From Spatial Regularization to Anatomical Priors in fMRI Analysis

In this paper, we study Markov Random Fields as spatial smoothing priors in fMRI detection. Relatively high noise in fMRI images presents a serious challenge for the detection algorithms, creating a need for spatial regularization of the signal. Gaussian smoothing, traditionally employed to boost the signal-to-noise ratio, often removes small activation regions. Recently, the use of Markov priors has been suggested as an alternative regularization approach. In this work, we investigate fast approximate inference algorithms for using MRFs in fMRI detection, propose a novel way to incorporate anatomical information into the detection framework, validate the methods through ROC analysis on simulated data and demonstrate their application in a real fMRI study.

[1]  W. Eric L. Grimson,et al.  Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[2]  Karl J. Friston,et al.  Assessing the significance of focal activations using their spatial extent , 1994, Human brain mapping.

[3]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[4]  Xavier Descombes,et al.  Spatio-temporal fMRI analysis using Markov random fields , 1998, IEEE Transactions on Medical Imaging.

[5]  Donald Geman,et al.  Constrained Restoration and the Recovery of Discontinuities , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  W. Eric L. Grimson,et al.  Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery , 1998, MICCAI.

[7]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[8]  W. Eric L. Grimson,et al.  Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images , 2002, MICCAI.

[9]  Stuart Geman,et al.  Statistical methods for tomographic image reconstruction , 1987 .

[10]  John W. Fisher,et al.  Exact MAP Activity Detection in f MRI Using a GLM with an Ising Spatial Prior , 2004, MICCAI.

[11]  Jun Zhang.  The mean field theory in EM procedures for Markov random fields , 1992, IEEE Trans. Signal Process..

[12]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[13]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[14]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[15]  Charles R. G. Guttmann,et al.  Functional MRI of auditory verbal working memory: long-term reproducibility analysis , 2004, NeuroImage.

[16]  Ron Kikinis,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002 , 2002, Lecture Notes in Computer Science.

[17]  W. Eric L. Grimson,et al.  Discriminative Analysis for Image-Based Studies , 2002, MICCAI.

[18]  Xavier Descombes,et al.  fMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions , 1998, NeuroImage.

[19]  Jagath C. Rajapakse,et al.  Bayesian approach to segmentation of statistical parametric maps , 2001, IEEE Transactions on Biomedical Engineering.

[20]  Ari Visa,et al.  Contextual clustering for analysis of functional MRI data , 2001, IEEE Transactions on Medical Imaging.

[21]  J. W. Modestino,et al.  Use of the mean-field approximation in an EM-based approach to unsupervised stochastic model-based image segmentation , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[22]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[23]  A M Dale,et al.  Estimation and detection of event‐related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach , 2000, Human brain mapping.

[24]  Mark W. Woolrich,et al.  Fully Bayesian spatio-temporal modeling of FMRI data , 2004, IEEE Transactions on Medical Imaging.

[25]  N. Hartvig,et al.  A Stochastic Geometry Model for Functional Magnetic Resonance Images , 2002 .