A Tool for Comparison of PET and fMRI Methods: Calculation of the Uncertainty in the Location of an Activation Site in a PET Image

A technique for calculating the uncertainty in the location of an activation site in a PET image, without performing repeated measures, is presented. With the development of new fMRI methods for measuring cerebral hemodynamics, demonstration of the efficacy of these techniques will be critical to establish clinical utility. Comparisons with PET are a powerful tool for validating these new fMRI techniques. In addition to the fact that PET techniques are well-established methods for making physiological measurements in vivo, PET methods are also free of the geometric distortions and nonuniform signal-to-noise artifacts (due to signal dropout) common in fMRI techniques. Comparisons reported previously have been limited by the large number of trials acquired in single-subject fMRI studies and the small number of trials in a PET study (due to the radiation dose to the patient or the interscan delays for tracer decay). Our method calculates both the center of mass (CM) of a predefined region of interest and the uncertainty in the location of the CM using the preimage PET data (sinograms). Results of phantom studies demonstrate that our method is an unbiased measurement equivalent to that of repeated measures with a large number of images. Extension of this technique to estimate the uncertainty in the location of an activation site in a PET statistical parametric map will permit precise rigorous comparisons of PET and fMRI methods in single subjects without the constraints imposed by the relatively small number of PET measurements.

[1]  T. L. Davis,et al.  Mr perfusion studies with t1‐weighted echo planar imaging , 1995, Magnetic resonance in medicine.

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

[3]  D. Weinberger,et al.  Functional Mapping of Human Sensorimotor Cortex with 3D BOLD fMRI Correlates Highly with H215O PET rCBF , 1996, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  R. S. Hinks,et al.  Time course EPI of human brain function during task activation , 1992, Magnetic resonance in medicine.

[5]  Seong‐gi Kim Cmrr,et al.  Comparison of blood oxygenattion and cerebral blood flow effect in fMRI: Estimation of relative oxygen consumption change , 1997, Magnetic resonance in medicine.

[6]  A. John Mallinckrodt,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1993 .

[7]  R. Shulman,et al.  Lactate rise detected by 1H NMR in human visual cortex during physiologic stimulation. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[8]  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.

[9]  M. Raichle,et al.  Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography. , 1984, Journal of neurophysiology.

[10]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[11]  J. V. Haxby,et al.  Visual area topography as revealed by fMRI vs. PET , 1996, NeuroImage.

[12]  R. Huesman A new fast algorithm for the evaluation of regions of interest and statistical uncertainty in computed tomography. , 1984, Physics in medicine and biology.

[13]  P T Fox,et al.  Quantification of dynamic changes in cerebral venous oxygenation with MR phase imaging at 1.9 T , 1999, Magnetic resonance in medicine.

[14]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .

[15]  J R Reichenbach,et al.  In vivo measurement of changes in venous blood‐oxygenation with high resolution functional MRI at 0.95 Tesla by measuring changes in susceptibility and velocity , 1998, Magnetic resonance in medicine.

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

[17]  Egill Rostrup,et al.  Determination of relative CMRO2 from CBF and BOLD changes: Significant increase of oxygen consumption rate during visual stimulation , 1999, Magnetic resonance in medicine.

[18]  M. Mintun,et al.  Noninvasive functional brain mapping by change-distribution analysis of averaged PET images of H215O tissue activity. , 1989, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[19]  G. L. Brownell,et al.  Estimation of the Local Statistical Noise in Emission Computed Tomography , 1982, IEEE Transactions on Medical Imaging.

[20]  M. Mintun,et al.  Enhanced Detection of Focal Brain Responses Using Intersubject Averaging and Change-Distribution Analysis of Subtracted PET Images , 1988, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[21]  D. Noll,et al.  Functional MRI using steady‐state arterial water labeling , 1998, Magnetic resonance in medicine.

[22]  Meredith C. Phelps,et al.  Metabolic mapping of the brain's response to visual stimulation: studies in humans , 1981 .

[23]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[24]  J R Reichenbach,et al.  In vivo measurement of blood oxygen saturation using magnetic resonance imaging: A direct validation of the blood oxygen level‐dependent concept in functional brain imaging , 1997, Human brain mapping.

[25]  D. Ingvar Functional landscapes of the dominant hemisphere Review lecture presented at E.B.B.S. Annual General Meeting, Munich, September 8–10, 1975 , 1976, Brain Research.

[26]  Y Yonekura,et al.  Activation patterns of covert word generation detected by fMRI: comparison with 3D PET. , 1998, Journal of computer assisted tomography.

[27]  Jianfeng Gao,et al.  Perfusion‐based event‐related functional MRI , 1999, Magnetic resonance in medicine.

[28]  Peter Herscovitch,et al.  An approximation formula for the variance of PET region-of-interest values , 1993, IEEE Trans. Medical Imaging.

[29]  P T Fox,et al.  A Highly Accurate Method of Localizing Regions of Neuronal Activation in the Human Brain with Positron Emission Tomography , 1989, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[30]  Seong-Gi Kim Quantification of relative cerebral blood flow change by flow‐sensitive alternating inversion recovery (FAIR) technique: Application to functional mapping , 1995, Magnetic resonance in medicine.

[31]  J. Mazziotta,et al.  Positron emission tomography and autoradiography: Principles and applications for the brain and heart , 1985 .

[32]  R. Hoge,et al.  Perfusion‐based functional magnetic resonance imaging with single‐shot RARE and GRASE acquisitions , 1999, Magnetic resonance in medicine.

[33]  A Gelman,et al.  The Precision of Positron Emission Tomography: Theory and Measurement , 1991, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.