- Email: [email protected]
Echo-shifted multislice EPI for high-speed fMRI
Magnetic Resonance Imaging 24 (2006) 433 – 442
Echo-shifted multislice EPI for high-speed fMRI Andrew Gibson, Andrew M. Peters, Richard Bowtell4 Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, NG7 2RD Nottingham, UK Received 2 December 2005; accepted 2 December 2005
Abstract The advantages of event-related functional Magnetic Resonance Imaging (fMRI) and the increasing use of fMRI in cognitive experiments are both driving the development of techniques that allow images sensitive to the blood oxygen level-dependent effect to be acquired at everhigher temporal resolution. Here, we present a technique based on the use of echo shifting (ES) in conjunction with a multislice (MS) echo planar imaging (EPI) readout, which allows T2*-weighted images to be generated with a repetition time per slice that is less than the echo time (TE). Using this ES-MS-EPI approach, it is shown that images with a TE of 40 ms can be acquired with an acquisition time per slice of only 27 ms. The utility of the MS-ES-EPI sequence is demonstrated in a visual-motor, event-related fMRI study in which nine-slice image volumes are acquired continuously at a rate of 4.1 Hz. The sequence is shown to produce reliable activation associated with both visual stimuli and motor actions. D 2006 Elsevier Inc. All rights reserved. Keywords: fMRI; High-speed; Echo shifting; Temporal resolution
1. Introduction Functional magnetic resonance imaging (fMRI) has proven to be a very powerful tool in the investigation of human brain function. The rate of image acquisition is an important parameter in the design of any fMRI experiment, since changing the temporal resolution can affect many characteristics of the acquired data. Increasing the temporal resolution with which fMRI data are acquired has a number of advantages. First, the resulting improvement in the sampling and, hence, characterization of the haemodynamic response increases the potential analytical power of the technique and also reduces problems in analysis caused when spatially separate regions of the brain are sampled at different times. This is particularly advantageous in event-related fMRI in which short interstimulus intervals [1,2] are used or in which small differences in the timing of activation across trials need to be assessed [3]. Second, increasing the rate of acquisition of images in fMRI experiments via reduction of the repetition time (TR) between successive radiofrequency (RF) excitations can increase the efficiency with which activation is detected. The signal-to-noise ratio (SNR) of a
4 Corresponding author. Tel.: +44 115 9514737; fax: +44 115 9515166. E-mail address: [email protected] (R. Bowtell). 0730-725X/$ – see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.mri.2005.12.030
voxel time course in an fMRI experiment depends upon the volumar TR of the experiment. Reducing TR leads to a decrease in SNR in each image due to saturation; however, the number of images acquired per unit time increases, and with an optimal choice of flip angle, this leads to an overall increase in image SNR per unit time. This is favorable for fMRI experiments where temporal filtering is used to average signals over time to give a smoothed, reduced-noise representation of the original signal. As the filter shape is usually designed to match the form of the haemodynamic response, any increase in SNR per unit time in the data will lead to an increase in the blood oxygen level-dependent (BOLD) contrast-to-noise ratio of the temporally filtered data. Thus, as the SNR of the filtered data increases with increased sampling rate, the efficiency of detection will increase, assuming all other factors are held constant. Increasing the sampling rate in an fMRI study also has the advantage of allowing for the possible detection and subsequent removal of physiological noise due to cardiac and respiratory effects [4– 6]. For example, if the physiological noise is critically sampled, standard temporal filtering techniques can be used to attenuate frequencies at which physiological noise occurs. With volumar TRs of 2 to 3 s, as are commonly employed in fMRI experiments, fluctuations linked to the cardiac cycle are undersampled. Consequently, their contributions to signal variation are aliased to lower frequencies, making them
434
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
difficult to eliminate by simple low-pass filtering. Third, increasing the volumar acquisition rate potentially allows more accurate correction of subject motion, as movement occurring during the acquisition of an individual image volume will be reduced. When three-dimensional data are acquired via phase encoding across multiple excitations, movement during one volumar acquisition leads to image blurring and formation of artifacts that cannot be corrected by simple motion correction algorithms. In the case of multislice (MS) fMRI data, movement during the acquisition of an MS set means that different slices are acquired with the head at different positions so that the resulting data does not truly conform to the model of rigid body motion often assumed in motion correction algorithms [7]. Improvement in the accuracy with which the haemodynamic response can be characterized as the volumar sampling rate is increased has been demonstrated previously via simulation and experiment by Dilharreguy et al. [3]. In particular, their work showed a decrease in the accuracy of determination of the time at which the haemodynamic response peaks by approximately 50 ms for each second by which TR is increased in MS echo planar experiments for TRs in the range of 0.5–2.5 s. The improved characterization of cardiac fluctuations via a reduction in TR has been particularly explored in studies of functional connectivity [8], where it is important to sample cardiac fluctuations adequately [9,10], so that their contribution to covariation of signal in apparently connected spatial regions can be eliminated. 1.1. Improved efficiency of detection of BOLD responses In order to illustrate the potential for improved efficiency of detection of BOLD responses through reduction of the volumar TR, fMRI data sampled at a range of TR values was simulated and then subjected to a conventional statistical analysis. In generating the simulated data, it was assumed that T 2 b TR so that steady-state transverse magnetization was not formed, and it was also assumed that the Ernst angle was employed for RF excitation, so that the signal strength was given by sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 eT R=T1 S ð T R Þ ¼ S0 ð1Þ 1 þ eT R=T1
The event-related haemodynamic response to a stimulus was modeled using a typical gamma-variate function [12], giving a peak signal change of 2%. The simulated data represented 10 responses to stimuli separated by a 15-s interstimulus time, giving a total time series duration of 2.5 min. A random delay (in the range of F2.5 s) was introduced at the start of the time series to model the effects of slice ordering and variable haemodynamic delays. The simulated response was then sampled using different TRs varying from 0.2–5 s, and the level of white noise predicted by Eq. (2) was added. The resulting time series were then temporally filtered with a 2.8-s-width Gaussian filter, as might be used in the analysis of event-related fMRI data. A cross-correlation statistic was subsequently produced using the response prior to addition of noise as the reference. The correlation coefficient was then converted to a Z score, allowing for the correct number of degrees of freedom of the data. In order to investigate the variability of the efficiency of detection, the simulation was repeated 100 times at each TR with different random noise and delays. The results of the simulation are shown in Fig. 1, where the error bars represent F1 standard deviation of the averaged Z score at each value of TR. The simulation shows that efficiency (Z score) increases with decreasing TR, as predicted from simple noise analysis. The variability in the Z score also decreases with decreasing TR. This is important as the efficiency of detection of a response ideally should not depend strongly on the slice timing. The simulation shows that for increased efficiency with decreased variability, it is best to scan with as small a TR as possible. The theory used in the simulation is simplistic on several levels. The noise in the haemodynamic response is assumed to be white, irrespective of its source. It is clear that physiologically induced noise will not be white in nature and will have components that fall into definite frequency bands. If this noise is critically sampled at short TRs, it would mean that it would have more spectral power in the
where S 0 is the signal that would be measured at the same echo time (TE) using a 908 pulse with TR NNT 1. The noise, r, in the fMRI signal can be modeled using the theory of Kruger and Glover [11] as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ¼ r20 þ ½S ðTRÞ2 k2 ð2Þ where r 0 is the intrinsic thermal and scanner noise and the physiological noise is given by the product of k and S(TR). k is an TE-dependent constant, which characterizes the sum of the BOLD and non-BOLD physiological noise contributions [11].
Fig. 1. Results of a Monte Carlo simulation, demonstrating the increased efficiency (Z score) of detection with decreasing volumar TR, for a simulated train of haemodynamic responses. The simulation also shows that at lower volumar TRs, the variability in the efficiency (Z score) decreases.
