ul></ul>Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY
  23. 24. Methods I. – Creation of thalamic statistical shape models (SSM) Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY A Krauth, R Blanc, A Poveda, D Jeanmonod, A Morel, G Székely (2010) A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data . Neuroimage. 49(3). 2053-2062. <ul><li>Generate 3D MEAN THALAMUS ATLAS – 7 thalami </li></ul><ul><li>Statistical shape models: </li></ul><ul><ul><li>Functions that describe complex geometric shapes </li></ul></ul><ul><ul><li>Incorporate inter-subject variability </li></ul></ul><ul><ul><li>Use sparse observations (“predictors”) </li></ul></ul><ul><ul><li>Guide and constrain geometry </li></ul></ul><ul><li>Include visible thalamic outlines in the SSM </li></ul>
  24. 25. Methods II. – Cortico-thalamic tractography Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY <ul><li>Perform Cortico-thalamic probabilistic tractography in 40 subjects </li></ul><ul><li>Incorporate average connectivity maxima (center-of-mass) points in SSM </li></ul>
  25. 26. Donnerstag, 20. Oktober 2011 Departement/Institut/Gruppe
  26. 27. Biological correspondence: regions defined by Atlas vs. connectivity
  27. 28. Biological correspondence: regions defined by Atlas vs. connectivity
  28. 29. Biological correspondence: regions defined by Atlas vs. connectivity
  29. 30. <ul><li>Cortico-thalamic connections as patient-specific landmarks: feasibility of the center-of-gravity points of connectivity maps </li></ul>
  30. 31. Cortico-thalamic connections depicted using probabilistic tractography. Points represent the center-of-gravities of each connectivity map. Axial (top) view, isometric 3d display.
  31. 32. Connections of the precentral and postcentral gyrus and the spatial relationship between connectivity-based landmarks and the MNI152-transformed Morel Atlas data (VLpv, VPLa, VPLp nuclei). Points represent the center-of-gravities of each connectivity map. Superior view, isometric 3d display.
  32. 33. Results – aligned maps and intrathalamic landmarks Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Black outlines: SSM-matched thalamus atlas Red spot: somatosensory connections, VPL
  33. 34. Results – Comparison to ACPC matching Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Blue: SSM-based target map Red: ACPC aligned thalamus atlas Comparison on 1.5 T clinical imaging and CORTICOTHALAMIC TRACTOGRAPHY VL nucleus (VLa, VLpv) Somatomotor connections Somatomotor connections
  34. 35. Results – Comparison to ACPC matching Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Blue: SSM-based target map Red: ACPC aligned thalamus atlas Comparison on 7.0 T post mortem imaging CENTROMEDIAN NUCLEUS
  35. 36. Testing of the outline-based method: postmortem images
  36. 37. Testing of the outline-based method: postmortem images
  37. 38. Testing of the outline-based method: postmortem images
  38. 39. Testing of the outline-based method: postmortem images
  39. 40. Results – spatial accuracy (quantitative) Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Name of the structure tested Alignment method Mean distance (mm) Median distance (mm) Thalamus outline ACPC reg. with scaling 1.44 ± 0.44 1.24 ± 0.44 Thalamus outline Rigid reg. of surface 1.16 ± 0.11 1.07 ± 0.13 Thalamus outline SSM matching of outline 0.83 ± 0.1 0.56 ± 0.09 Thalamus outline Hybrid SSM matching (internal landmarks) 1.07 ± 0.18 0.83 ± 0.17 Postmortem nuclei (AV, MDmc, MDpc) SSM matching of outline 0.79 ± 0.22 0.59 ± 0.14
  40. 41. An application – MRgFUS targeting Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY Individual target volume adapted to individual geometry, after segmentation of iMRI image.
  41. 42. CONCLUSIONS <ul><li>(1) We aligned a 3D mean thalamus atlas to the patient’s geometry with feasible accuracy (< 1mm) </li></ul><ul><li>(2) Comparison with the conventional ACPC matching method shows superiority (3) Nonlinear deformation of the thalamus makes the shape more individual = follows individual variability (4) DTI corticothalamic tractography can be implemented in the method </li></ul><ul><li>(5) Such target maps can be used for image-guided neurosurgery </li></ul>Donnerstag, 20. Oktober 2011 D-ITET / COMPUTER VISION LABORATORY

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