Dynamic magnetic resonance inverse imaging of human brain function

MRI is widely used for noninvasive hemodynamic‐based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order‐of‐magnitude speedup in generating time‐resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time‐consuming gradient‐encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography (MEG) source localization techniques. Dynamic MR InI was evaluated by means of numerical simulations. InI was also applied to measure BOLD hemodynamic time curves at 20‐ms temporal resolution in a visual stimulation experiment using a 90‐channel head array. InI is expected to improve the time resolution of MRI and provide increased flexibility in the trade‐off between spatial and temporal resolution for studies of dynamic activation patterns in the human brain. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.

[1]  G. Glover,et al.  Physiological noise in oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[2]  J. Duyn,et al.  A. functional MRI technique combining principles of echo‐shifting with a train of observations (PRESTO) , 1993, Magnetic resonance in medicine.

[3]  J. Bodurka,et al.  Toward direct mapping of neuronal activity: MRI detection of ultraweak, transient magnetic field changes , 2002 .

[4]  S. Ogawa,et al.  An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Lawrence L. Wald,et al.  Reconstruction of sensitivity encoded images using regulariztion and discrete time wavelet transform estimates of the coil maps , 2002 .

[6]  J. Duyn,et al.  Design of a SENSE‐optimized high‐sensitivity MRI receive coil for brain imaging , 2002, Magnetic resonance in medicine.

[7]  S. Ogawa Brain magnetic resonance imaging with contrast-dependent oxygenation , 1990 .

[8]  D. Sodickson,et al.  A generalized approach to parallel magnetic resonance imaging. , 2001, Medical physics.

[9]  J. Xiong,et al.  Directly mapping magnetic field effects of neuronal activity by magnetic resonance imaging , 2003, Human brain mapping.

[10]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[11]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[12]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[13]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[14]  P. Hansen Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion , 1987 .

[15]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[16]  F. Jolesz,et al.  Dynamically adaptive MRI with encoding by singular value decomposition , 1994, Magnetic resonance in medicine.

[17]  Daniel K Sodickson,et al.  Phase-constrained parallel MR image reconstruction. , 2005, Journal of magnetic resonance.

[18]  John W Belliveau,et al.  Monte Carlo simulation studies of EEG and MEG localization accuracy , 2002, Human brain mapping.

[19]  J. Weaver,et al.  Wavelet‐encoded MR imaging , 1992, Magnetic resonance in medicine.

[20]  Renxin Chu,et al.  Magnetic Resonance in Medicine 51:22–26 (2004) Signal-to-Noise Ratio and Parallel Imaging Performance of a 16-Channel Receive-Only Brain Coil Array at , 2022 .

[21]  D. Sodickson Tailored SMASH image reconstructions for robust in vivo parallel MR imaging , 2000, Magnetic resonance in medicine.

[22]  L. Wald,et al.  A 96-channel MRI System with 23- and 90-channel Phase Array Head Coils at 1.5 Tesla , 2005 .

[23]  Alan C. Evans,et al.  A General Statistical Analysis for fMRI Data , 2000, NeuroImage.

[24]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[25]  B. Rosen,et al.  Functional mapping of the human visual cortex by magnetic resonance imaging. , 1991, Science.

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

[27]  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.

[28]  S. T. Nichols,et al.  Quantitative evaluation of several partial fourier reconstruction algorithms used in mri , 1993, Magnetic resonance in medicine.

[29]  M. McDougall,et al.  64‐channel array coil for single echo acquisition magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[30]  Fu-Nien Wang,et al.  Functional MRI using regularized parallel imaging acquisition , 2005, Magnetic resonance in medicine.

[31]  M Braun,et al.  Fast Magnetic Resonance Imaging Using Spiral Trajectories , 1987, Medical Imaging.

[32]  J W Belliveau,et al.  Functional cerebral imaging by susceptibility‐contrast NMR , 1990, Magnetic resonance in medicine.

[33]  L. Wald,et al.  A 32 Channel Receive-only Phased Array Head Coil for 3T with Novel Geodesic Tiling Geometry , 2005 .

[34]  A G Webb,et al.  Unifying linear prior‐information‐driven methods for accelerated image acquisition , 2001, Magnetic resonance in medicine.

[35]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[36]  Mark Bydder,et al.  Partial fourier partially parallel imaging , 2005, Magnetic resonance in medicine.

[37]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[38]  A. Dale,et al.  Distributed current estimates using cortical orientation constraints , 2006, Human brain mapping.

[39]  K. Kwong,et al.  Parallel imaging reconstruction using automatic regularization , 2004, Magnetic resonance in medicine.

[40]  J. J. van Vaals,et al.  “Keyhole” method for accelerating imaging of contrast agent uptake , 1993, Journal of magnetic resonance imaging : JMRI.

[41]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[42]  P. Mansfield Multi-planar image formation using NMR spin echoes , 1977 .

[43]  A K Liu,et al.  Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. , 1998, Proceedings of the National Academy of Sciences of the United States of America.