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
stop band of the filter than assumed in the model, which would be advantageous. Physiological noise resulting from haemodynamic fluctuations is likely to have a correlation time of a few seconds as a result of the time scale of the haemodynamic response. It will consequently survive the filtering process, and the gain in efficiency achieved by going to short sampling times will therefore be reduced. 1.2. Sequences for rapid acquisition of T2*-weighted volumar image data The vast majority of fMRI experiments that are currently carried out employ an MS, echo planar imaging (EPI) sequence, as shown in Fig. 2A for image acquisition. Such sequences typically employ a TR of a few seconds, which is needed to acquire data from multiple (N 10) slices, since each slice acquisition requires a time of the order of 100 ms. Analysis of Fig. 2A shows that the minimum volumar TR is in fact given by TRcNSL ðTRF =2 þ TE þ TEPI =2Þ
ð3Þ
where N SL is the number of slices sampled, T RF is the duration of the RF pulse used for selective excitation and T EPI is the duration of the echo train generated by the switched gradient of the EPI sequence. Assuming a TE of 36 ms, as is commonly employed in fMRI at 3 T and use of an EPI sequence involving sampling of 64 echoes with a 1-kHz gradient switching frequency, and an RF pulse duration of 4 ms, gives a time per slice of about 70 ms. At 1.5 T where T2* and, hence, the optimal TE for fMRI is longer, a greater time per slice is required. Inspection of Eq. (3) shows that for a fixed number of slices, the most significant reduction of TR can be achieved by decreasing TE. However, this is not normally an acceptable approach since it leads to a reduction in the BOLD contrast. This leaves the alternative of shortening T RF or T EPI. Since T RF is generally much shorter than TE and T EPI, reducing its value has little effect on TR whilst leading to significant increases in the required RF power. Reduction of T EPI is more productive and can be achieved by increasing both the gradient switching rate and gradient strength and/or employing parallel imaging [13,14] so as to reduce the number of echoes acquired. Both strategies can, however, yield only limited reductions in TR, the former approach being restricted by the performance of gradient hardware and the need to avoid peripheral nerve stimulation, whilst use of too high speed-up factors in parallel imaging causes significant spatially varying noise enhancement [13]. Revisiting the example mentioned above, a two-fold reduction in the number of k-space lines sampled and an increase in the gradient switching frequency by a factor of 1.5 would yield a minimum TR of about 50 ms per slice, at the cost of a 1.7-fold reduction in SNR due to the shortening of the echo-train from 32- to 11-ms duration. The echo volumar imaging (EVI) technique [15] offers an alternative approach to the rapid generation of threedimensional, T2*-weighted image data. EVI generates three
435
dimensional images using a single RF excitation, followed by application of a periodic, switched gradient waveform and two orthogonal blipped gradients (applied as the switched gradient reverses sign). The switched gradient generates a train of gradient echoes that are phase-encoded in two orthogonal directions by the blipped gradients. Unfortunately, generating large image matrices with EVI necessitates the use of long echo trains, leading to a high sensitivity to image distortion due to magnetic field inhomogeneity [16]. Consequently, at high field, EVI is not well suited to use in fMRI studies where whole-brain coverage is required in conjunction with reasonable spatial resolution. EVI does, however, offer significant potential for the study of small cortical regions with high spatial and temporal resolution [17]. Principles of echo-shifting with a train of observations (PRESTO) imaging [18,19] has also been used for rapid acquisition of three-dimensional fMRI data [20 –22]. PRESTO employs multiple RF excitations of the volume, in conjunction with echo shifting (ES), so as to achieve a TE that is greater than the inter-RF pulse spacing, TR. A partial EPI readout is generally carried out after each excitation, and in three-dimensional mode, a varying phase-encoding gradient is applied in the third dimension immediately after RF excitation. In a recent implementation of 3D-PRESTO in combination with partial Fourier encoding and parallel imaging, Klarhffer et al. [20] were able to generate T2*-weighted images (with 444-mm3 voxels) covering the whole brain, with an acquisition time of 0.5 s per volume. Implementation of 3D-PRESTO involves the use of short TRs (e.g., TR = 29 ms), leading to the formation of steadystate transverse magnetization, which causes a more complicated signal dependence on TR, TE, T1, T2 and T2* than obtained in MS EPI. In addition, the persistence of highly dephased magnetization over several TR periods leads to sensitivity to motion and consequent temporal instability. This is often ameliorated via the use navigator echoes [21]. Here, we present a technique based on the use of ES in conjunction with MS EPI. This allows images in an MS set to be generated with a TR per slice that is less than the TE, thus speeding up the volumar data acquisition rate compared with conventional MS EPI, without altering image contrast. This MS-ES-EPI sequence has been implemented at 3 T and used to generate images with a TE of 40 ms and an acquisition time per slice of only 27 ms. The utility of the MS-ES-EPI sequence is demonstrated in a visual-motor, event-related fMRI study in which a nine-slice volume is acquired continuously at a volumar data rate of 4.1 Hz. The sequence is shown reliably to detect activation associated with both visual and motor stimuli.
2. Method 2.1. The MS-ES-EPI sequence Fig. 2B shows the timing diagram describing the MSES-EPI sequence. Successive slices are sequentially excited,
436
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
Fig. 2. (A) Conventional MS EPI sequence. The relative areas under the gradient pulses applied along the slice direction are shown numerically, and the number of the slice excited by each RF pulse is also indicated. (B) MS-ES-EPI sequence. The signal excited from slice 1 by the first RF pulse is refocused in the period following the second RF pulse, so that TE N TR/N slice. The gradient pulses on the slice axis that are shown using dashed lines can be added to provide greater dephasing of the signal from any slice during periods in which it is not being measured.
