Selection of voxel size and slice orientation for fMRI in the presence of susceptibility field gradients: application to imaging of the amygdala

NeuroImage 19 (2003) 817– 825

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Selection of voxel size and slice orientation for fMRI in the presence of susceptibility field gradients: application to imaging of the amygdala Nan-Kuei Chen,a,b Chandlee C. Dickey,b,c,d Seung-Schik Yoo,b Charles R.G. Guttmann,a,b and Lawrence P. Panycha,b,* a

Center for Neurological Imaging, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA b Department of Radiology, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA c Brigham Behavioral Neurology Group, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA d VA Boston Healthcare System, Brockton Division, Brockton, MA, USA Received 26 August 2002; accepted 23 January 2003

Abstract The impact of voxel geometry on the blood oxygenation level-dependent (BOLD) signal detectability in the presence of field inhomogeneity is assessed and a quantitative approach to selecting appropriate voxel geometry is developed in this report. Application of the developed technique to BOLD sensitivity improvement of the human amygdala is presented. Field inhomogeneity was measured experimentally at 1.5 T and 3 T and the dominant susceptibility field gradient in the human amygdala was observed approximately along the superior-inferior direction. Based on the field mapping studies, an optimal selection for the slice orientation would be an oblique pseudo-coronal plane with its frequency-encoding direction parallel to the field gradient measured from each subject. Experimentally this was confirmed by comparing the normalized standard deviation of time-series echo-planar imaging signals acquired with different slice orientations, in the absence of a functional stimulus. A further confirmation with a carefully designed functional magnetic resonance imaging study is needed. Although the BOLD sensitivity may generally be improved by a voxel size commensurable with the activation volume, our quantitative analysis shows that the optimal voxel size also depends on the susceptibility field gradient and is usually smaller than the activation volume. The predicted phenomenon is confirmed with a hybrid simulation, in which the functional activation was mathematically added to the experimentally acquired rest-period echo-planar imaging data. © 2003 Elsevier Science (USA). All rights reserved.

Introduction T2*-weighted gradient-echo echo-planar imaging (EPI) sequences, because of their inherent sensitivity to blood oxygenation level-dependent (BOLD)-related susceptibility effect, are commonly used in acquiring functional magnetic resonance imaging (fMRI) data with BOLD contrast. However, T2*-weighted imaging is also very sensitive to the static field gradient created by the tissue-air susceptibility difference. This static susceptibility-related field gradient results in an intravoxel dephasing in certain regions-of-interest

* Corresponding author. Department of Radiology, Brigham and Womens Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Fax: ⫹1-617-264-5275. E-mail address: [email protected] (L.P. Panych).

(ROI) such as the amygdala, reducing the T2*-weighted MR signal intensity and, thus, the BOLD sensitivity (Deichmann et al., 2002). In fMRI studies, therefore, it is important to select appropriate scan parameters to minimize the dephasing artifact to improve BOLD sensitivity. Susceptibility-induced signal degradation in T2*weighted images depends on several physical parameters, such as the magnetic field strength, echo time, and the voxel geometry (voxel size and slice orientation). In this study, we will focus on selection of the appropriate voxel geometry to achieve optimal BOLD detectability. Several studies relevant to this topic and strategies designed to reduce susceptibility-related dephasing artifact have been reported previously. For example, it is well known that the dephasing artifact can be reduced and the in-plane MRI signal uniformity can be improved by selecting a smaller voxel size

