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Combining Spatial Priors and Anatomical Information for FMRI Detection
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Institution: |
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. wanmei@csail.mit.edu 2Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA. |
Publisher: |
Elsevier Science |
Publication Date: |
Jun-2010 |
Journal: |
Med Image Anal |
Volume Number: |
14 |
Issue Number: |
3 |
Pages: |
318-31 |
Citation: |
Med Image Anal. 2010 Jun;14(3):318-31. |
PubMed ID: |
20362488 |
Keywords: |
FMRI Detection, Markov Random Field, Mean Field, Variational approximation method, Spatial prior, Anatomical information, GLM, Projects:fMRIDetection |
Appears in Collections: |
NA-MIC, NAC, SPL |
Sponsors: |
NAMIC U54 EB005149 NSF IIS 9610249 NIH NCRR mBIRN U24-RR021382 NIH NCRR NAC P41 RR13218 NIH NINDS R01 NS051826 NSF CAREER 0642971 NCRR FIRST-BIRN U24 RR021992 |
Generated Citation: |
Ou W., Wells III W.M., Golland P. Combining Spatial Priors and Anatomical Information for FMRI Detection. Med Image Anal. 2010 Jun;14(3):318-31. PMID: 20362488. |
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| Paper: | Download, View online |
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In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.
Additional Material
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Ou-MEDIA2010-fig9.jpg (202.335kB)