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
but the negative polarity-refocusing gradient pulse that usually follows immediately after slice selection is omitted, and a negative gradient lobe, the area of which is 1.5 times that of the slice-select gradient, is added at a time immediately preceding the application of the selective RF pulse. This combination of gradient pulses acts to shift the gradient echo of the signal formed due to each RF excitation by a time equal to the RF pulse separation [18]. Consequently, the signal from the nth slice is measured in the interval following the RF pulse that excites the n+1th slice, so that the TE is greater than the RF pulse spacing. The EPI switched (read) and blipped (phase-encoding) gradients are played out in the period between RF pulses, and both gradient waveforms are adjusted to have zero total area in each inter-RF-pulse interval, so that no phase is accumulated between successive RF pulse applications. Inspection of Fig. 2B, shows that the minimum time needed to acquire a MS data set using the MS-ES-EPI sequence is reduced to TR ¼ NSL ðTRFþ TEPI Þ
ð4Þ
whilst the minimum TE is equal to 1.5 (T RF+T EPI). Considering, once again, an EPI sequence involving sampling of 64 echoes with a 1-kHz gradient switching frequency and an RF pulse duration of 4 ms, the MS-ES-EPI sequence gives an acquisition time per slice of about 36 ms, whilst the minimum TE is 54 ms. The form of the gradient waveform applied in the slice direction means that the transverse magnetization generated by excitation of a particular slice by the nthik RF pulse is s ðm1Þs modulated by a phase factor of the form e 2 in the period following the n+mth RF pulse, where s is the spatial coordinate in the slice direction and k s =cG sT RF. Consequently, the phase dispersion across a slice of thickness, D s, is k s (m1) D s/2, taking a value of zero only when m = 1, and for other values having a minimum phase dispersion of k s D s/2. This dispersion needs to be large enough to crush the signal to very low levels in all interpulse periods except when m =1. If a larger phase dispersion is required to achieve an adequate level of signal attenuation, this can be achieved by adding gradient lobes at the beginning and end of the interpulse period, the areas of which are in the ratio 1:2, as shown using dashed lines in Fig. 2B. ES over more than one interpulse period can be achieved by varying the ratio of the gradient pulse areas, as introduced by Liu et al. [18] in their original description of the PRESTO technique. 2.2. Implementation at 3 T The MS-ES-EPI sequence was implemented on a 3-T imaging system that was previously constructed inhouse and equipped with an insert head gradient coil [23]. This system has only one RF channel for reception, and so, implementation of parallel imaging in conjunction with the
437
MS-ES-EPI sequence was not possible. A sinusoidal, switched gradient waveform of 1.9-kHz frequency was generated using a simple resonant circuit. Using a selective RF pulse of 1.5-ms duration, images with a matrix size of 6464 pixels were acquired with an in-plane resolution of 4 mm and a slice thickness of 10 mm. For this sequence, 64 echoes were acquired in a time of 17 ms. An extra 3 ms was required at the beginning of the echo train for the k-space pre-excursion in the blipped gradient direction and application of a dephasing gradient pulse in the slice direction. A time of 5 ms was needed at the end of the echo train for application of a rephasing gradient lobe in the phaseencoding direction and a dephasing gradient pulse in the slice direction. This gave an acquisition time per slice of 27 ms, and in these experiments, each volume consisted of nine slices, giving a volumar acquisition rate of 4.1 Hz. The minimum possible TE of 40 ms was employed, which is close to the optimum value for generating BOLD contrast at 3 T. The slice gradient waveform applied gave a value of k s of 14 mm1 so that the minimum phase dispersion of unwanted coherences across the slice thickness was 70 radians. Initial experiments were carried out using a transmit/ receive birdcage RF coil and employed a coronal slice orientation. In a series of 21 experiments, the flip angle of the RF excitation pulse was varied between 08 and 1368, and the image intensity in regions of grey and white matter was measured and compared with the predicted signal strength. In each experiment, 220 MS data sets were acquired in 54 s, and the data from the first 100 volumes was discarded to ensure that the signal had come to steady state. 2.3. Visual/motor fMRI studies The MS-ES-EPI technique was then applied in an fMRI study employing a simple, visually cued motor task. Five healthy volunteers were presented with a series of visual stimuli in the form of a flashing checkerboard at 29.16-s intervals. The duration of each stimulus was either 5 or 1 s. The subjects were instructed to press a button with the thumb of the right hand at the end of each visual cue, and the time of the button press was recorded. Fifteen cycles of each condition were presented, giving a total imaging time of 14 min and 35 s; during this time, a total of 3600 volumes of data were collected. Each volume consisting of 9, sagittal slices spanning the left hemisphere of the brain (4410-mm3 voxel dimensions) was acquired in 243 ms, with TE =40 ms. A surface RF coil of 14-cm diameter was positioned on the right side of the head and used for both signal excitation and reception. The data were analyzed in MEDx (Medical Numerics, VA, USA) and motion correction, normalization and spatial and temporal filtering [12] were applied prior to statistical analysis. A correlation analysis with a delayed boxcar convolved with a gamma-variate function was used to identify activated regions of interest. Voxels showing a
438
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
Fig. 3. An example 9-slice image volume acquired using the MS-ES-EPI sequence. Images were acquired with a matrix size of 6464 pixels, a slice thickness of 10 mm, in-plane resolution of 4 mm and an TE of 40 ms. Using an RF pulse separation of 27 ms, the whole volume was acquired in 243 ms.
pattern of activity associated with the motor action or visual stimulus were identified for further data processing to explore the timing of the haemodynamic responses.
simulations [24]. In displaying the results, both simulated and measured data values have been scaled by the maximum signal strength over the range of flip angles considered.
3. Results Fig. 3 shows a volume data set consisting of nine 10-mm-thick coronal slices (6464 matrix, 256256 mm2 field of view), which was acquired in 243 ms using the birdcage RF coil. These images display significant T2* contrast, reflecting the 40 ms TE. In addition, there is some signal dropout in frontal areas due to dephasing across the relatively large slice thickness. Fig. 4 shows the variation of signal intensity in grey and white matter areas with flip angle in MS-ES-EPI images acquired with a TR value of 243 ms. Experimental results are shown using open circles, while the theoretical data calculated using the expression S ðaÞ ¼ S0 sin a
1 eTR=T1 1 cos a eTR=T1
ð5Þ
are shown using continuous lines. T 1 values of 1330 and 830 ms were employed for grey and white matter in the
Fig. 4. Variation of the intensity of the signal generated by grey matter (blue) and white matter (red) as a function of flip angle when using the MS-ES-EPI sequence with TR=243 ms in a nine slice acquisition. The open circles show experimentally measured values, from regions of interest in grey and white matter, while the continuous lines show the values predicted using Eq. (4).
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
439
Fig. 5. An activation map from one subject performing the visual motor paradigm overlaid on a T2*-weighted high-speed MS PRESTO-EPI image. The Z score is shown in grey and white, using a corrected threshold for statistical significance of Pb.001.
Fig. 5A shows the corrected Z score ( P b.001) map from the correlation analysis overlaid on T2*-weighted MS-ESEPI image data produced from one subject who performed the visual-motor task. The sagittal images show the signal drop-off produced by the sensitivity profile of the surface coil used in this experiment. Areas of high correlation are apparent in visual and motor cortices. A similar pattern of activation was identified in the four other subjects studied. Fig. 6 shows the average response in visual cortex to visual stimuli of 1- and 5-s duration in one subject, without temporal smoothing. Sampling at 4.1 Hz yields more than 120 time points in each 30-s average time course. As expected, the response to the longer duration stimulus peaks later in time and is of higher peak amplitude. To compare the time courses of the activation produced in the motor cortex due to the button presses after visual cues of long and short duration, the pixels shown to be activated by the correlation analysis were selected for further processing. The raw time-series data were high-pass temporally smoothed to reduce the effect of baseline signal drift [12] and corrected for variable haemodynamic lags across pixels. The latter adjustment was performed by fitting a straight line to the rising edge of the average response elicited by the button press following the shorter visual cue (1-s duration) for
each pixel and then temporally shifting the whole pixel time course so that the intercept of the straight line fit with the baseline occurred at the same time point in all pixels [12]. The
Fig. 6. The haemodynamic response in active areas of visual cortex, averaged over the 15 presentations of the 1-s (blue line) and 5-s (red line) stimulus. The time course is the average of the pixels whose response produced an above threshold Z score in the first stage of statistical analysis.
440
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
Fig. 7. Representative time courses of the motor area activation shown in Fig. 5. (A) Part of the total time course for the motor activation clearly showing the response to four single button presses, the first and last being the response to a 1-s cue and the second and third being the response to the 5-s cue. (B) The average motor response to the 1-s visual cue, the error bars are F1 standard deviation over 15 cycles. The response in (C) is the motor response to the 5-s visual cue; the extended length of the cue has produced a detectable early anticipatory response in the motor region and delayed the button press by 4 s, relative to the response from the 1-s cue.
