Use of split slice GRAPPA (aka Leak Block) for SMS-EPI reconstruction

Accurate separation of the simultaneously acquired slices is one of the bigger limitations of the SMS-EPI method, compared to the processing used for conventional multislice EPI. The default SMS reconstruction, as used in my two introductory posts on the SMS sequences from CMRR (MB-EPI) and MGH (Blipped CAIPI), is a slice dimension adaptation of the GeneRalized Autocalibrating Partial PArallel (GRAPPA) method that was originally applied in-plane to acceleration of the phase encoding direction. It's not essential to understand the GRAPPA method applied in-plane for the purposes of understanding this post or for SMS reconstruction more generally. But if you're curious I wrote a brief introduction to in-plane GRAPPA in 2011. That post was specifically concerned with motion sensitivity of (in-plane) GRAPPA. I'll be looking in more detail at the motion sensitivity of SMS in a future post. In this post I want to compare the standard SMS reconstruction - what is generally termed Slice GRAPPA - with an alternative known as Split Slice GRAPPA. The latter option is termed "Leak Block" in the CMRR pulse sequence, MB-EPI.

What's the concern?

CMRR's parameter nomenclature offers a strong clue to the problem. In conventional EPI reconstruction we use a 2D Fourier transform (FT) which produces some amount of ringing. We also use slices that have some degree of cross-talk to neighboring slices, arising out of the limitations of frequency selectivity. So, while we think of voxels as perfect little rectangles or cubes, in reality they are blurry beasts that spread their signal into adjoining voxels because of a non-rectangular point-spread function (PSF). The dimensions we assign a voxel are entirely nominal.

With SMS we have a broader spatial problem than just non-cubic PSF. Separation of the simultaneous slices can leave signal in an incorrect position that is quite some distance from where it is supposed to be. It's a longer length scale error than the simple PSF of a voxel. Let's suppose we acquire four 2 mm slices simultaneously, 84 total slices. In one SMS acquisition we will have four slices separated by one quarter of the total slice dimension extent of 168 mm, or about 42 mm (assuming no additional inter-slice gap). Do a quick thought experiment. Imagine that in the first slice there is a very strong activation and nothing in the other three. If there is a large residual spatial error arising from poor SMS separation then we might start seeing this activation projected 4.2, 8.4 or even 12.6 cm from where it should be! And how would we know that the distant activation sites were erroneous?

This slice leakage, as it's usually called in the literature, may be strongest for simultaneously acquired neighbors but may extend throughout the slice dimension, between simultaneously acquired slices that might be quite far apart in anatomical space. And, as the thought experiment illustrates, one might assume that distant leakage would be harder to spot than the conventional cross-talk between successively acquired slices in conventional multislice EPI, or errors arising from the PSF more generally. The PSF can usually be interpreted as a local phenomenon, with errors decreasing monotonically from a voxel. Not so with SMS slice separation, meaning there is more risk of interpreting a false positive remote from the true activation site.

At this point we can recognize that reducing leakage is a noble, perhaps essential, goal. As usual with MRI, however, there's a catch. Reducing leakage using the Split Slice GRAPPA reconstruction may come at the cost of increasing in-plane artifacts. The overall (total) artifact level might be higher, too. I'll go into these issues in some detail below. The goal of this post is to perform a rudimentary assessment of the artifacts and determine the circumstances when Split Slice GRAPPA might be preferred over the conventional Slice GRAPPA reconstruction. For the CMRR sequence this amounts to whether or not to enable the Leak Block option.

What does the literature tell us?

"A couple of months in the laboratory can frequently save a couple of hours in the library." This aphorism is attributed to the chemist Frank Westheimer, in 1988. I first came across it courtesy of the Unix command fortune, which we used to run automatically after logging in to a Sun workstation back in the 17th century (or thereabouts). It has been updated for the 21st century by Runyon's corollary: "A couple of hours on the Internet can frequently save a couple of minutes in the library." Even so, reading the literature before heading down to the scanner can still be a useful exercise, provided one takes the precaution to disable e-mail and Twitter first.

