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Unbiased Longitudinal Processing of Structural MRI in FreeSurfer

Unbiased Longitudinal Processing of Structural MRI in FreeSurfer. Martin Reuter mreuter@ nmr.mgh.harvard.edu http:// reuter.mit.edu. What can we do with FreeSurfer ?. measure volume of cortical or subcortical structures compute thickness (locally) of the cortical sheet

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Unbiased Longitudinal Processing of Structural MRI in FreeSurfer

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  1. Unbiased Longitudinal Processing of Structural MRI in FreeSurfer Martin Reuter mreuter@nmr.mgh.harvard.edu http://reuter.mit.edu

  2. What can we do with FreeSurfer? • measure volume of cortical or subcortical structures • compute thickness (locally) of the cortical sheet • study differences of populations (diseased, control)

  3. We'd like to: • exploit longitudinal information • (same subject, different time points)) Why longitudinal? • to reduce variability on intra-individual morph. estimates • to detect small changes, or use less subjects (power) • for marker of disease progression (atrophy) • to better estimate time to onset of symptoms • to study effects of drug treatment • ...

  4. Challenges in Longitudinal Designs • Over-Regularization: • Temporal smoothing • Non-linear warps • Limited designs: • Only 2 time points • Special purposes (e.g. only surfaces, only CSF/WM/GM) • Bias[Reuter and Fischl 2010] • Interpolation Asymmetries [Yushkevich et al 2010] • Asymmetric Registrations • Asymmetric Information Transfer

  5. How can it be done? • Stay unbiased with respect to any specific time point by treating all the same • Create a within subject template (base) as an initial guess for segmentation and reconstruction • Initialize each time point with the template to reduce variability in the optimization process • For this we need a robust registration (rigid) and template estimation

  6. Robust Registration [Reuter et al 2010] Inverse consistency: • a symmetric displacement model: • resample both source and target to an unbiased half-way space in intermediate steps (matrix square root) Source Target Half-Way

  7. Robust Registration [Reuter et al 2010](c.f. Nestares and Heeger, 2000)‏ Limited contribution for outliers [Nestares&Heeger 2000] Square Tukey'sBiweight

  8. Robust Registration [Reuter et al 2010] Tumor data courtesy of Dr. Greg Sorensen Tumor data with significant intensity differences in the brain, registered to first time point (left).

  9. Robust Registration [Reuter et al 2010] Target Target

  10. Robust Registration [Reuter et al 2010] Registered Src FSL FLIRT Registered Src Robust

  11. mri_robust_register • mri_robust_registeris part of FreeSurfer • can be used for pair-wise registration (optimally within subject, within modality) • can output results in half-way space • can output ‘outlier-weights’ • see also Reuter et al. “Highly Accurate Inverse Consistent Registration: A Robust Approach”, NeuroImage 2010. http://reuter.mit.edu/publications/ • for more than 2 images: mri_robust_template

  12. Robust Template Estimation • Minimization problem for N images: • Image Dissimilarity: • Metric of Transformations:

  13. Robust Template Intrinsic median: • (Initialization) • Create median image • Register with median (unbiased) • Iterate until convergence Command: mri_robust_template

  14. Robust Template: MEDIAN Top: difference Left: mean Right: median The mean is more blurry in regions with change: Ventricles or Corpus Callosum Median: • Crisp • Faster convergence • No ghosts

  15. Robust Template for Initialization • Unbiased • Reduces Variability • Common space for: • - TIV estimation • - Skullstrip • - Affine Talairach Reg. • Basis for: • - Intensity Normalization • - Non-linear Reg. • - Surfaces / Parcellation

  16. Bias Subcortical Cortical Biased information transfer: [BASE1] and [BASE2]. Our Method: [FS5.1] [FS5.1rev] shows no bias.

  17. Simulated Atrophy (2% left Hippo.) Left Hippocampus Right Hippocampus Cross sectional RED, longitudinal GREEN Simulated atrophy was applied to the left hippocampus only

  18. Test-Retest Reliability Subcortical Cortical [LONG] significantly improves reliability 115 subjects, ME MPRAGE, 2 scans, same session

  19. Test-Retest Reliability Diff. ([CROSS]-[LONG]) of Abs. Thick. Change: Significance Map [LONG] significantly improves reliability 115 subjects, ME MPRAGE, 2 scans, same session

  20. Increased Power Left Hemisphere: Right Hemisphere Sample Size Reduction when using [LONG]

  21. Huntington’s Disease (3 visits) Independent Processing Longitudinal Processing [LONG] shows higher precision and better discrimination power between groups (specificity and sensitivity).

  22. Huntington’s Disease (3 visits) Rate of Atrophy Baseline Vol. (normalized) Putamen Atrophy Rate can is significant between CN and PHD far, but baseline volume is not.

  23. Still to come … • Future Improvements: • Same voxel space for all time points (in FS 5.1) • Common warps (non-linear) • Intracranial volume estimation • Joint intensity normalization • New thickness computation • Joint spherical registration http://freesurfer.net/fswiki/LongitudinalProcessing http://reuter.mit.edu Thanks to: the FreeSurfer Team, specifically Bruce Fischl and Nick Schmansky

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