resulting time courses were then used in an independent component analysis (FastICA, Laboratory for Information and Computer Science, Helsinki University of Technology) over time to extract one time course associated with the motor action. Fig. 7 shows representative time courses from the same subject as shown in Fig. 5. Fig. 7A, shows a 120-s portion of the total time course, in which the responses to four button presses at 30-s intervals are evident. The first and fourth responses each follow a visual cue of 1-s duration, while the second and third follow cues of 5 s duration. The longer duration of the response to the 5-s cues that is apparent here, is more clearly seen in the averages of the 15 time courses measured following the short and long cues, which are shown in Fig. 7B and C. In both traces, time zero corresponds to the start of the visual cue. Before averaging the time courses across trials, the response to each button press was time-shifted to take account of the slight variation in the time of the button press relative to the end of the cue across trials. The standard deviation of these response times across subjects was about 80 ms. The different shape of the
haemodynamic response in the case of the longer visual cue makes it difficult to compare the timings of the responses to the different cues and seems to reflect the presence of an early anticipatory response [25] superimposed on the later response to the button press. To explore this hypothesis, the haemodynamic response elicited by the button press following the longer visual cue was modeled by convolving the response to the 1-s cue, with a unit impulse at time zero added to another varying strength impulse at a later time, nTR, where n is an integer. n was varied in from 1 to 50, corresponding to delays spanning the range 0.234–11.7 s. In all subjects, the best fit of the modeled response to the experimental data occurred when n = 17, yielding a delay of 3.978 s, which is the closest accessible value to the expected 4-s time difference. 4. Discussion The MS-ES-EPI sequence allows rapid acquisition of MS T2*-weighted echo planar images by using the principle of
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
echo shifting [18] to make the TE longer than the time between RF excitation of successive slices. The MS-ES-EPI sequence has been implemented at 3 T and used to acquire MS data sets made up of nine slices at a rate of more than 4 volumes per second. With this approach, the TR is significantly longer than the T2 relaxation time in brain tissue, and the images consequently show similar contrast to conventional MS EPIs acquired with short TR. This is in contrast to 3D-PRESTO imaging [18], in which volumar RF excitation is employed with very short TR values, such that TR b T 2, and steady-state transverse magnetization is formed, thus causing different contrast behavior. The presence of steady-state transverse magnetization not only leads to a higher SNR per unit time but also makes the signal more sensitive to motion and consequent temporal instability. The results shown in Fig. 4 indicate that the variation of signal intensity with flip angle in the MS-ES-EPI sequence follows that expected from simple saturation recovery in grey and white matter when using a TR of 243 ms. This implies that the magnetization from each slice is largely unaffected by the RF pulses applied to other slices and also that the dephasing of magnetization due to the unbalanced gradient pulses applied in the slice direction is high enough to adequately attenuate the signal during all inter-RF pulse periods except that into which the echo is shifted. In this work, we used gradient pulses that provided a minimum of 70 radians of phase dispersion across the slice. Following initial RF excitation, the dephased magnetization persists for one inter-RF pulse period before it is refocused, ready for image formation, consequently imparting some diffusion weighting to the signal. With a value of TR/N SL of 243 ms and k s =14 mm1 the b factor characterizing the diffusion weighting is approximately equal to 1.3 s mm2 and, so, will cause negligible attenuation of the tissue signal, although it will reduce the intravascular signal contribution [26]. The MS-ES-EPI sequence produced robust BOLD activation in all five subjects studied with the simple visual/motor task in which nine slices were acquired every 243 ms. This allows the nature of the BOLD response at a temporal sampling rate of 4.1 Hz. The resulting average BOLD response in the visual cortex to the 1-s visual stimulus peaks approximately 4 s earlier than that due to the longer 4-s stimulus and has a peak magnitude that is approximately half as large. The haemodynamic responses in motor cortex resulting from the subject’s button presses were significantly different in form, following the short and long duration visual cues, which made it difficult to compare fully the timings of the responses. The experimental work described here was carried out on a 3-T scanner equipped with only a single RF channel, so that it was not possible to apply parallel imaging in conjunction with MS-ES-EPI. Using parallel imaging would allow a reduction in the time required for each EPI acquisition, which could be translated into an increase in the number of slices acquired at fixed TR with a slight
441
reduction in the TE. Alternatively, it could be used with echo shifting over more than one inter-RF pulse spacing to produce a more significant increase in the number of sample slices or a reduction in the TR. Acknowledgment This work was supported by MRC grant G9900259. References [1] Dale AM, Buckner RL. Selective averaging of rapidly presented individual trials using fMRI. Hum Brain Mapp 1997;5:329 – 40. [2] Rosen BR, Buckner RL, Dale AM. Event-related functional MRI: past, present, and future. Proc Natl Acad Sci U S A 1998;95:773 – 80. [3] Dilharreguy B, Jones RA, Moonen CTW. Influence of fMRI data sampling on the temporal characterization of the hemodynamic response. Neuroimage 2003;19:1820 – 8. [4] Biswal B, De Yoe EA, Hyde JS. Reduction of physiological fluctuations in fMRI using digital filters. Magn Reson Med 1996; 35:107 – 13. [5] Glover GH, Li TQ, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 2000;44:162 – 7. [6] Hu XP, Le TH, Parrish T, Erhard P. Retrospective estimation and correction of physiological fluctuation in functional MRI. Magn Reson Med 1995;34:201 – 12. [7] Jiang AP, Kennedy DN, Baker JR, Weisskoff RM, Tootell RBH, Woods RP, et al. Motion detection and correction in functional MR imaging. Hum Brain Mapp 1995;3:224 – 35. [8] Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34:537 – 41. [9] Bhattacharyya PK, Lowe MJ. Cardiac-induced physiologic noise in tissue is a direct observation of cardiac-induced fluctuations. Magn Reson Imaging 2004;22:9 – 13. [10] Lund TE. fcMRI — mapping functional connectivity or correlating cardiac-induced noise? Magn Reson Med 2001;46:628. [11] Kruger G, Glover GH. Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magn Reson Med 2001;46:631 – 7. [12] Gibson AM, Brookes MJ, Kim SS, Francis ST, Morris PG. A new quantitative analysis of significant timing differences between externally cued and self-initiated motor tasks in an fMRI study. Solid State Nucl Magn Reson 2005;28(2 – 4):258 – 65. [13] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952 – 62. [14] Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38:591 – 603. [15] Mansfield P, Howseman AM, Ordidge RJ. Volumar imaging using NMR spin echoes — echo-volumar imaging (EVI) at 0.1-T. J Phys [E] 1989;22:324 – 30. [16] Mansfield P, Coxon R, Hykin J. Echo-volumar imaging (EVI) of the brain at 3.0 T — first normal volunteer and functional imaging results. J Comput Assist Tomogr 1995;19:847 – 52. [17] van der Zwaag W, Francis ST, Bowtell RW. Proceedings of the 12th Annual Meeting of the ISMRM, Kyoto. 2004. p. 1005. [18] Liu GY, Sobering G, Duyn J, Moonen CTW. A functional MRI technique combining principles of echo-shifting with a train of observations (PRESTO). Magn Reson Med 1993;30:764 – 8. [19] Liu GY, Sobering G, Olson AW, Vangelderen P, Moonen CTW. Fast echo-shifted gradient-recalled MRI — combining a short repetition time with variable T(2)asterisk weighting. Magn Reson Med 1993; 30:68 – 75.
442
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
[20] Klarhofer M, Dilharreguy B, van Gelderen P, Moonen CTW. A PRESTO-SENSE sequence with alternating partial-Fourier encoding for rapid susceptibility — weighted 3D MRI time series. Magn Reson Med 2003;50:830 – 8. [21] Ramsey NF, van den Brink JS, van Muiswinkel AMC, Folkers PJM, Moonen CTW, Jansma JM, et al. Phase navigator correction in 3D fMRI improves detection of brain activation: Quantitative assessment with a graded motor activation procedure. Neuroimage 1998;8:240 – 8. [22] Vangelderen P, Ramsey NF, Liu GY, Duyn JH, Frank JA, Weinberger DR, et al. 3-dimensional functional magnetic-resonance-imaging of human brain on a clinical 1.5-T scanner. Proc Natl Acad Sci U S A 1995;92:6906 – 10.
[23] Bowtell R, Peters A. Analytic approach to the design of transverse gradient coils with co-axial return paths. Magn Reson Med 1999;41:600 – 8. [24] Wansapura JP, Holland SK, Dunn RS, Ball WS. NMR relaxation times in the human brain at 3.0 tesla. J Magn Reson Imaging 1999;9:531 – 8. [25] Kim SG, Richter W, Ugurbil K. Limitations of temporal resolution in functional MRI. Magn Reson Med 1997;37:631 – 6. [26] Henkelman RM, Neil JJ, Xiang QS. A quantitative interpretation of IVIM measurements of vascular perfusion in the rat-brain. Magn Reson Med 1994;32:464 – 9.