The paper that introduced the Split Slice GRAPPA reconstruction for SMS-EPI is by Cauley et al. (2014). I've quoted below some of the most important points from the paper, beginning with a brief review of the history of SMS then highlighting the motivation for Split Slice GRAPPA over Slice GRAPPA. My clarifications are in [square brackets] and I truncated some sentences... to remove formulas that we don't need for this post:

SMS is a promising parallel imaging modality that has been shown to work well when the simultaneously acquired slices have substantial distance between them. However, for brain imaging the smaller FOV along the slice direction limits the distance factor and the simultaneously acquired slices are more difficult to separate.

Controlled aliasing (CAIPI) techniques have been introduced in (9) to perform shifts across the slices to more easily unalias the accelerated data.

A recent work (14) examined using blipped-CAIPI to achieve spatial shifts in the PE [phase encode] direction, between simultaneously excited slices to avoid voxel tilting artifacts. This has enabled SMS acquisitions with high acceleration factors with a low g-factor penalty; allowing for significant gains in the temporal efficiency of diffusion imaging and fMRI acquisitions.

The blipped-CAIPI scheme is now used by default in both the CMRR and MGH sequences, so the issue of tilted voxels is no longer a concern. Let's go on.

Similar to [in-plane] GRAPPA (3), the SG [Slice GRAPPA] method uses training data to fit a linear model that is used to unalias the simultaneously acquired slices. With SG, distinct kernels are used to unalias each of the simultaneously acquired imaging slices. It was illustrated in (14) that the fitted SG kernels showed a strong dependence on the static coil sensitivity profiles and not on the training data image contrast. This is a desirable property that allows the SG kernels to be used to accurately unalias SMS data that can have different contrast from the training data, e.g. in the case of diffusion encoding. However, in this work we will show that when using high slice acceleration together with in-plane accelerations the contrast independent property of the SG kernels will suffer. This results in an increased dependency of the kernels on the training data image contrast and causes increased signal leakage between the unaliased simultaneously acquired slices.

While the standard SG kernel fitting produces kernels that minimize the image artifact, the SP-SG [Split Slice GRAPPA] method takes a more balanced approach. The SP-SG method simultaneously minimizes errors coming from both image artifacts and leakage. This is accomplished by forming a new kernel fitting objective function to consider the importance of both sources of error.

In particular, the robustness of the fitting kernel across b-values is demonstrated through reductions in artifacts and improved SNR. Based on this work, the SP-SG method has the potential to enable a more robust and less artifact prone SMS acquisitions at high acceleration factors. This should facilitate further improvements in temporal efficiency of fMRI and diffusion imaging acquisitions.

...reducing the total artifact without placing restrictions on the intra- and inter-slice artifacts can lead to a dependency on artifact cancellations. With the SG method the intra- and inter-slice artifacts might be arbitrarily large but combine to help reduce the total artifact...

Note that the SP-SG formulation will result in higher total artifact error (5). That is, for SG reconstruction each convolution matrix will directly contribute toward reducing the RMSE [root-mean-square error] while no condition is placed on the inter-slice artifact... For SP-SG reconstruction we attempt to limit the influence of inter-slice artifacts... This additional condition for SP-SG can increase the kernel fitting RMSE with respect to the SG objective. However, with the SG method, using all of the slice convolution matrices to improve the kernel fitting RMSE can result in artifacts during the application of those kernels to images with different contrasts. This is caused by changes in the inter-slice leakage that no longer contribute toward reducing RMSE. By limiting the dependency on inter-slice leakage artifacts during the training process the SP-SG method is less vulnerable to produce artifacts based upon image contrast change.

So the Slice GRAPPA reconstruction might be sub-optimal for diffusion-weighted imaging (DWI), where a non-DW scan, often referred to as a b=0 image, is typically used to generate the reconstruction kernel before the kernel is applied to all the DW images in the data set.

There's an additional factor, another potential difference between SMS used for DWI and SMS used for fMRI. For DWI we almost always use in-plane acceleration to render the echo time (TE) acceptably short. If we also want to use SMS then we would be under-sampling two of the three spatial dimensions. How might in-plane acceleration interfere with SMS? Back to Cauley et al.