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(Reichenbach et al., 1997; Young et al., 1988). Based on this fact, Merboldt et al. (2000, 2001) recently suggested that, in brain areas with pronounced dephasing artifact such as the amygdala, a small voxel size should be used for BOLD imaging. We argue that, although it is true that dephasing artifact is reduced through the use of smaller voxel size, the inherent signal-to-noise ratio also decreases when one chooses smaller voxels and, thus, simply minimizing dephasing artifact may not necessarily be the best strategy to maximize BOLD sensitivity. This argument follows from our previous work where it was shown that, in the absence of a susceptibility-related field gradient, the BOLD sensitivity is optimized when the voxel size matches the size of the activation area (Yoo et al., 2001). This may, in fact, lead one to choose a relatively low resolution if the goal is simply to detect the presence of activation as opposed, for example, to mapping the activation in fine detail. The conclusion of Yoo et al., (2001), that detection is optimized when the voxel matches the size of the activation area cannot, however, be directly applied to the cases in which there exists a significant susceptibility-related field gradient. This is because MR signal loss due to dephasing artifact also depends on the voxel geometry. A thorough investigation of the impact of the voxel geometry on BOLD sensitivity must also include the effects of this susceptibility-related signal loss. In this study, we present a general quantitative approach for selecting the optimal voxel geometry for fMRI in the presence of a susceptibility-related field gradient. This method is applied to fMRI of the amygdala, which is the anatomy of interest in studies of emotion. BOLD signal changes in the human amygdala have been successfully observed in numerous studies (Breiter et al., 1996; Buchel et al., 1998; Irwin et al., 1996; LaBar et al., 1998; Phillips et al., 1999; Schneider et al., 1997; Whalen et al., 1998). However, due to an anatomical location in proximity to the sinuses, BOLD contrast-to-noise ratio (CNR) in amygdala fMRI studies may be severely impacted by susceptibilityrelated signal loss (Merboldt et al., 2001). Using the voxel geometry suggested by the quantitative study presented in this study, the BOLD sensitivity in the human amygdala and thus detection of activation could be improved. The quantitative approach established in this study is general and can be applied to fMRI of other brain areas where there exist pronounced susceptibility-related field gradients.

Theory This section examines the dependence of BOLD CNR on the slice orientation and voxel size, in the presence of a susceptibility field gradient. The arguments and mathematical simulations presented in this section provide general guidelines for selecting appropriate voxel geometry for functional imaging of critical brain regions affected by pronounced field gradients.

First, the dependence of BOLD sensitivity on the slice thickness was studied with a one-dimensional (1D) mathematical simulation with the susceptibility field gradient along the slice-encoding direction. Gaussian-shaped spatial profiles of activation (FWHM ranging from 0.2 to 10 mm) were assumed, and MRI signal intensities in the selected slices (with the thickness ranging from 1 to 34 mm) were calculated by integrating the intravoxel isochromatic units (0.2 mm in each unit), which had inconsistent phase terms as a result of field gradients. The impact of susceptibility gradient on the functional CNR was evaluated by comparing the BOLD signal corresponding to various linear field gradients. Second, we studied the dependence of BOLD sensitivity on the spatial resolution in the presence of a susceptibility field gradient along the frequency-encoding direction. We simulated a 1D imaging field-of-view (FOV) of 200 mm with 1000 isochromatic units spaced at intervals of 0.2 mm. The phase term for each isochromatic unit was calculated for various levels of linear and nonlinear gradients, assuming the imaging echo time was 50 ms. Gaussian-shaped spatial profiles of activation, with FWHM ranging from 0.2 to 10 mm, were placed within the FOV. A full-size k-space dataset was simulated by taking the inverse Fourier transform of the isochromat distribution. Simulated MR datasets representing different frequencyencoding spatial resolution were obtained by truncating and zero-filling full-size k-space datasets. Image data were then reconstructed by taking the Fourier transform of the truncated and zero-filled k-space data, and the height of each reconstructed activation spatial profile was measured. The final BOLD sensitivity was then obtained by dividing the height of the reconstructed activation spatial profiles by the square root of the k-space size to account for the effects of low-pass filtering on a noise sequence. This approach to estimating BOLD sensitivity is similar to that used in our previous work (Yoo et al., 2001) except that we have included the effect due to a susceptibility gradient (different phase terms of isochromatic units). The outputs of the simulation are BOLD sensitivities as a function of different levels of susceptibility field gradients, size of the spatial activation profile, and spatial resolution. The BOLD sensitivity dependence on the slice thickness for a 5-mm FWHM (full-width half maximum) Gaussian activation profile is shown in Fig. 1a. It can be seen that (1) when the slice thickness is smaller than the activation volume, the BOLD sensitivity increases as the slice thickness increases, (2) the BOLD sensitivity gradually saturates when the slice thickness exceeds the activation volume, and (3) the BOLD sensitivity is generally lower when there exists a susceptibility field gradient. Fig. 1b shows the dependence of BOLD sensitivity on frequency-encoding spatial resolution for a Gaussian activation profile of 5-mm FWHM. In the absence of a susceptibility field gradient, the BOLD sensitivity reaches the maximum when the imaging voxel size approximately matches the size of activation (5