Echo-shifted multislice EPI for high-speed fMRI Andrew Gibson, Andrew M. Peters, Richard Bowtell4 Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, NG7 2RD Nottingham, UK Received 2 December 2005; accepted 2 December 2005
Abstract The advantages of event-related functional Magnetic Resonance Imaging (fMRI) and the increasing use of fMRI in cognitive experiments are both driving the development of techniques that allow images sensitive to the blood oxygen level-dependent effect to be acquired at everhigher temporal resolution. Here, we present a technique based on the use of echo shifting (ES) in conjunction with a multislice (MS) echo planar imaging (EPI) readout, which allows T2*-weighted images to be generated with a repetition time per slice that is less than the echo time (TE). Using this ES-MS-EPI approach, it is shown that images with a TE of 40 ms can be acquired with an acquisition time per slice of only 27 ms. The utility of the MS-ES-EPI sequence is demonstrated in a visual-motor, event-related fMRI study in which nine-slice image volumes are acquired continuously at a rate of 4.1 Hz. The sequence is shown to produce reliable activation associated with both visual stimuli and motor actions. D 2006 Elsevier Inc. All rights reserved. Keywords: fMRI; High-speed; Echo shifting; Temporal resolution
1. Introduction Functional magnetic resonance imaging (fMRI) has proven to be a very powerful tool in the investigation of human brain function. The rate of image acquisition is an important parameter in the design of any fMRI experiment, since changing the temporal resolution can affect many characteristics of the acquired data. Increasing the temporal resolution with which fMRI data are acquired has a number of advantages. First, the resulting improvement in the sampling and, hence, characterization of the haemodynamic response increases the potential analytical power of the technique and also reduces problems in analysis caused when spatially separate regions of the brain are sampled at different times. This is particularly advantageous in event-related fMRI in which short interstimulus intervals [1,2] are used or in which small differences in the timing of activation across trials need to be assessed [3]. Second, increasing the rate of acquisition of images in fMRI experiments via reduction of the repetition time (TR) between successive radiofrequency (RF) excitations can increase the efficiency with which activation is detected. The signal-to-noise ratio (SNR) of a
4 Corresponding author. Tel.: +44 115 9514737; fax: +44 115 9515166. E-mail address: [email protected] (R. Bowtell). 0730-725X/$ – see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.mri.2005.12.030
voxel time course in an fMRI experiment depends upon the volumar TR of the experiment. Reducing TR leads to a decrease in SNR in each image due to saturation; however, the number of images acquired per unit time increases, and with an optimal choice of flip angle, this leads to an overall increase in image SNR per unit time. This is favorable for fMRI experiments where temporal filtering is used to average signals over time to give a smoothed, reduced-noise representation of the original signal. As the filter shape is usually designed to match the form of the haemodynamic response, any increase in SNR per unit time in the data will lead to an increase in the blood oxygen level-dependent (BOLD) contrast-to-noise ratio of the temporally filtered data. Thus, as the SNR of the filtered data increases with increased sampling rate, the efficiency of detection will increase, assuming all other factors are held constant. Increasing the sampling rate in an fMRI study also has the advantage of allowing for the possible detection and subsequent removal of physiological noise due to cardiac and respiratory effects [4– 6]. For example, if the physiological noise is critically sampled, standard temporal filtering techniques can be used to attenuate frequencies at which physiological noise occurs. With volumar TRs of 2 to 3 s, as are commonly employed in fMRI experiments, fluctuations linked to the cardiac cycle are undersampled. Consequently, their contributions to signal variation are aliased to lower frequencies, making them
434
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
difficult to eliminate by simple low-pass filtering. Third, increasing the volumar acquisition rate potentially allows more accurate correction of subject motion, as movement occurring during the acquisition of an individual image volume will be reduced. When three-dimensional data are acquired via phase encoding across multiple excitations, movement during one volumar acquisition leads to image blurring and formation of artifacts that cannot be corrected by simple motion correction algorithms. In the case of multislice (MS) fMRI data, movement during the acquisition of an MS set means that different slices are acquired with the head at different positions so that the resulting data does not truly conform to the model of rigid body motion often assumed in motion correction algorithms [7]. Improvement in the accuracy with which the haemodynamic response can be characterized as the volumar sampling rate is increased has been demonstrated previously via simulation and experiment by Dilharreguy et al. [3]. In particular, their work showed a decrease in the accuracy of determination of the time at which the haemodynamic response peaks by approximately 50 ms for each second by which TR is increased in MS echo planar experiments for TRs in the range of 0.5–2.5 s. The improved characterization of cardiac fluctuations via a reduction in TR has been particularly explored in studies of functional connectivity [8], where it is important to sample cardiac fluctuations adequately [9,10], so that their contribution to covariation of signal in apparently connected spatial regions can be eliminated. 1.1. Improved efficiency of detection of BOLD responses In order to illustrate the potential for improved efficiency of detection of BOLD responses through reduction of the volumar TR, fMRI data sampled at a range of TR values was simulated and then subjected to a conventional statistical analysis. In generating the simulated data, it was assumed that T 2 b TR so that steady-state transverse magnetization was not formed, and it was also assumed that the Ernst angle was employed for RF excitation, so that the signal strength was given by sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 eT R=T1 S ð T R Þ ¼ S0 ð1Þ 1 þ eT R=T1
The event-related haemodynamic response to a stimulus was modeled using a typical gamma-variate function [12], giving a peak signal change of 2%. The simulated data represented 10 responses to stimuli separated by a 15-s interstimulus time, giving a total time series duration of 2.5 min. A random delay (in the range of F2.5 s) was introduced at the start of the time series to model the effects of slice ordering and variable haemodynamic delays. The simulated response was then sampled using different TRs varying from 0.2–5 s, and the level of white noise predicted by Eq. (2) was added. The resulting time series were then temporally filtered with a 2.8-s-width Gaussian filter, as might be used in the analysis of event-related fMRI data. A cross-correlation statistic was subsequently produced using the response prior to addition of noise as the reference. The correlation coefficient was then converted to a Z score, allowing for the correct number of degrees of freedom of the data. In order to investigate the variability of the efficiency of detection, the simulation was repeated 100 times at each TR with different random noise and delays. The results of the simulation are shown in Fig. 1, where the error bars represent F1 standard deviation of the averaged Z score at each value of TR. The simulation shows that efficiency (Z score) increases with decreasing TR, as predicted from simple noise analysis. The variability in the Z score also decreases with decreasing TR. This is important as the efficiency of detection of a response ideally should not depend strongly on the slice timing. The simulation shows that for increased efficiency with decreased variability, it is best to scan with as small a TR as possible. The theory used in the simulation is simplistic on several levels. The noise in the haemodynamic response is assumed to be white, irrespective of its source. It is clear that physiologically induced noise will not be white in nature and will have components that fall into definite frequency bands. If this noise is critically sampled at short TRs, it would mean that it would have more spectral power in the
where S 0 is the signal that would be measured at the same echo time (TE) using a 908 pulse with TR NNT 1. The noise, r, in the fMRI signal can be modeled using the theory of Kruger and Glover [11] as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ¼ r20 þ ½S ðTRÞ2 k2 ð2Þ where r 0 is the intrinsic thermal and scanner noise and the physiological noise is given by the product of k and S(TR). k is an TE-dependent constant, which characterizes the sum of the BOLD and non-BOLD physiological noise contributions [11].
Fig. 1. Results of a Monte Carlo simulation, demonstrating the increased efficiency (Z score) of detection with decreasing volumar TR, for a simulated train of haemodynamic responses. The simulation also shows that at lower volumar TRs, the variability in the efficiency (Z score) decreases.
