Combining spatial priors and anatomical information for fMRI detection
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, traditiona...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2015
|
| Online Access: | http://hdl.handle.net/1721.1/100235 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1811082089299181568 |
|---|---|
| author | Ou, Wanmei Wells, William M. Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ou, Wanmei Wells, William M. Golland, Polina |
| author_sort | Ou, Wanmei |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:57:24Z |
| format | Article |
| id | mit-1721.1/100235 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:57:24Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1002352022-10-01T07:14:42Z Combining spatial priors and anatomical information for fMRI detection Ou, Wanmei Wells, William M. Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ou, Wanmei Wells, William M. Golland, Polina 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. National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) Grant U54-EB005149) National Science Foundation (U.S.) (Grant IIS 9610249) National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Biomedical Informatics Research Network Grant U24-RR021382) National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) Grant P41-RR13218) National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) Grant R01-NS051826) National Science Foundation (U.S.) (CAREER Grant 0642971) National Science Foundation (U.S.). Graduate Research Fellowship National Center for Research Resources (U.S.) (FIRST-BIRN Grant) Neuroimaging Analysis Center (U.S.) 2015-12-14T13:29:18Z 2015-12-14T13:29:18Z 2010-03 2010-02 Article http://purl.org/eprint/type/JournalArticle 13618415 http://hdl.handle.net/1721.1/100235 Ou, Wanmei, William M. Wells, and Polina Golland. “Combining Spatial Priors and Anatomical Information for fMRI Detection.” Medical Image Analysis 14, no. 3 (June 2010): 318–331. https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1016/j.media.2010.02.007 Medical Image Analysis Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Ou, Wanmei Wells, William M. Golland, Polina Combining spatial priors and anatomical information for fMRI detection |
| title | Combining spatial priors and anatomical information for fMRI detection |
| title_full | Combining spatial priors and anatomical information for fMRI detection |
| title_fullStr | Combining spatial priors and anatomical information for fMRI detection |
| title_full_unstemmed | Combining spatial priors and anatomical information for fMRI detection |
| title_short | Combining spatial priors and anatomical information for fMRI detection |
| title_sort | combining spatial priors and anatomical information for fmri detection |
| url | http://hdl.handle.net/1721.1/100235 https://orcid.org/0000-0003-2516-731X |
| work_keys_str_mv | AT ouwanmei combiningspatialpriorsandanatomicalinformationforfmridetection AT wellswilliamm combiningspatialpriorsandanatomicalinformationforfmridetection AT gollandpolina combiningspatialpriorsandanatomicalinformationforfmridetection |