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mm). In the presence of a significant susceptibility field gradient, however, the BOLD sensitivity is generally lower, and the voxel size corresponding to the highest BOLD sensitivity also decreases (to 3 mm in the example shown in Fig. 1). Fig. 1 implies that a higher BOLD sensitivity may be achieved when the dominant field gradient is along the frequency-encoding direction, rather than the slice-encoding direction. This result suggests that the imaging slice orientation should be chosen in such a way that the dominant susceptibility field gradient is along the frequencyencoding direction, rather than the slice-selection direction. Choosing a susceptibility gradient to be along the phaseencoding direction is problematic for EPI, because the effective echo time, and thus the BOLD sensitivity, may be greatly altered by the field gradient along the phase-encoding direction (Gorno-Tempini et al., 2002). In addition, the geometric distortions may be more pronounced when the dominant field gradient is along the phase-encoding direction (due to the smaller bandwidth per pixel) (Ojemann et al., 1997). The impact of frequency-encoding spatial resolution on the BOLD sensitivity for various levels of linear and nonlinear field gradients is illustrated in Fig. 2a and b, respectively. The left panel of Fig. 2a shows five levels of linear field gradients (0, 1, 1.5, 2, and 2.5 Hz/mm), and the right panel shows the optimal voxel size dependence on activation volume for the corresponding field gradients. As expected, in the absence of a susceptibility field gradient, there is a linear relationship between voxel size and activation area with an approximate slope of 1. When there is a significant susceptibility field gradient, however, the voxel size for achieving the optimal BOLD sensitivity is smaller than expected. It cannot always be assumed that susceptibility gradients are linear, therefore, the optimal activation volume was computed for various levels of nonlinear field gradients and is shown in Fig. 2b. The voxel size resulting in the highest BOLD sensitivity decreases as the field gradient nonlinearity increases. Fig. 2 may be used as a guideline for selection of appropriate frequency-encoding spatial resolution for functional mapping of critical brain regions, once the field gradients are experimentally measured. The simulation results suggest that (1) the imaging slice orientation should be chosen in such a way that frequency -encoding is parallel to the dominant susceptibility field gradient direction (an oblique-plane prescription may be needed), and (2) the optimal fMRI voxel size depends on activation volume, the susceptibility field gradient strength, and gradient linearity.

Methods Experimental imaging studies were performed on 1.5and 3-T GE Signa systems (Milwaukee, WI, USA). First, the voxel geometry to achieve optimal BOLD sensitivity

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was determined by measuring susceptibility-related field gradients and then finding the dominant field gradient direction in the amygdala. The general approach to maximizing BOLD CNR with appropriate voxel geometry selection was experimentally confirmed by comparing amygdala EPI data corresponding to different voxel geometry with respect to (1) normalized standard deviation of EPI signal fluctuations over time, and (2) statistical significance of a BOLD activation mathematically added to an EPI time series. Measurement of the susceptibility-related field (1.5 and 3 T) Susceptibility-related field gradients in the amygdala were measured from five healthy human subjects after obtaining informed consent. Either axial-plane or coronalplane brain images were obtained using a multislice, multiecho gradient-echo imaging sequence (Chen and Wyrwicz, 1999a) with the following parameters: bandwidth 100 kHz, TR 5 s, 120 echoes with echo spacing time 0.872 ms, minimal TE 6.8 ms, maximal TE 110.5 ms, slice thickness 2 mm, in-plane matrix size 120 ⫻ 120, and FOV 240 ⫻ 240 mm. The phase terms of the acquired 120-echo images were unwrapped along the temporal domain (Ahn and Cho, 1987), and the off-resonance frequency for each image voxel was calculated from the phase evolution over time. Using this approach, three-dimensional (3 D) human brain field inhomogeneity maps (2 ⫻ 2 ⫻ 2-mm spatial resolution) were reconstructed. By comparing the off-resonance frequencies of neighboring pixels in the reconstructed human brain field map, components of the 3 D field gradient along the anterior-posterior (AP), superior-inferior (SI), and left-right (LR) directions were calculated. The three field gradient components (along AP, SI, and LR directions) in the amygdala area (manually segmented) were compared, and the direction of the dominant field gradient component was determined. The mean value of the dominant field gradient component within the human amygdala was also calculated. Selection of optimal slice orientation: experimental confirmation (1.5 T) In a block fMRI design with t -test analysis, the t statistic is equal to the product of the BOLD signal change (⌬S) and the square root of image number (N1/2), divided by 2 times the standard deviation (s.d.) of MRI signal fluctuation over time (Parrish et al., 2000). Since ⌬S is the product of the baseline MR signal intensity and the percentage BOLD signal change (p), the t value can also be expressed as the product of p and N1/2, divided 2 times the normalized standard deviation (n.s.d.), as expressed by Eq. (1). t⫽

pN 1/ 2 2共n.s.d.兲

(1)

