In this study, performance of the blipped-CAIPI method in 3× simultaneous multi-slice diffusion imaging was assessed using a pseudo-multiple replica SNR measure. In addition, reproducibility of the Q-ball ODFs was assessed via bootstrapping metrics. DSI tractography was qualitatively assessed and the average FA and volume of the major white matter pathways were compared. Through these quantitative and qualitative assessments, we show that the data acquisition times for Q-ball and DSI can be reduced 3-fold using simultaneous multislice with small loss in SNR or diffusion information compared to conventional acquisitions, thereby providing large gains in sensitivity per unit time.
The bootstrap metrics performance was found to be sensitive and repeatable. For example, the measure of Q-ball ODF reproducibility (JSD) was sensitive to minor SNR differences in the raw data. This is illustrated via the results in the Q-ball measures of . These data contained minor differences in SNR level between the Rsl×Rinplane=1×1 acquisition acquired with TR=11.8 s and again at TR=3.8 s. The expected SNR difference due to the TR differences for white matter (T1~850 ms) was around ~1%. Some additional SNR differences can also result from imperfect slice profiles and slice interleaving. For TR=3.8 s, the saturation-recovery factor of the unintentionally crushed signal due to imperfect excitation of spatially adjacent slice (excited TR/2 prior) was ~11% (e-(TR/2)/T1). Overall, the expected SNR loss was small. Nonetheless, this small SNR loss still translated to visible differences in the Q-ball bootstrap metrics. In addition, the bootstrap metrics were shown to be repeatable in three independent bootstrap datasets with identical acquisition parameters (). Here we note that the angular confidence interval is more robust for the 1st (CI 1) than for the 2nd maximum (CI 2), and that JSD shows good reproducibility across the three bootstrap datasets. The reduction in robustness of the 2nd maxima is likely to be from the lower SNR of this metric.
The bootstrap results of the slice-accelerated acquisitions agree well with the predicted SNR loss from the pseudo-multiple replica simulation and the expected time averaging SNR gain from averaging across multiple repetitions. This is highlighted in (Q-ball acquisition with Rsl×Rinplane=3×2 acceleration). Based on the pseudo-multiple replica simulation, the average reduction in SNR for the Rsl×Rinplane=3×2 accelerated acquisition was 0.85 (for the center slice). This SNR loss could, in principle, be compensated by averaging together 1.38 fold more data, as supported by . The performance level of the bootstrap metrics of the non-slice accelerated acquisition is between the performance of the 1-average and 2-average of the 3× slice accelerated acquisition. Thus, averaging together two Rsl×Rinplane=3×2 accelerated acquisitions should (and does) overcompensate for the SNR reduction of the accelerated acquisition. Thus the blipped-CAIPI simultaneous multi-slice method achieves a 3-fold acquisition time boost with an SNR cost equivalent of 1.38 acquisitions, and thus could re-create the data of the non-multi-slice acquisition with equal SNR with a time savings of 3/1.38 = 2.2 fold. We note that the g-factor related SNR reduction in Rsl×Rinplane=3×2 accelerated acquisitions can be overcome with the use of a 64-channel head array coil.
In this work, the SNR per unit time gain in the blipped-CAIPI acquisition was illustrated by assessing the uncertainty metrics of the 2 and 3 averages of the 3× slice accelerated acquisition. The work by Jones (2004)
suggests that with more scan time, a larger gain in diffusion metrics robustness may be achieved by sampling more diffusion directions rather than using the extra time to acquire more averages of the original diffusion directions. Therefore, the acquisition time gained from the blipped-CAIPI sequence might be best used to acquire more diffusion directions.
The novel modified image reconstruction method, which incorporates the tailored ghost correction and the two GRAPPA kernel techniques, significantly reduced the large inter-slice ghosting artifact associated with blipped-CAIPI acquisition. This was demonstrated in for the FOV/2 inter-slice image shift acquisition. With an acquisition that employs a different inter-slice shift (such as FOV/3), the inter-slice ghost artifact will appear at a spatially different location. Nonetheless, the proposed ghost correction method should still provide a good performance in removing the majority of this artifact.