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
stop band of the filter than assumed in the model, which would be advantageous. Physiological noise resulting from haemodynamic fluctuations is likely to have a correlation time of a few seconds as a result of the time scale of the haemodynamic response. It will consequently survive the filtering process, and the gain in efficiency achieved by going to short sampling times will therefore be reduced. 1.2. Sequences for rapid acquisition of T2*-weighted volumar image data The vast majority of fMRI experiments that are currently carried out employ an MS, echo planar imaging (EPI) sequence, as shown in Fig. 2A for image acquisition. Such sequences typically employ a TR of a few seconds, which is needed to acquire data from multiple (N 10) slices, since each slice acquisition requires a time of the order of 100 ms. Analysis of Fig. 2A shows that the minimum volumar TR is in fact given by TRcNSL ðTRF =2 þ TE þ TEPI =2Þ
ð3Þ
where N SL is the number of slices sampled, T RF is the duration of the RF pulse used for selective excitation and T EPI is the duration of the echo train generated by the switched gradient of the EPI sequence. Assuming a TE of 36 ms, as is commonly employed in fMRI at 3 T and use of an EPI sequence involving sampling of 64 echoes with a 1-kHz gradient switching frequency, and an RF pulse duration of 4 ms, gives a time per slice of about 70 ms. At 1.5 T where T2* and, hence, the optimal TE for fMRI is longer, a greater time per slice is required. Inspection of Eq. (3) shows that for a fixed number of slices, the most significant reduction of TR can be achieved by decreasing TE. However, this is not normally an acceptable approach since it leads to a reduction in the BOLD contrast. This leaves the alternative of shortening T RF or T EPI. Since T RF is generally much shorter than TE and T EPI, reducing its value has little effect on TR whilst leading to significant increases in the required RF power. Reduction of T EPI is more productive and can be achieved by increasing both the gradient switching rate and gradient strength and/or employing parallel imaging [13,14] so as to reduce the number of echoes acquired. Both strategies can, however, yield only limited reductions in TR, the former approach being restricted by the performance of gradient hardware and the need to avoid peripheral nerve stimulation, whilst use of too high speed-up factors in parallel imaging causes significant spatially varying noise enhancement [13]. Revisiting the example mentioned above, a two-fold reduction in the number of k-space lines sampled and an increase in the gradient switching frequency by a factor of 1.5 would yield a minimum TR of about 50 ms per slice, at the cost of a 1.7-fold reduction in SNR due to the shortening of the echo-train from 32- to 11-ms duration. The echo volumar imaging (EVI) technique [15] offers an alternative approach to the rapid generation of threedimensional, T2*-weighted image data. EVI generates three
435
dimensional images using a single RF excitation, followed by application of a periodic, switched gradient waveform and two orthogonal blipped gradients (applied as the switched gradient reverses sign). The switched gradient generates a train of gradient echoes that are phase-encoded in two orthogonal directions by the blipped gradients. Unfortunately, generating large image matrices with EVI necessitates the use of long echo trains, leading to a high sensitivity to image distortion due to magnetic field inhomogeneity [16]. Consequently, at high field, EVI is not well suited to use in fMRI studies where whole-brain coverage is required in conjunction with reasonable spatial resolution. EVI does, however, offer significant potential for the study of small cortical regions with high spatial and temporal resolution [17]. Principles of echo-shifting with a train of observations (PRESTO) imaging [18,19] has also been used for rapid acquisition of three-dimensional fMRI data [20 –22]. PRESTO employs multiple RF excitations of the volume, in conjunction with echo shifting (ES), so as to achieve a TE that is greater than the inter-RF pulse spacing, TR. A partial EPI readout is generally carried out after each excitation, and in three-dimensional mode, a varying phase-encoding gradient is applied in the third dimension immediately after RF excitation. In a recent implementation of 3D-PRESTO in combination with partial Fourier encoding and parallel imaging, Klarhffer et al. [20] were able to generate T2*-weighted images (with 444-mm3 voxels) covering the whole brain, with an acquisition time of 0.5 s per volume. Implementation of 3D-PRESTO involves the use of short TRs (e.g., TR = 29 ms), leading to the formation of steadystate transverse magnetization, which causes a more complicated signal dependence on TR, TE, T1, T2 and T2* than obtained in MS EPI. In addition, the persistence of highly dephased magnetization over several TR periods leads to sensitivity to motion and consequent temporal instability. This is often ameliorated via the use navigator echoes [21]. Here, we present a technique based on the use of ES in conjunction with MS EPI. This allows images in an MS set to be generated with a TR per slice that is less than the TE, thus speeding up the volumar data acquisition rate compared with conventional MS EPI, without altering image contrast. This MS-ES-EPI sequence has been implemented at 3 T and used to generate images with a TE of 40 ms and an acquisition time per slice of only 27 ms. The utility of the MS-ES-EPI sequence is demonstrated in a visual-motor, event-related fMRI study in which a nine-slice volume is acquired continuously at a volumar data rate of 4.1 Hz. The sequence is shown reliably to detect activation associated with both visual and motor stimuli.
2. Method 2.1. The MS-ES-EPI sequence Fig. 2B shows the timing diagram describing the MSES-EPI sequence. Successive slices are sequentially excited,
436
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
Fig. 2. (A) Conventional MS EPI sequence. The relative areas under the gradient pulses applied along the slice direction are shown numerically, and the number of the slice excited by each RF pulse is also indicated. (B) MS-ES-EPI sequence. The signal excited from slice 1 by the first RF pulse is refocused in the period following the second RF pulse, so that TE N TR/N slice. The gradient pulses on the slice axis that are shown using dashed lines can be added to provide greater dephasing of the signal from any slice during periods in which it is not being measured.
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
but the negative polarity-refocusing gradient pulse that usually follows immediately after slice selection is omitted, and a negative gradient lobe, the area of which is 1.5 times that of the slice-select gradient, is added at a time immediately preceding the application of the selective RF pulse. This combination of gradient pulses acts to shift the gradient echo of the signal formed due to each RF excitation by a time equal to the RF pulse separation [18]. Consequently, the signal from the nth slice is measured in the interval following the RF pulse that excites the n+1th slice, so that the TE is greater than the RF pulse spacing. The EPI switched (read) and blipped (phase-encoding) gradients are played out in the period between RF pulses, and both gradient waveforms are adjusted to have zero total area in each inter-RF-pulse interval, so that no phase is accumulated between successive RF pulse applications. Inspection of Fig. 2B, shows that the minimum time needed to acquire a MS data set using the MS-ES-EPI sequence is reduced to TR ¼ NSL ðTRFþ TEPI Þ
ð4Þ
whilst the minimum TE is equal to 1.5 (T RF+T EPI). Considering, once again, an EPI sequence involving sampling of 64 echoes with a 1-kHz gradient switching frequency and an RF pulse duration of 4 ms, the MS-ES-EPI sequence gives an acquisition time per slice of about 36 ms, whilst the minimum TE is 54 ms. The form of the gradient waveform applied in the slice direction means that the transverse magnetization generated by excitation of a particular slice by the nthik RF pulse is s ðm1Þs modulated by a phase factor of the form e 2 in the period following the n+mth RF pulse, where s is the spatial coordinate in the slice direction and k s =cG sT RF. Consequently, the phase dispersion across a slice of thickness, D s, is k s (m1) D s/2, taking a value of zero only when m = 1, and for other values having a minimum phase dispersion of k s D s/2. This dispersion needs to be large enough to crush the signal to very low levels in all interpulse periods except when m =1. If a larger phase dispersion is required to achieve an adequate level of signal attenuation, this can be achieved by adding gradient lobes at the beginning and end of the interpulse period, the areas of which are in the ratio 1:2, as shown using dashed lines in Fig. 2B. ES over more than one interpulse period can be achieved by varying the ratio of the gradient pulse areas, as introduced by Liu et al. [18] in their original description of the PRESTO technique. 2.2. Implementation at 3 T The MS-ES-EPI sequence was implemented on a 3-T imaging system that was previously constructed inhouse and equipped with an insert head gradient coil [23]. This system has only one RF channel for reception, and so, implementation of parallel imaging in conjunction with the