In-plane acceleration reduces the effective amount of PE shift that can be applied in a SMS acquisition. In this work, a FOV/3 shift was used within the in-plane accelerated “reduced” FOV [field-of-view]. This corresponds to a FOV/6 shift in the full FOV [for acceleration factor of R=2 in-plane], which results in relatively small distances between the aliased voxels. Therefore, with our combined acceleration approach, we expect the contrast dependency property of the SG kernel to be similar to MB=6 acquisitions with no in-plane acceleration (and significantly larger than the MB=3 only case).

In fMRI acquisitions the image contrast does not change significantly for a time-series. Therefore, the total artifact error of the standard SG method should be lower than that from the SP-SG method. However, the SG method will still result in more signal leakage because of the kernel fitting dependency.

For example, in fMRI applications it might be desirable to sacrifice intra-slice artifact performance to reduce the inter-slice leakage artifact. This can be viewed as a specificity and sensitivity trade-off. The inter-slice leakage artifact can cause a reduction in specificity by creating displaced false positives due to signal leakage. On the other hand, the intra-slice artifact will cause a spatially varying signal attenuation/amplification for a given slice. This will affect the sensitivity to activation detection and with large attenuation false negatives can occur. However, small modulation on signal level is not particularly harmful while a small leakage can result in a large displacement of detected activation. This is particularly evident when the acquisition is physiological noise dominated. In this regime, the relatively small attenuation/amplification due to the intra-slice artifact will affect both the signal and noise equally and there should be no net effect on the sensitivity to activation.

Let's turn our attention to another paper: Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts, by Todd et al. (2016). They assessed leakage for these parameter combinations:

MB factors of 1, 2, 4, and 6, combined with in-plane GRAPPA acceleration of 2 × (GRAPPA 2), and the two reconstruction approaches of Slice-GRAPPA and Split Slice-GRAPPA. 

Unfortunately, the one condition not tested was SMS-only, i.e. SMS without in-plane GRAPPA, most likely for the convenience of predicting false positive locations in a consistent manner, as will be explained below. Never mind.

The aim of the Todd study was to determine leakage via projection of false positives from the actual locations of activity, for a simultaneous visual and motor task: a 10 Hz flashing checkerboard and finger-to-thumb tapping. It's the real version of the thought experiment I did above. First, they established the true activation sites in the visual and motor cortices, and in the cerebellum. The locations of false positives were then determined using seed voxels in the true activation locations, as follows:

To determine if a false-positive activation occurred at an aliased location, the voxel with the largest t-score value for all activation clusters defined by SPM were considered as “seed” voxels. For each “seed” voxel chosen, the t-score values in the single voxel at the possible alias locations were evaluated. The detection of a false positive had to satisfy both criteria that (1) the single voxel at the exact alias location had a t-score value larger than the significance threshold corresponding to p < 0.001 (uncorrected), and (2) a 3 × 3 × 3-voxel volume at the alias location in the three other multiband scans did not have any voxels with t-score values that exceeded the significance threshold. The second criterion was designed to guard against the possibility that the aliased location could fall within a region of true positive activation.

How did they determine all possible aliased locations? This is where the consistent use of in-plane GRAPPA enters the picture:

The expected alias locations of a particular voxel were inferred from the multiband factor, in-plane GRAPPA factor, and in-plane CAIPI-shift. Since all multiband factors used in-plane GRAPPA 2 and in-plane CAIPI-shift FOV/3, there are two alias locations per simultaneously acquired slice, one shifted by (FOV/3)*m and one shifted by (FOV/3)*m + FOV/2, where m is the number of simultaneously excited slices away from the original slice.

(For anyone attempting to repeat this study, please see Note 1 for an important caveat regarding the FOV shift amount.)

Todd et al. observed 106 false positives for Slice GRAPPA versus only 11 for Split Slice GRAPPA:

When using Slice-GRAPPA reconstruction, evidence of false-positive activation due to signal leakage between simultaneously excited slices was seen in one instance, 35 instances, and 70 instances over the ten volunteers for the respective accelerations of MB 2 × GRAPPA 2, MB 4 × GRAPPA 2, and MB 6 × GRAPPA 2. The use of Split Slice-GRAPPA reconstruction suppressed the prevalence of false positives significantly, to 1 instance, 5 instances, and 5 instances for the same respective acceleration factors.