Therefore, for a fixed number of image acquisitions and a

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signal intensities in these two ROIs were measured from the four acquired EPI datasets, and the normalized standard deviations (i.e., the index for the BOLD sensitivity) were calculated and compared. Selection of optimal voxel size: a hybrid fMRI simulation (1.5 T)

Fig. 1. Simulated BOLD sensitivity corresponding to different voxel sizes and different levels of susceptibility-related field gradients along the (a) slice-selection direction and (b) frequency-encoding direction, assuming a 5-mm activation area.

fixed percentage BOLD signal change, the detectability of BOLD activation depends only on the normalized standard deviation of MR signal fluctuation. To evaluate the impact of slice orientation on the normalized standard deviation and thus the statistical significance of an fMRI study, four EPI datasets (60 time points) with the same voxel size (2 ⫻ 4 ⫻ 6 mm) but different orientations were acquired from a healthy subject and their normalized standard deviations were compared. Scan parameters included bandwidth 100 kHz, echo spacing time 0.872 ms, TR 4 s, TE 50 ms, FOV 240 ⫻ 240 mm, in-plane matrix size 40 ⫻ 60 or 120 ⫻ 60, and slice thickness 2 or 6mm. The voxel geometries of the acquired EPI time series are summarized in Table 1. Two ROIs (6 ⫻ 8 ⫻ 6 mm for each) located within the left and right amygdala, respectively, were selected for evaluation of the MRI signal fluctuation over time. MRI

To assess the impact of the frequency-encoding resolution (along the dominant field gradient direction) on the fMRI statistical significance, a hybrid fMRI simulation (in which BOLD activation were mathematically added to experimentally acquired rest-period EPI data) was performed. This fMRI simulation is appropriate for a quantitative evaluation of the BOLD detectability, since the activation ground truth is known and the physiological noise is taken into consideration (Lange et al., 1999). A rest-period EPI time-series dataset (60 time points) was first acquired from a healthy subject at 1.5 T. Scan parameters included bandwidth 100 kHz, echo spacing time 0.872 ms, TR 4 s, TE 50 ms, FOV 240 ⫻ 240, in-plane matrix size 120 ⫻ 60, and coronal-slice thickness 2 mm. The simulated fMRI dataset was constructed from the acquired EPI data in the following steps. First, an ROI (various sizes) located in the left amygdala was manually selected to be the activated area. The block paradigm assumed in this hybrid simulation consisted of 15 rest time points, 15 activation time points, 15 rest time points, and 15 activation time points. The magnitudes of MRI signals in the selected ROI were elevated by 6% in activation period, while the phase terms were not changed. Second, the BOLD-embedded data were then converted back to k-space data with inverse Fourier transformation. By truncating and zerofilling the high spatial-frequency k-space data along the frequency-encoding direction, new fMRI data sets with different frequency-encoding spatial resolutions (ranging from 2 to 11 mm) were constructed. Third, the t values of the pixels within the activation area were calculated from the constructed data sets corresponding to different frequencyencoding spatial resolutions. A mean t value was calculated from each data set. To further evaluate the impact of the susceptibility field gradient on the relation between the spatial resolution and

Table 1 The voxel geometries used and the normalized standard deviation (NSD) measured from series of axial-plane EPI (data 1 and data 2) and coronal-plane EPI (data 3 and data 4)a

Data Data Data Data

1: 2: 3: 4:

axial axial coronal coronal

AP

LR

SI

NSD

2mm (freq.) 6mm (freq.) 2mm (slice) 6mm (slice)

4mm (phase) 4mm (phase) 4mm (phase) 4mm (phase)

6mm (slice) 2mm (slice) 6mm (freq.) 2mm (freq.)