437
MS-ES-EPI sequence was not possible. A sinusoidal, switched gradient waveform of 1.9-kHz frequency was generated using a simple resonant circuit. Using a selective RF pulse of 1.5-ms duration, images with a matrix size of 6464 pixels were acquired with an in-plane resolution of 4 mm and a slice thickness of 10 mm. For this sequence, 64 echoes were acquired in a time of 17 ms. An extra 3 ms was required at the beginning of the echo train for the k-space pre-excursion in the blipped gradient direction and application of a dephasing gradient pulse in the slice direction. A time of 5 ms was needed at the end of the echo train for application of a rephasing gradient lobe in the phaseencoding direction and a dephasing gradient pulse in the slice direction. This gave an acquisition time per slice of 27 ms, and in these experiments, each volume consisted of nine slices, giving a volumar acquisition rate of 4.1 Hz. The minimum possible TE of 40 ms was employed, which is close to the optimum value for generating BOLD contrast at 3 T. The slice gradient waveform applied gave a value of k s of 14 mm1 so that the minimum phase dispersion of unwanted coherences across the slice thickness was 70 radians. Initial experiments were carried out using a transmit/ receive birdcage RF coil and employed a coronal slice orientation. In a series of 21 experiments, the flip angle of the RF excitation pulse was varied between 08 and 1368, and the image intensity in regions of grey and white matter was measured and compared with the predicted signal strength. In each experiment, 220 MS data sets were acquired in 54 s, and the data from the first 100 volumes was discarded to ensure that the signal had come to steady state. 2.3. Visual/motor fMRI studies The MS-ES-EPI technique was then applied in an fMRI study employing a simple, visually cued motor task. Five healthy volunteers were presented with a series of visual stimuli in the form of a flashing checkerboard at 29.16-s intervals. The duration of each stimulus was either 5 or 1 s. The subjects were instructed to press a button with the thumb of the right hand at the end of each visual cue, and the time of the button press was recorded. Fifteen cycles of each condition were presented, giving a total imaging time of 14 min and 35 s; during this time, a total of 3600 volumes of data were collected. Each volume consisting of 9, sagittal slices spanning the left hemisphere of the brain (4410-mm3 voxel dimensions) was acquired in 243 ms, with TE =40 ms. A surface RF coil of 14-cm diameter was positioned on the right side of the head and used for both signal excitation and reception. The data were analyzed in MEDx (Medical Numerics, VA, USA) and motion correction, normalization and spatial and temporal filtering [12] were applied prior to statistical analysis. A correlation analysis with a delayed boxcar convolved with a gamma-variate function was used to identify activated regions of interest. Voxels showing a
438
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
Fig. 3. An example 9-slice image volume acquired using the MS-ES-EPI sequence. Images were acquired with a matrix size of 6464 pixels, a slice thickness of 10 mm, in-plane resolution of 4 mm and an TE of 40 ms. Using an RF pulse separation of 27 ms, the whole volume was acquired in 243 ms.
pattern of activity associated with the motor action or visual stimulus were identified for further data processing to explore the timing of the haemodynamic responses.
simulations [24]. In displaying the results, both simulated and measured data values have been scaled by the maximum signal strength over the range of flip angles considered.
3. Results Fig. 3 shows a volume data set consisting of nine 10-mm-thick coronal slices (6464 matrix, 256256 mm2 field of view), which was acquired in 243 ms using the birdcage RF coil. These images display significant T2* contrast, reflecting the 40 ms TE. In addition, there is some signal dropout in frontal areas due to dephasing across the relatively large slice thickness. Fig. 4 shows the variation of signal intensity in grey and white matter areas with flip angle in MS-ES-EPI images acquired with a TR value of 243 ms. Experimental results are shown using open circles, while the theoretical data calculated using the expression S ðaÞ ¼ S0 sin a
1 eTR=T1 1 cos a eTR=T1
ð5Þ
are shown using continuous lines. T 1 values of 1330 and 830 ms were employed for grey and white matter in the
Fig. 4. Variation of the intensity of the signal generated by grey matter (blue) and white matter (red) as a function of flip angle when using the MS-ES-EPI sequence with TR=243 ms in a nine slice acquisition. The open circles show experimentally measured values, from regions of interest in grey and white matter, while the continuous lines show the values predicted using Eq. (4).
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
439
Fig. 5. An activation map from one subject performing the visual motor paradigm overlaid on a T2*-weighted high-speed MS PRESTO-EPI image. The Z score is shown in grey and white, using a corrected threshold for statistical significance of Pb.001.
Fig. 5A shows the corrected Z score ( P b.001) map from the correlation analysis overlaid on T2*-weighted MS-ESEPI image data produced from one subject who performed the visual-motor task. The sagittal images show the signal drop-off produced by the sensitivity profile of the surface coil used in this experiment. Areas of high correlation are apparent in visual and motor cortices. A similar pattern of activation was identified in the four other subjects studied. Fig. 6 shows the average response in visual cortex to visual stimuli of 1- and 5-s duration in one subject, without temporal smoothing. Sampling at 4.1 Hz yields more than 120 time points in each 30-s average time course. As expected, the response to the longer duration stimulus peaks later in time and is of higher peak amplitude. To compare the time courses of the activation produced in the motor cortex due to the button presses after visual cues of long and short duration, the pixels shown to be activated by the correlation analysis were selected for further processing. The raw time-series data were high-pass temporally smoothed to reduce the effect of baseline signal drift [12] and corrected for variable haemodynamic lags across pixels. The latter adjustment was performed by fitting a straight line to the rising edge of the average response elicited by the button press following the shorter visual cue (1-s duration) for
each pixel and then temporally shifting the whole pixel time course so that the intercept of the straight line fit with the baseline occurred at the same time point in all pixels [12]. The
Fig. 6. The haemodynamic response in active areas of visual cortex, averaged over the 15 presentations of the 1-s (blue line) and 5-s (red line) stimulus. The time course is the average of the pixels whose response produced an above threshold Z score in the first stage of statistical analysis.
440
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
Fig. 7. Representative time courses of the motor area activation shown in Fig. 5. (A) Part of the total time course for the motor activation clearly showing the response to four single button presses, the first and last being the response to a 1-s cue and the second and third being the response to the 5-s cue. (B) The average motor response to the 1-s visual cue, the error bars are F1 standard deviation over 15 cycles. The response in (C) is the motor response to the 5-s visual cue; the extended length of the cue has produced a detectable early anticipatory response in the motor region and delayed the button press by 4 s, relative to the response from the 1-s cue.