After accounting for multiple comparisons they estimate that up to seven false positives might arise by chance. So, the Split Slice GRAPPA seemed to work rather well, whereas leakage was a problem for Slice GRAPPA once the SMS factor was 4 or higher and when also using in-plane GRAPPA. The aliased location of some of the false positives with Slice GRAPPA is important to note, too:

False-positive activation was seen not only in the simultaneously excited slice immediately adjacent to the true positive origination slice, but also sometimes in a location two slices away. Of the 106 instances of false-positive detection, there were 22 cases in which false positives were seen in more than one alias location. There were no instances of false-positive activation being detected in a location three slices or more away from the true activation origin.

Let's sum up. The main recommendations arising from Todd's study are as follows:

...false-positive activation arising when BOLD signal changes due to true positive activation in one slice leak into other simultaneously excited slices can occur when using multiband factors of 4 or higher combined with in-plane accelerations,

A very conservative approach for high-resolution whole-brain fMRI studies would be to use multiband acceleration factor 2, in-plane GRAPPA acceleration factor 2, and Split Slice-GRAPPA reconstruction.

This is all good to know. But we don't yet know what might be appropriate if we disable in-plane GRAPPA and use just SMS acceleration. We also have a vast parameter space to explore, as Todd et al. caution:

It is important to point out that most 3 T studies, which use more conventional resolutions of 2–3 mm, do not typically employ in-plane accelerations, nor is the need for it significant given the lower resolutions of those studies.

...the effectiveness and optimization of the CAIPI shift factor was not evaluated for the experimental conditions used in this work and the use of different shift factors may have altered the findings presented here. 

I think we have a starting point. I'll keep the spatial resolution reasonably high, at 2 mm isotropic - Todd et al. used 1.5 mm cubic voxels - and stick with the default CAIPI shifts (as described in Note 1).

Throwaway tests on a Trio

I currently have version R014 of MB-EPI installed on my TIM/Trio running VB17A software. Todd et al. used version R011a of the same sequence. (See Note 1 for an important change in the default CAIPI shift in R012 and later.) I used a 32-channel receive-only head coil and an FBIRN gel phantom. The fixed acquisition parameters were: voxel size = 2mm isotropic, TE = 36.4 ms, echo spacing = 0.7 ms, read bandwidth = 1814 Hz/pixel, RF duration = 8200 us, axial slice prescription with A-P phase encoding, 7/8ths partial Fourier in the phase encoding dimension. The number of slices, TR and SMS factor were then varied as described below.

For the remainder of this post I'm going to switch to using the CMRR nomenclature. That is, in the MB-EPI sequence the Split Slice GRAPPA reconstruction is activated by enabling the Leak Block option. Otherwise, when Leak Block is off we are using the default reconstruction, i.e. Slice GRAPPA. I'm also going to use the Siemens nomenclature for in-plane acceleration. The in-plane acceleration method will always be GRAPPA (the other Siemens option being mSENSE) while the degree of in-plane acceleration is given by the iPAT factor. This creates one more parameter option to consider: the way the auto-calibration scans (ACS) are acquired for in-plane GRAPPA at iPAT = 2. The Siemens default is a single shot ACS, whereas distortion effects are matched properly only when using 2-shot (interleaved) ACS. See Note 2 for more information on the ACS options.

Artifacts visible in signal regions:

We were warned by Cauley to expect more in-plane artifacts when using Leak Block, so I opted to begin with an evaluation of in-plane artifacts before moving on to an assessment of leakage artifacts between slices. Todd concluded that an SMS factor of 4 or more, with iPAT = 2, was "accelerating too far." For the initial tests I wanted to explore the extremes. I was also interested to know if the potential for distortion-related artifacts from 1-shot ACS might be an issue overlooked by Todd et al., since they used the Siemens default for in-plane GRAPPA. I therefore tested single shot ACS versus a 2-shot segmented ACS that can be selected with a sequence flag, for SMS factors of 3 and 6:

SMS = 3, TR = 2000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: iPAT = 2 with 1-shot ACS, Leak Block off. Bottom-left: iPAT = 2, segmented 2-shot ACS, Leak Block off. Bottom-right: iPAT = 2, segmented 2-shot ACS, Leak Block enabled. (Click on the image to enlarge.)