2.13% 1.49% 2.13% 0.90%

a All data were acquired from subject ID 1. EPI, echo-planar imaging; AP, anterior-posterior; LR, left-right; SI, superior-inferior.

Fig. 2. Imaging voxel sizes resulting in the optimal BOLD sensitivity, as a function of size of the activation area, in the presence of (a) linear susceptibility-related field gradients and (b) nonlinear field gradients. Fig. 3. Coronal-plane reconstruction of the acquired three-dimensional multiecho gradient-echo image and field maps (for subject ID 1 scanned at 1.5 T). The outlined areas indicate the amygdala. (a) The first echo image of the multiecho gradient-echo data. (b) The 40th echo of the multiecho gradient-echo data. (c) The calculated field inhomogeneity map, with the values presented in the scale bar (in Hz) on the left. Absolute values of field gradients along the anterior-posterior, left-right, and superior-inferior directions are shown in (d), (e), and (f), respectively. The values (in Hz/mm) are presented in the scale bar on the right.

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Table 2 Susceptibility field gradients (Hz/mm) in the amygdala measured from 5 subjects at 7 scan sessionsa Field

Subject

1.5T 1.5T 1.5T 1.5T 3T 3T 3T

ID ID ID ID ID ID ID

1 2 2 3 4 1 5

AP

LR

SI

Magnitude

Angle

⫺0.059 ⫺0.23 ⫺0.08 0.55 ⫺0.23 ⫺0.2 1.84

0.11 ⫺0.2 ⫺0.03 ⫺0.15 ⫺0.31 0.24 ⫺0.03

1.45 1.89 2.06 1.12 2.41 3.43 2.69

1.46 1.91 2.06 1.26 2.44 3.45 3.26

⫺2 deg ⫺7 deg ⫺2 deg 26 deg ⫺5 deg ⫺3 deg 34 deg

a The angle between the dominant field gradient direction and the SI direction is also presented. EPI, echo-planar imaging; AP, anterior-posterior; LR, left-right; SI, superior-inferior.

BOLD sensitivity, another simulation study with a reduced susceptibility effect was performed by using the same parent EPI dataset. In this simulation, the background phase terms of the parent high-resolution EPI data were set to zero. Thus, the susceptibility artifacts in low spatial-resolution datasets constructed from the phase-corrected parent EPI data set were effectively reduced (although not completely removed because there was still intravoxel dephasing effect in the high-resolution voxels). Mean t values were calculated by using the same simulation procedures described in the previous paragraph.

Results Measurement of the susceptibility-related field gradients Based on the acquired multiecho gradient-echo images, a 3D field inhomogeneity map of the brain was reconstructed. The first and 40th echo magnitude image of a selected coronal-section (from subject ID 1) are shown in Fig. 3a and b, respectively, and the calculated field inhomogeneity map is shown in Fig. 3c. The off-resonance frequencies (in Hz) of this field map are displayed in the scale bar on the left. The components of the field gradients along the AP, LR, and SI directions are shown in Fig. 3d, e, and f, respectively. The histogram of the field gradients within the manually segmented amygdala areas of this subject is shown in Fig. 4. It can be seen that the field gradient in the amygdala is greatest along the SI direction. Susceptibility field gradients in the amygdala measured from five subjects in seven scan sessions are shown in Table 2. The tilt angle between the dominant susceptibility field gradient and the SI direction varies in this intra- and intersubject study, due to slightly different scan positioning and anatomic variation. Based on the field gradient measurements presented in Table 2, either a pseudo-coronal or pseudo-sagittal plane should be selected for amygdala fMRI so that the frequency encoding is along the dominant field gradient direction. Further, taking the anatomic structure of the human amyg-

