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From Spatial Regularization to Anatomical Priors in fMRI Analysis

From Spatial Regularization to Anatomical Priors in fMRI Analysis. Fragmented Map. Regularized Map. Wanmei Ou William Wells Polina Golland. Core 1 Meeting – May 23 rd , 2006. Main Contributions. Propose new spatial regularization method: Incorporating Anatomical Information

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From Spatial Regularization to Anatomical Priors in fMRI Analysis

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  1. From Spatial Regularization to Anatomical Priors in fMRI Analysis Fragmented Map Regularized Map Wanmei Ou William Wells Polina Golland Core 1 Meeting – May 23rd, 2006

  2. Main Contributions • Propose new spatial regularization method: Incorporating Anatomical Information • Empirically Compare proposed methods with traditional methods

  3. Road Map • Background and Motivation • Markov Random Field (MRF) and Anatomical Guided MRF Model • Mean Field Approximation Solver • Experimental Evaluation • Conclusions

  4. Road Map • Background and Motivation • Markov Random Field (MRF) and Anatomical Guided MRF Model • Mean Field Approximation Solver • Experimental Evaluation • Conclusions

  5. From fMRI Images to fMRI Analysis Image Acquisition MRI fMRI Task protocol: Auditory, Vision, etc. Spatial Regularization Voxel-by-voxel detector Threshold Activation Map Scatter activation

  6. Road Map • Background and Motivation • Markov Random Field (MRF) and Anatomical Guided MRF Model • Mean Field Approximation Solver • Experimental Evaluation • Conclusions

  7. Goal and Approaches Goal: Recover True Activation through Spatial Regularization Our Approach: • Incorporate MRF into General Linear Model (GLM) Statistic • Include Anatomical Information into MRF

  8. Detailed Approaches Our Approach: • 1. Incorporate MRF into GLM Statistic • Capture spatial dependency • Overcome over-smoothing effect • 2. Include Anatomical Information into MRF • Activation is more likely in gray matter • Spatial dependency is strong within tissue type Activation Maps MRF MRI Synthetic Ground Truth Segmentation Gray, White, Other

  9. General Linear Model (GLM) Protocol-Independent Signal Two Hypotheses Not-active voxel Active voxel Protocol-Dependent Signal GLM ML Estimate F or T statistic P-value Traditional Approach General Log Likelihood Ratio (Cosman, 04) Our Approach

  10. Markov Random Field Spatial Priors: Likelihood: -- Hidden Activation State -- Noisy Observation/Statistic MAP Estimate:

  11. Incorporating Anatomical Information Combine activation state & tissue type MRI Segmentation -- Hidden Activation State -- Tissue Type MAP Estimate: -- Segmentation Label -- Noise Statistic

  12. Markov Random Fields Solvers • Binary MRF Min-Cut/Max-Flow (Min-Max) Binary MRF Only • Gibbs Sampling Slow • Simulated Annealing Slow • Belief Propagation Fast, Approximation • Mean Field Fast, Approximation

  13. Road Map • Background and Motivation • Markov Random Field (MRF) and Anatomical Guided MRF Model • Mean Field Approximation Solver • Experimental Evaluations • Conclusion and Future work

  14. Mean Field • Approximate by • Iterative up-date rule Belief: Prob. Of voxel is active • Approximated MAP

  15. Mean Field Similar up-date rule while incorporating anatomical information Approximated MAP

  16. Alternative Anatomically Guided Filters • No smoothing with Anatomical Information • Suppress all activation in the non-gray matter. • Anatomically Guided Gaussian Filter • Adjust weights based on segmentation labels.

  17. Road Map • Background and Motivation • Markov Random Field (MRF) and Anatomical Guided MRF Model • Mean Field Approximation Solver • Experimental Evaluation • Synthetic Data • Real fMRI Data • Conclusions

  18. Experiments – Synthetic Data Sets Low SNR Fragmented Activation Maps Noise SNR = -6.3dB Noise SNR = -9.3dB Activation Maps Threshold: False positive = 0.05% Forward Model GLM Detector + Synthetic Ground Truth

  19. Experiments – Synthetic Data Sets Noise Forward Model GLM with different smoothing methods + Synthetic Ground Truth • No Smoothing • No Smoothing w/ Anatomical Info • Gaussian Smoothing w/o Anatomical Info • Gaussian Smoothing w/ Anatomical Info • MRF w/o Anatomical Info • MRF w/ Anatomical Info

  20. ROC Analysis Without Anatomical Information Min-Max (Exact Solver) vs. Mean Field (Approximation) SNR = -6dB SNR = -9dB

  21. ROC Analysis Without Anatomical Information MRF (Mean Field) vs. Gaussian Smoothing SNR = -6dB SNR = -9dB

  22. ROC Analysis With Anatomical Information MRF (Mean Field) vs. Gaussian Smoothing SNR = -6dB SNR = -9dB

  23. Road Map • Background and Motivation • Markov Random Field (MRF) and Anatomical Guided MRF Model • Mean Field Approximation Solver • Experimental Evaluation • Synthetic Data • Real fMRI Data • Conclusions

  24. Evaluation on Real Data “Ground Truth” GLM Majority Voting GLM 8 task epochs comparisons GLM with various spatial regularizers 2 task epochs

  25. Activation Maps Comparison Anat No Smoothing Gaussian MRF “Ground Truth” No Smoothing+Anat Gaussian+Anat MRF+Anat Three Epochs sm007ep3

  26. Activation Maps Comparison Anat No Smoothing Gaussian MRF “Ground Truth” No Smoothing+Anat Gaussian+Anat MRF+Anat Three Epochs sm007ep3

  27. Activation Maps Comparison Anat No Smoothing Gaussian MRF “Ground Truth” No Smoothing+Anat Gaussian+Anat MRF+Anat Three Epochs sm007ep3

  28. Conclusions • New Spatial Regularization method • Anatomical Bias • Empirical Evaluation • ROC analysis • Activation maps • MRF + Anatomical Information • Increase detection accuracy with reduced-length signal

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