Blind Estimation for Localized Low Contrast-to-Noise Ratio BOLD Signals

Accurate detection of low contrast-to-noise ratio (CNR) blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) data is important for presurgical planning and cognitive research. Robust detection is challenging in small regions of low CNR activation since the probability of detecting each individual voxel is low. We present a processing technique for improving the detection of localized low CNR BOLD signals in fMRI data. When applied to synthetic fMRI data, this blind estimation scheme significantly improves the probability of correctly detecting voxels in a small region of activation with a CNR between 0.5 and 1.0 compared to the standard general linear model approach. More activation is detected in expected (based on input stimulus) regions of experimental data after processing with the proposed technique.

[1]  Daniel B. Rowe,et al.  An evaluation of thresholding techniques in fMRI analysis , 2004, NeuroImage.

[2]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[3]  G. Radda,et al.  Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. , 1982, Biochimica et biophysica acta.

[4]  C. Westin,et al.  Sequential anisotropic Wiener filtering applied to 3D MRI data. , 2007, Magnetic resonance imaging.

[5]  Jean-Baptiste Poline,et al.  Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment , 2003, IEEE Transactions on Medical Imaging.

[6]  Dimitri Van De Ville,et al.  Integrated wavelet processing and spatial statistical testing of fMRI data , 2004, NeuroImage.

[7]  Arnold Neumaier,et al.  Estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[8]  V. Mountcastle.  The columnar organization of the neocortex. , 1997, Brain : a journal of neurology.

[9]  D. Y. von Cramon,et al.  Comparison of Filtering Methods for fMRI Datasets , 1999, NeuroImage.

[10]  Michael Unser,et al.  Statistical analysis of functional MRI data in the wavelet domain , 1998, IEEE Transactions on Medical Imaging.

[11]  I Kanno,et al.  Statistical methods for detecting activated regions in functional MRI of the brain. , 1998, Magnetic resonance imaging.

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

[13]  X Hu,et al.  Wavelet transform‐based Wiener filtering of event‐related fMRI data , 2000, Magnetic resonance in medicine.

[14]  Jos B. T. M. Roerdink,et al.  Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing , 2004, IEEE Transactions on Medical Imaging.

[15]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[16]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.

[17]  Farzad Kamalabadi,et al.  Basis selection for wavelet processing of sparse signals , 2008, Signal Process..

[18]  Carl E. Rasmussen,et al.  Bayesian Modelling of fMRI lime Series , 1999, NIPS.

[19]  Sylvain Faisan,et al.  Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models , 2005, IEEE Transactions on Medical Imaging.

[20]  Thomas E. Nichols,et al.  Combining voxel intensity and cluster extent with permutation test framework , 2004, NeuroImage.

[21]  Robert D. Nowak,et al.  Wavelet-based Rician noise removal for magnetic resonance imaging , 1999, IEEE Trans. Image Process..

[22]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[23]  E C Wong,et al.  Processing strategies for time‐course data sets in functional mri of the human brain , 1993, Magnetic resonance in medicine.

[24]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[25]  William K. Pratt,et al.  Generalized Wiener Filtering Computation Techniques , 1972, IEEE Transactions on Computers.

[26]  Karl J. Friston,et al.  A unified statistical approach for determining significant signals in images of cerebral activation , 1996, Human brain mapping.

[27]  Jos B. T. M. Roerdink,et al.  BOLD Noise Assumptions in fMRI , 2006, Int. J. Biomed. Imaging.

[28]  T A Carpenter,et al.  Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains , 2001, Human brain mapping.

[29]  Markus Svensén,et al.  Probabilistic modeling of single-trial fMRI data , 2000, IEEE Transactions on Medical Imaging.

[30]  Keith R. Thulborn,et al.  Specified-resolution wavelet analysis of activation patterns from BOLD contrast fMRI , 2001, IEEE Transactions on Medical Imaging.

[31]  R. Nowak,et al.  Generalized likelihood ratio detection for fMRI using complex data , 1999, IEEE Transactions on Medical Imaging.

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

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

[34]  Lars Kai Hansen,et al.  Modeling the hemodynamic response in fMRI using smooth FIR filters , 2000, IEEE Transactions on Medical Imaging.

[35]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Markus Barth,et al.  Contrast‐to‐noise ratio (CNR) as a quality parameter in fMRI , 2007, Journal of magnetic resonance imaging : JMRI.

[37]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[38]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[39]  Thomas E. Nichols,et al.  Non-white noise in fMRI: Does modelling have an impact? , 2006, NeuroImage.

[40]  Ian Charles Atkinson.  Techniques for Approximating Optimal Linear Estimators of Multidimensional Data , 2007 .

[41]  Karl J. Friston,et al.  Detecting Activations in PET and fMRI: Levels of Inference and Power , 1996, NeuroImage.

[42]  M. Lindquist,et al.  Validity and power in hemodynamic response modeling: A comparison study and a new approach , 2007, Human Brain Mapping.

[43]  M. J. Fadili,et al.  A comparative evaluation of wavelet-based methods for hypothesis testing of brain activation maps , 2004, NeuroImage.

[44]  Thomas E. Nichols,et al.  Controlling the familywise error rate in functional neuroimaging: a comparative review , 2003, Statistical methods in medical research.

[45]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[46]  François G. Meyer.  Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series , 2003, IEEE Transactions on Medical Imaging.

[47]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[48]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[49]  R Baumgartner,et al.  A hierarchical clustering method for analyzing functional MR images. , 1999, Magnetic resonance imaging.

[50]  Richard J. Davidson,et al.  Comparison of fMRI motion correction software tools , 2005, NeuroImage.