The coil sensitivity profiles were used in combining coil array data to improve SNR and reduce signal bias in the magnitude images of high b-value low SNR acquisitions. Further gains in SNR from optimizing the coil combination process can be achieved by employing the coils’ noise covariance information. However, the application of slice GRAPPA and standard GRAPPA algorithms can greatly modify the noise covariance information of the reconstructed coil images (Breuer et al., 2009
). Further work will be needed to correctly characterize and account for these effects.
For DSI tractography results in , it is possible that more stable FA and volume measurements of the labeled 18 white matter pathways could be obtained by manual labeling of the paths directly on the data, instead of using the average ROIs, which are susceptible to registration errors and are probably larger than the ROIs that a rater would draw directly on the images. However, we used the average ROIs here to avoid introducing variability due to manual labeling. In a previous study we evaluated the intra-rater and inter-rater reliability of the manual labeling procedure by performing manual labeling several times on the same data set. We found the average distance between pathways labeled by the same and different raters to be, respectively, in the order of 1 voxel and 2 voxels (Yendiki et al., 2011
). In the present study we found that the distance between the pathways obtained from the 1× and 3× data sets was comparable (median distance: 2.52 mm, mean distance: 3.98 mm). Further investigation with test-retest scans is warranted to determine how the differences between the 1× and 3× results compare to the test-retest reliability of each type of scan.
The simultaneous multi-slice method does put some constraints on the number of slices. For example, the acquisition is simplified if the total number of slices is a multiple of Rsl. A more subtle effect occurs when an interleaved slice order is used. The purpose of interleaving is, of course, to avoid exciting spatially adjacent slices in rapid succession. In a standard interleaved acquisition, adjacent slices are taken TR/2 apart in time. The interleaved Rsl=3 acquisition has an additional constraint if one wishes to avoid having some spatially adjacent slices acquired in rapid succession. In the simultaneous multislice acquisition with a total of Nsl, a total of Rsl subgroups each with Nsl/Rsl slices are created. The successive excitation problem occurs between the top slice of one subgroup and the bottom slice of the subgroup above it. The problem can be avoided if the number of slices in each excitation subgroup is odd. Thus Nsl/Rsl should be an odd integer to avoid signal loss slices with imperfect slice profiles at the edge of each sub-group. If an even integer is chosen, the first slice of each subgroup will be excited right after the excitation of an adjacent slice that corresponds to the last excited slice in an adjacent slice group (from the previous TR). This leads to a signal loss from the slice crosstalk for these edge slices. This effect was observed in the Q-ball experiment where 60 axial slices were acquired with a 3× slice acceleration factor (20 slices per subgroup). In this acquisition, acquired as interleaved slices from the bottom of the head up, slice 20 (from the bottom) and slice 21 were acquired adjacent in time. Slice 20 was acquired in the last (20th) excitation of the subgroup and slice 21 was acquired as the first excitation (in the next TR period) of the second subgroup immediately after the excitation for slice 20. Therefore any overlap between the two slices would have caused slice 21 to appear darker. These slices were not selected as the slice group for the bootstrap comparison and therefore did not have an effect on the present analysis. This effect is amplified by the common practice of using a wider slice-select profile for the 180 pulse of the spin echo, in order to reduce the effect of the poorer slice profile in refocusing pulses. As mentioned above, the problem could have been avoided by using an odd number of slices in the slice subgroups thus subsequent Rsl=3 acquisitions (e.g., Nsl=63).
