Mapping the MRI voxel volume in which thermal noise matches physiological noise—Implications for fMRI

doi:10.1016/j.neuroimage.2006.09.039

NeuroImage

Volume 34, Issue 2, 15 January 2007, Pages 542-549

https://doi.org/10.1016/j.neuroimage.2006.09.039 Get rights and content

Abstract

This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the “suggested” voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)³, (2.1 mm)³ and (1.4 mm)³, respectively. Serendipitously, (1.8 mm)³ cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.

Introduction

Since its inception, functional magnetic resonance imaging (fMRI) has become an important tool for studying human brain function and organization. Blood oxygenation level dependent (BOLD) contrast is the most commonly used in fMRI. To improve BOLD specificity it is desirable to obtain high spatial resolution functional maps (Cheng et al., 2001, Beauchamp et al., 2004). However, minimum voxel size in fMRI is limited by the MRI signal-to-noise ratio (SNR) (Edelstein et al., 1986). It is not advantageous to choose a voxel size that is too large either. Not only does this reduce specificity because of partial volume effects (PVE), but also more significantly, while increases in SNR are achieved, diminishing gains in temporal signal to noise ration (TSNR) result because of increasing contribution of physiologic noise (Yoo et al., 2001, Parrish et al., 2000, Krüger et al., 2001). TSNR is defined as a ratio of the average voxel time course signal over time course standard deviation. TSNR is the primary measure of the ability to detect BOLD signal changes (Parrish et al., 2000, Bellgowan et al., 2006). The nonlinear relationship between TSNR and SNR in gradient-echo BOLD imaging has been characterized at 3 Tesla, and has recently been verified and confirmed at 7 T (Triantafyllou et al., 2005). At 3 T we take advantage of the recent advances in multichannel MRI receiver and multi-element array coil technology (Bodurka et al., 2004, de Zwart et al., 2004) to map the relationship between TSNR and SNR. From this relationship, on a voxel-wise basis, we introduce the “suggested” voxel volume (SVV) for fMRI and construct voxel-wise SVV maps illustrating its spatial non-uniformity. We define the SVV as the imaging volume in which the physiological noise contribution (σ_P) equals the non-physiological (σ₀ = thermal + MRI scanner) system noise. Starting from this “suggested” point, if one tries to increase signal to noise by reducing resolution, the gains are increasingly diminishing. On the other hand if one tries to increase resolution, the losses in SNR are relatively rapid. This is essentially the point at which one can get the highest SNR for the least loss in resolution.

The choice of a somewhat arbitrary assignment and definition of “suggested point” helps to characterize the entire curve relating TSNR and SNR. The primary problem with providing a more quantitative “cost function” (based on SNR) is that it varies depending on the parameters of each investigator's study. For some studies, it might be unacceptable to have a signal to noise below a certain level. For other studies, there might be more time in which to average, thus allowing a lower “suggested” value.

At the SVV, shown schematically in Fig. 1, as the voxel volume increases further, increases in SNR translate into diminishing increases in TSNR. Therefore, larger voxels and/or improved MRI signal reception do not necessarily translate to improved BOLD detection. On the other hand, if the voxel volume is reduced relative to the SVV, the SNR and TSNR are reduced in increasingly direct proportion, potentially prohibiting detection of small BOLD signal changes.

In summary, in this work we: a) determine the theoretical expressions for the suggested fMRI voxel volume and the image SNR necessary to reach this volume; b) introduce a fast and simple EPI T₁ mapping technique for brain segmentation; c) experimentally map and determine the suggested volumes for different brain tissue compartments; and d) discuss implications for fMRI.

Section snippets

Suggested fMRI voxel volume

It is assumed that the noise variance in the imaging voxel is a superposition of thermal plus MRI scanner-related noise (σ₀²) and physiological noise contributions (σ_p²). Krüger et al. (2001) introduced the model describing the physiological noise in gradient-echo EPI BOLD resting state brain data that depends on the MRI signal strength (σ_p = λS, where S is MRI time course average signal strength after reaching steady state). This physiological noise is shown to be significantly greater in

Material and methods

Imaging hardware included: 3 T General Electric Signa VH/3 MRI scanner (3T/90 cm, whole body gradient inset 40 mT/m, slew rate 150 T/m/s, whole body RF coil) equipped with home-built 16 channel MRI digital receiver (Bodurka et al., 2004); standard T/R head coil, and 16-channel receive-only brain array (Nova Medical Inc) (de Zwart et al., 2004). For functional runs, a single shot full k-space gradient echo EPI with matrix size 128 × 96 was used. Time series data were collected during which the

Results

Fig. 1 shows the relationship between the TSNR and SNR ratios (Eq. (1) simulation) for three different brain compartments and a phantom. The suggested voxel volumes, where system (thermal and scanner related) noise equals physiological noise, are marked as large green, red, and black points for white, gray matter, and CSF, respectively. Brain tissue specific upper limits of TSNR are also shown. The TSNR_L limits for GM, WM, and CSF of 78, 117, and 47, respectively, were used (Bodurka et al., 2005

Discussion

The imaging voxel volume for BOLD fMRI studies is one of the most important variables affecting activation detection and specificity in functional brain imaging. Based on the physiological noise model in BOLD imaging (Krüger and Glover, 2001, Krüger et al., 2001), we have derived a novel formula to compute the “suggested” fMRI voxel volume (Eqs. (3) (4), (5)), taking into consideration the relationship between SNR and TSNR. At the “suggested” fMRI voxel volume (SVV) the magnitude of physiologic

Conclusions

We propose the “suggested” voxel volume for gradient-echo EPI BOLD imaging, which we define as the voxel volume where the physiological noise contribution is equal to system and thermal noise contribution. We have provided a simple criterion for a necessary SNR in order to reach the SVV condition. At the suggested voxel volume the image SNR is equal to the temporal SNR limit for a given brain tissue compartment. The temporal SNR limits for different brain compartments in fMRI are known and/or

Acknowledgments

The authors thank Kay Kuhns for editorial assistance. This research was supported by the Division of Intramural Research Programs for NIH, NIMH, and NINDS.

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Published by Elsevier Inc.

Mapping the MRI voxel volume in which thermal noise matches physiological noise—Implications for fMRI

Abstract

Introduction

Section snippets

Suggested fMRI voxel volume

Material and methods

Results

Discussion

Conclusions

Acknowledgments

NeuroImage

Neuron

Comput. Biomed. Res.

Brain Res. Bull.

Int. Rev. Neurobiol.

NeuroImage

NeuroImage

NeuroImage

Unraveling multisensory integration: patchy organization within human STS multisensory cortex

Nat. Neurosci.

Scalable multichannel MRI data acquisition system

Magn. Reson. Med.

Determination of the brain voxel specific temporal signal-to-noise ratio limit of 3T BOLD-weighted time course data

Signal-to-noise ratio and parallel imaging performance of a 16-channel receive-only brain coil array at 3.0 Tesla

Magn. Reson. Med.

The intrinsic signal-to-noise ratio in NMR imaging

Magn. Reson. Med.

Predicting the orientation of invisible stimuli from activity in human primary visual cortex

Nat. Neurosci.

Distributed and overlapping representations of faces and objects in ventral temporal cortex

Science