dala into consideration, pseudo-coronal plane is more suited for amygdala fMRI since it can cover both left and right amygdala with fewer slices. Selection of optimal slice orientation: experimental confirmation Table 1 compares the normalized standard deviations of four EPI time series corresponding to different slice orientations and encoding schemes (acquired from a single subject). It can be seen that the lowest normalized standard deviation (i.e., the highest BOLD sensitivity) can be achieved when a high frequency-encoding resolution is chosen along the dominant field gradient direction (the SI direction in this subject). The experimental results agree with the simulations and arguments presented in the Theory section. Selection of optimal voxel size: fMRI simulation Results of the hybrid fMRI simulations are presented in Fig. 5. Fig. 5a compares the image qualities corresponding to different frequency-encoding resolutions. As expected, the susceptibility-related signal loss is more pronounced in low-resolution EPI (10 mm; middle image) than high-resolution EPI (3.75 mm; left image), especially in the amygdala area (indicated by the square box). Note that by mathematically removing the background phase terms in parent images, the susceptibility artifact in the reconstructed lowresolution image (10 mm) is reduced (right image). The dependence of the functional detectability on the voxel size for one simulated activation of 6mm is shown in Fig. 5b. When there is a susceptibility field gradient, the optimal voxel size is significantly smaller than the activation dimension (4 mm vs. 6 mm). Even when the susceptibility effects are artificially reduced (red curve), the optimal voxel size is still smaller than the activation area. The likely reason is that the physiological noise is often spatially coherent (less gain from averaging effect), and thus one might expect a further reduction in optimal voxel size for optimal BOLD sensitivity. Fig. 5c presents the optimal voxel size for simulations at various activation dimensions for this subject. Comparison of Fig. 5c with Fig. 2a predicts that the susceptibility field gradients within the selected regions are between 1 and 1.5 Hz/mm, which is consistent with the field gradient measurements for this subject (Fig. 4).

Discussion Several approaches have been reported for reducing intravoxel dephasing artifact and may be applied to improve the BOLD sensitivity in brain regions such as the amygdala where there is pronounced field inhomogeneity. Methods include 2D and 3D z-shim (Frahm et al., 1988; Yang et al.,

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1998; Constable and Spencer, 1999; Glover, 1999;), and radiofrequency (RF) pulse excitation with nonlinear phase responses (Cho and Ro, 1992; Chen and Wyrwicz, 1999b; Stenger et al., 2000, 2002). These approaches can be effective; however, they usually require special pulse sequences and reconstruction methods and may involve a sacrifice in temporal resolution. This study presents a quantitative approach for determining the impact of voxel geometry on the BOLD sensitivity in the presence of a susceptibility dephasing artifact and presents guidelines for selecting optimal voxel size and slice orientation. We believe that an appropriate choice of voxel geometry for imaging regions such as the amygdala is a good place to start in planning fMRI experiments where susceptibility is expected to be a problem. This can be as simple as adjusting the scan parameters of existing pulse sequences. Temporal resolution, rather than being sacrificed, may actually be enhanced in the optimization process. Based on field inhomogeneity map obtained at 1.5 and 3 T, we calculated the susceptibility field gradients within the human amygdala and found them to be most significant along the SI direction. Therefore, the amygdala BOLD sensitivity obtained with coronal-plane imaging is generally higher than that obtained with axial-plane imaging. An optimal selection for the slice orientation would be an oblique plane with its slice-selection direction perpendicular to the dominant field gradient in the amygdala, and the frequency-encoding direction parallel to the field gradient measured from each subject. Unfortunately, when EPI frequency encoding is along the SI direction, the subject may experience peripheral nerve stimulation, depending on the pulse sequence implementation and several scan parameters (e.g., gradient waveform, gradient strength, and echo spacing time). Thus, if this orientation is chosen, optimizing EPI gradient waveforms and parameters are needed so that the peripheral nerve stimulation can be reduced and does not become a limiting factor in fMRI voxel geometry selection. A possible approach to reduce the peripheral nerve stimulation is to use alternative gradient coil designs (Kimmlingen et al., 2002). However, these are not yet readily available. If the peripheral nerve stimulation occurs and the SI direction must be phase-encoded in the pseudo-coronal imaging, the following issues must be addressed. First, one must ensure that neck and shoulder MR signals outside the FOV are not aliased into the brain image. We have not found that this aliasing artifact in T2*-weighted EPI to be significant and it can be avoided completely by using 2D spatially selective RF pulses (Rieseberg et al., 2002). Second, the k-space representation of the amygdala signals may deviate from the central ky line due to the susceptibility field gradient along the phase-encoding direction, and thus the effective echo time and BOLD sensitivity may be altered. An appropriate echo time must be selected to compensate for this effect. Based on our simulations for the field gradient range

