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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Afonso, D.M. Sanches, J.M. Lauterbach, M.H. |
| Copyright Year | 2008 |
| Description | Author affiliation: Inst. for Syst. & Robot., Lisbon (Afonso, D.M.; Sanches, J.M.) |
| Abstract | The emerging functional MRI (magnetic resonance imaging), fMRI, imaging modality was developed to obtain non-invasive information regarding the neural processes behind pre-determined task. The data is gathered in such a way that the extraction certainty of the desired information is maximized. Still this is a difficult task due to low Signal-to-Noise Ratio (SNR), corrupting noise and artifacts from several sources. The most prevalent method, here called SPM-GLM uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD hemodynamic response function (HRF), constant for the whole region of interest (ROI). A new algorithm, designed in a Bayesian framework, is presented in this paper, called SPM-MAP. The algorithm jointly detects the brain activated regions and the underlying HRF in an adaptative and local basis. This approach presents two main advantages: (1) the activity detection benefits from the method's high flexibility toward the HRF shape; (2) it provides local estimations for the HRF. The SPM-MAP algorithm is validated by using Monte Carlo tests with synthetic data and comparisons with the SPM-GLM are also performed. Tests using real data are also performed and results are compared with the ones provided by the SPM-GLM method tuned by the medical doctor. |
| Starting Page | 1255 |
| Ending Page | 1258 |
| File Size | 300408 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424420025 |
| DOI | 10.1109/ISBI.2008.4541231 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-05-14 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Brain Hemodynamics Bayesian methods Magnetic resonance imaging Signal to noise ratio Shape Inference algorithms Performance evaluation Data mining Magnetic noise Adaptive signal processing functional Magnetic Resonance Nervous system MAP estimation Biomedical signal detection |
| Content Type | Text |
| Resource Type | Article |
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