resulting time courses were then used in an independent component analysis (FastICA, Laboratory for Information and Computer Science, Helsinki University of Technology) over time to extract one time course associated with the motor action. Fig. 7 shows representative time courses from the same subject as shown in Fig. 5. Fig. 7A, shows a 120-s portion of the total time course, in which the responses to four button presses at 30-s intervals are evident. The first and fourth responses each follow a visual cue of 1-s duration, while the second and third follow cues of 5 s duration. The longer duration of the response to the 5-s cues that is apparent here, is more clearly seen in the averages of the 15 time courses measured following the short and long cues, which are shown in Fig. 7B and C. In both traces, time zero corresponds to the start of the visual cue. Before averaging the time courses across trials, the response to each button press was time-shifted to take account of the slight variation in the time of the button press relative to the end of the cue across trials. The standard deviation of these response times across subjects was about 80 ms. The different shape of the
haemodynamic response in the case of the longer visual cue makes it difficult to compare the timings of the responses to the different cues and seems to reflect the presence of an early anticipatory response [25] superimposed on the later response to the button press. To explore this hypothesis, the haemodynamic response elicited by the button press following the longer visual cue was modeled by convolving the response to the 1-s cue, with a unit impulse at time zero added to another varying strength impulse at a later time, nTR, where n is an integer. n was varied in from 1 to 50, corresponding to delays spanning the range 0.234–11.7 s. In all subjects, the best fit of the modeled response to the experimental data occurred when n = 17, yielding a delay of 3.978 s, which is the closest accessible value to the expected 4-s time difference. 4. Discussion The MS-ES-EPI sequence allows rapid acquisition of MS T2*-weighted echo planar images by using the principle of
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
echo shifting [18] to make the TE longer than the time between RF excitation of successive slices. The MS-ES-EPI sequence has been implemented at 3 T and used to acquire MS data sets made up of nine slices at a rate of more than 4 volumes per second. With this approach, the TR is significantly longer than the T2 relaxation time in brain tissue, and the images consequently show similar contrast to conventional MS EPIs acquired with short TR. This is in contrast to 3D-PRESTO imaging [18], in which volumar RF excitation is employed with very short TR values, such that TR b T 2, and steady-state transverse magnetization is formed, thus causing different contrast behavior. The presence of steady-state transverse magnetization not only leads to a higher SNR per unit time but also makes the signal more sensitive to motion and consequent temporal instability. The results shown in Fig. 4 indicate that the variation of signal intensity with flip angle in the MS-ES-EPI sequence follows that expected from simple saturation recovery in grey and white matter when using a TR of 243 ms. This implies that the magnetization from each slice is largely unaffected by the RF pulses applied to other slices and also that the dephasing of magnetization due to the unbalanced gradient pulses applied in the slice direction is high enough to adequately attenuate the signal during all inter-RF pulse periods except that into which the echo is shifted. In this work, we used gradient pulses that provided a minimum of 70 radians of phase dispersion across the slice. Following initial RF excitation, the dephased magnetization persists for one inter-RF pulse period before it is refocused, ready for image formation, consequently imparting some diffusion weighting to the signal. With a value of TR/N SL of 243 ms and k s =14 mm1 the b factor characterizing the diffusion weighting is approximately equal to 1.3 s mm2 and, so, will cause negligible attenuation of the tissue signal, although it will reduce the intravascular signal contribution [26]. The MS-ES-EPI sequence produced robust BOLD activation in all five subjects studied with the simple visual/motor task in which nine slices were acquired every 243 ms. This allows the nature of the BOLD response at a temporal sampling rate of 4.1 Hz. The resulting average BOLD response in the visual cortex to the 1-s visual stimulus peaks approximately 4 s earlier than that due to the longer 4-s stimulus and has a peak magnitude that is approximately half as large. The haemodynamic responses in motor cortex resulting from the subject’s button presses were significantly different in form, following the short and long duration visual cues, which made it difficult to compare fully the timings of the responses. The experimental work described here was carried out on a 3-T scanner equipped with only a single RF channel, so that it was not possible to apply parallel imaging in conjunction with MS-ES-EPI. Using parallel imaging would allow a reduction in the time required for each EPI acquisition, which could be translated into an increase in the number of slices acquired at fixed TR with a slight
441
reduction in the TE. Alternatively, it could be used with echo shifting over more than one inter-RF pulse spacing to produce a more significant increase in the number of sample slices or a reduction in the TR. Acknowledgment This work was supported by MRC grant G9900259. References [1] Dale AM, Buckner RL. Selective averaging of rapidly presented individual trials using fMRI. Hum Brain Mapp 1997;5:329 – 40. [2] Rosen BR, Buckner RL, Dale AM. Event-related functional MRI: past, present, and future. Proc Natl Acad Sci U S A 1998;95:773 – 80. [3] Dilharreguy B, Jones RA, Moonen CTW. Influence of fMRI data sampling on the temporal characterization of the hemodynamic response. Neuroimage 2003;19:1820 – 8. [4] Biswal B, De Yoe EA, Hyde JS. Reduction of physiological fluctuations in fMRI using digital filters. Magn Reson Med 1996; 35:107 – 13. [5] Glover GH, Li TQ, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 2000;44:162 – 7. [6] Hu XP, Le TH, Parrish T, Erhard P. Retrospective estimation and correction of physiological fluctuation in functional MRI. Magn Reson Med 1995;34:201 – 12. [7] Jiang AP, Kennedy DN, Baker JR, Weisskoff RM, Tootell RBH, Woods RP, et al. Motion detection and correction in functional MR imaging. Hum Brain Mapp 1995;3:224 – 35. [8] Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34:537 – 41. [9] Bhattacharyya PK, Lowe MJ. Cardiac-induced physiologic noise in tissue is a direct observation of cardiac-induced fluctuations. Magn Reson Imaging 2004;22:9 – 13. [10] Lund TE. fcMRI — mapping functional connectivity or correlating cardiac-induced noise? Magn Reson Med 2001;46:628. [11] Kruger G, Glover GH. Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magn Reson Med 2001;46:631 – 7. [12] Gibson AM, Brookes MJ, Kim SS, Francis ST, Morris PG. A new quantitative analysis of significant timing differences between externally cued and self-initiated motor tasks in an fMRI study. Solid State Nucl Magn Reson 2005;28(2 – 4):258 – 65. [13] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952 – 62. [14] Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38:591 – 603. [15] Mansfield P, Howseman AM, Ordidge RJ. Volumar imaging using NMR spin echoes — echo-volumar imaging (EVI) at 0.1-T. J Phys [E] 1989;22:324 – 30. [16] Mansfield P, Coxon R, Hykin J. Echo-volumar imaging (EVI) of the brain at 3.0 T — first normal volunteer and functional imaging results. J Comput Assist Tomogr 1995;19:847 – 52. [17] van der Zwaag W, Francis ST, Bowtell RW. Proceedings of the 12th Annual Meeting of the ISMRM, Kyoto. 2004. p. 1005. [18] Liu GY, Sobering G, Duyn J, Moonen CTW. A functional MRI technique combining principles of echo-shifting with a train of observations (PRESTO). Magn Reson Med 1993;30:764 – 8. [19] Liu GY, Sobering G, Olson AW, Vangelderen P, Moonen CTW. Fast echo-shifted gradient-recalled MRI — combining a short repetition time with variable T(2)asterisk weighting. Magn Reson Med 1993; 30:68 – 75.
442
A. Gibson et al. / Magnetic Resonance Imaging 24 (2006) 433 – 442
[20] Klarhofer M, Dilharreguy B, van Gelderen P, Moonen CTW. A PRESTO-SENSE sequence with alternating partial-Fourier encoding for rapid susceptibility — weighted 3D MRI time series. Magn Reson Med 2003;50:830 – 8. [21] Ramsey NF, van den Brink JS, van Muiswinkel AMC, Folkers PJM, Moonen CTW, Jansma JM, et al. Phase navigator correction in 3D fMRI improves detection of brain activation: Quantitative assessment with a graded motor activation procedure. Neuroimage 1998;8:240 – 8. [22] Vangelderen P, Ramsey NF, Liu GY, Duyn JH, Frank JA, Weinberger DR, et al. 3-dimensional functional magnetic-resonance-imaging of human brain on a clinical 1.5-T scanner. Proc Natl Acad Sci U S A 1995;92:6906 – 10.
[23] Bowtell R, Peters A. Analytic approach to the design of transverse gradient coils with co-axial return paths. Magn Reson Med 1999;41:600 – 8. [24] Wansapura JP, Holland SK, Dunn RS, Ball WS. NMR relaxation times in the human brain at 3.0 tesla. J Magn Reson Imaging 1999;9:531 – 8. [25] Kim SG, Richter W, Ugurbil K. Limitations of temporal resolution in functional MRI. Magn Reson Med 1997;37:631 – 6. [26] Henkelman RM, Neil JJ, Xiang QS. A quantitative interpretation of IVIM measurements of vascular perfusion in the rat-brain. Magn Reson Med 1994;32:464 – 9.