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measured for 1.5 T (e.g., 1.5 Hz/mm), the relationship between optimal voxel size and size of activation is approximately linear before 2 mm (see Fig. 2a). However, for activation size greater than 2 mm, the optimal voxel size remains less than 4 mm regardless of the activation extent. Given these results, we would not tend to choose a resolution of higher than 2 mm because it is unlikely that the activation size is less than this. Without a prior knowledge of activation volume, we may consider choosing a smaller voxel size (e.g., 2mm) in data acquisition. Images with lower resolution can then be reconstructed from the acquired high-resolution data, if this is needed for enhancement of BOLD sensitivity. It may also be possible to increase statistical significance by adjusting the TE when acquiring functional data from brain regions with pronounced susceptibility field gradients (i.e., to account for the shorter T2* values in those brain regions). A T2* map can be generated from a multiecho gradient-echo dataset prior to the fMRI study, and an appropriate TE can be chosen for fMRI of the selected brain region. It should be noted that the optimal TE depends on the voxel size and slice orientation. If an inappropriate voxel size is chosen and the tissue T2* value is already very short in brain regions with a significant susceptibility field gradient, then the BOLD sensitivity cannot be efficiently recovered even if a short TE is chosen (Gorno-Tempini et al., 2002). However, optimizing the TE for ROIs where susceptibility effects are significant means that TE is not likely to be optimized elsewhere in the brain. The ideal situation, therefore, would be to acquire data at multiple TEs so that the optimal TE can be selected on a voxel-by-voxel basis. This is not generally practical for standard whole-brain fMRI but, with the introduction of parallel imaging, this is becoming a more feasible option. In this study, the field inhomogeneities were measured with multiecho gradient-echo imaging technique (Chen and Wyrwicz, 1999a). This technique was chosen because of its capability of acquiring high-resolution images corresponding to a large range of echo times in a relatively short scan time. For example, in our implementation, 120 echo images of 2-mm isotropic resolution with TE ranging from 6.8 to 110 ms were acquired in 10 min (39 slices, TR ⫽ 5 s). High-quality field inhomogeneity map and T2* map can be calculated from the acquired dataset, and both of them are valuable for selection of appropriate scan parameters in fMRI scans. However, it should be noted that the field maps measured in this approach may reflect the off-resonance effects due to both main field inhomogeneity and EPI readout gradient-induced eddy current. To confirm that the observed anisotropic field gradients in the amygdala are mainly due to the susceptibility effect rather than the eddy current, we have conducted two additional studies. First, a field map was acquired from a uniform water phantom with multiecho gradient-echo imaging, and the magnitudes of the observed field gradients were found to be much smaller than that in the amygdala. Since

Fig. 4. Histogram of the field gradients within the amygdala along the anterior-posterior (AP), left-right (LR), and superior-inferior (SI) directions (for subject ID 1 scanned at 1.5 T). Fig. 5. (a) Comparison of the quality of images used in the hybrid simulation. Susceptibility artifact in amygdala region (indicated by the box) is more pronounced in low-resolution (middle) than high-resolution (left) echo-planar image. By mathematically correcting for the phase terms, the artifact is reduced (right). (b) The t value dependence on the frequency-encoding resolution for one of the simulated activation dimensions (6 mm). (c) The optimal functional magnetic resonance imaging voxel size for various activation dimensions. BOLD,

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the eddy current effect was the same in both human and phantom scans, we concluded that the observed field gradient anisotropy in the amygdala was mainly due to the susceptibility effect rather than the eddy current effect. Second, conventional gradient-echo images were acquired from one of the subjects, and the calculated field map (free from EPI eddy current effect) was found to be comparable to the map acquired with multiecho gradient-echo imaging method. If the eddy current effect is found to be pronounced in the MR system, then other field mapping approaches should be used so that susceptibility field gradient can be measured accurately. In this study, the improvement of BOLD sensitivity and other theoretical predictions are confirmed with a hybrid simulation in which the functional activation were mathematically added to the experimentally acquired rest period EPI datasets. Advantages of this hybrid simulation include (1) the activation ground truth can be defined, (2) the BOLD sensitivity can be easily evaluated for various activation regions and volume, and (3) the physiological noises are taken into consideration (Lange et al., 1999). A confirmation of the theoretical predictions by extensive fMRI study remains to be performed.

Acknowledgments This research is supported by NIH R01-NS37922 (S.S.Y., C.R.G.G., and L.P.P.) and Dr. Koichi Oshio (N.C.).

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