In this work, Rsl
=3× slice acceleration factor was used in the blipped-CAIPI simultaneous multi-slice acquisition scheme to accelerate diffusion acquisitions 3-fold. Further reduction in acquisition time can be achieved by increasing the slice acceleration factor and/or by combining parallel imaging simultaneous multi-slice method with the SIR technique as shown in Feinberg et al. (2010)
; Setsompop et al. (2010)
. With higher acceleration factors, SNR and SAR will need to be carefully considered. With a high slice acceleration factor, and hence short TR, the saturation effect will lower the SNR in a given acquisition, but the SNR per unit time
will still improve until TR is reduced below its optimum value of 1.25 T1
. On the other hand, the g-factor related SNR penalty will increases with increasing slice acceleration. In general, a reduction in SNR per acquisition
(from saturation effect and g-factor) is tolerable as long as the associated acquisition time reduction overcompensates this reduction to create a net gain in SNR per unit time
. However, additional consideration will need to be made in choosing the slice acceleration factor, when SNR of each acquisition is close to the noise floor (e.g., acquisition with high b values and/or spatial resolution). When diffusion weighted image (DWI) signals are close to the back-ground noise level, deviation from Gaussian noise behavior can cause signal bias that is orientationally dependent, resulting in a biased estimate of the diffusion parameters (Jones and Basser, 2004
). Increasing the slice acceleration factor reduces SNR per acquisition and thereby increases this bias. Since diffusion acquisitions are very sensitive to motion, which can cause large phase changes in the image, averaging of multiple repetitions is usually performed on the magnitude image. This improves the overall SNR but does not mitigate the aforementioned signal bias issue. Therefore in choosing the slice acceleration factor, one will need to consider both the SNR per unit time and the possible signal bias that results from low SNR per acquisition.
Simultaneous multi-slice excitation can lead to an increase in RF SAR deposition. In this work, the VERSE algorithm (Conolly et al., 1988
) was used to reduce the peak RF voltage and hence SAR at a cost of excitation profile degradation for off-resonance spins. This degradation was mitigated through the use of a large Time Band-Width (TBW) product value in the SLR (Pauly et al., 1991
) based RF pulse design. With this approach, we were able to obtain RF excitation and refocusing pulses for Rsl
=3 twice-refocused spin echo sequence (Feinberg and Jakab, 1990
; Reese et al., 2003
) that together provided good slice selection profiles while staying under the SAR limit. In the case of higher slice acceleration factors, slice profile fidelity will need to be traded for reductions in peak power to stay within SAR limits. The use of a single-refocused spin echo sequence would help to significantly lower SAR and mitigate this issue, but at the cost of eddy currents (Reese et al., 2003
For the acquisition on the CONNECTOM gradient system, a single refocused spin echo sequence was employed, while the maximum allowable gradient strength was limited to 200 mT/m (rather than 300 mT/m) to limit eddy-current distortions. This was a conservative approach that account for the relatively modest b-values (bmax
) that was used. At this maximum b-value, the increase in TE from reducing the maximal gradient strength from 300 mT/m to 200 mT/m is only a few ms, while the reduction in eddy current distortion is rather significant (33%). Future work will explore the use of phase reversal based distortion correction methods (Andersson et al., 2003
; Morgan et al., 2004
), which could improve the mitigation of both the eddy current and local field inhomogeneity related distortions, and allow for high-quality acquisitions of ultra high b values diffusion images using the full gradient capability of the CONNECTOM system.
In this work, we implemented the slice-GRAPPA kernel application as a k-space convolution in Siemen’s ICE reconstruction software environment and standard PC based reconstruction hardware. With our implementation, the reconstruction time is approximately 2× the acquisition time for the acquisitions used in this work. With a more efficient algorithm for the kernel application, such as the “split-domain” approach (Brau et al., 2008
), we expect significant improvement in the reconstruction speed.
In this section, a bootstrap based comparison between sensitivity combined and SoS coil combinations for HARDI diffusion data set is provided using data from experiment 1 (Q-ball imaging with 64 directions, b
and no parallel imaging acceleration). Figure S1A
shows the diffusion-weighted images and the 95% angular confidence interval of the second ODF maxima derived from boot-strap analysis. As expected, a strong elevation of the mean noise level is seen in low signal regions for the SoS coil combination. This is mitigated in the sensitivity based coil combination method. The reduction in signal bias and the improvement in SNR from sensitivity combined reconstruction results in a lower angular uncertainty (Figure S1A
right and Figure S1B
histograms). The sensitivity-based coil combination produces an over 2-fold increase in the number of voxels with a 95% angular confidence interval of less than 15°. Supplementary data related to this article can be found online at http://dx.doi.org/10.1016/j.neuroimage.2012.06.033