1 / 28

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

tait
Download Presentation

From Spatial Regularization to Anatomical Priors in fMRI Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related