SMS = 6, TR = 1000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: iPAT = 2 with 1-shot ACS, Leak Block off. Bottom-left: iPAT = 2, segmented 2-shot ACS, Leak Block off. Bottom-right: iPAT = 2, segmented 2-shot ACS, Leak Block enabled. (Click on the image to enlarge.)

There is clearly an interaction, leading to artifacts, for the case of SMS = 6, iPAT = 2 and use of Leak Block reconstruction. Disabling Leak Block (bottom-left and top-right panels in the above figure) eliminated the artifacts for both 1-shot and 2-shot ACS. Furthermore, to save space I displayed only the results for 2-shot segmented ACS when using iPAT = 2 and Leak Block enabled (bottom-right), but the results were very nearly identical for single shot ACS, for both SMS of 3 and 6. The consistent results suggest that for a spherical phantom with minimal structure - just a few air bubbles - there is no major interaction of the ACS scheme with the Leak Block reconstruction. Rather, it is the interaction of the Leak Block reconstruction with the total acceleration - iPAT = 2 and SMS factor 6 - that produces the artifacts. These result might not hold in a heterogeneous brain but for phantom testing purposes, given the similar performance of 1-shot and 2-shot ACS above, from this point on I opted to use just the 1-shot ACS. (See Note 3 for one additional test for iPAT = 3.)

In the next set of tests I sought to establish the point at which artifacts are introduced as the SMS and iPAT factors are increased. Here are the tests for SMS factors of 4, 5, and 6, with iPAT = 1 and 2:

SMS = 4, TR = 2000 ms, 68 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

SMS = 5, TR = 2000 ms, 60 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

SMS = 6, TR = 1000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

If you have a very good eye then you might just pick up subtle intensity variations introduced by iPAT = 2 and SMS = 5 when using Leak Block. The combination iPAT = 2, SMS = 6 and Leak Block produces clear in-plane artifacts. The corresponding single band reference (SBref) images - not shown - are artifact-free, however, confirming that we are indeed seeing an interaction of the Leak Block SMS reconstruction with the in-plane acceleration. (See Note 4 for a comparison of the MGH sequence, Blipped CAIPI to CMRR's MB-EPI. They perform similarly.)

Slice leakage:

What about the effect of Leak Block on slice leakage? What benefit might we get for the cost of the in-plane artifacts seen above? I'm going to use the simplest analysis I can think of: inspection. Leakage artifacts are easily seen in regions that should be noise if one positions the slices to one side of the phantom. It turns out that the leakage patterns are quite periodic, reflecting the SMS factor being used, and they extend in regular fashion off into the noise.

To help you identify the different artifact sources, consider this matrix of expected artifacts that corresponds to the panels in the three figures below: 

Artifact types expected in the next three figures. N/2 ghosts are intrinsic to EPI acquisition and always present. Slice leakage is expected for Slice GRAPPA reconstruction of SMS-EPI but minimally for Split Slice GRAPPA (i.e. Leak Block) reconstruction. Residual aliasing is a feature of in-plane GRAPPA (i.e. iPAT).

Here are views of the leakage artifacts corresponding to the three tests for SMS factors of 4, 5, and 6, with iPAT = 1 and 2. The slice positions are slightly different than in the three figures above, and the image contrast has been optimized for the leakage artifacts, but otherwise these three composite figures match the three composite figures above:

SMS = 4, TR = 2000 ms, 68 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

SMS = 5, TR = 2000 ms, 60 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

SMS = 6, TR = 1000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

The benefit of Leak Block (right-hand panels) is obvious. All the left-hand panels contain artifacts consistent with inter-slice leakage. The artifacts look like the wet rings left by beer glasses on a bar! Maybe someone should contrive the acronym COASTER for the next version of Split Slice GRAPPA. (See Note 5.)

Conclusions

In this post I considered only axial slices on a stationary phantom. The performance of any particular SMS, iPAT and Leak Block parameter combination will likely differ as soon as there is significant sample/subject motion. And, since head motion is anisotropic, performance will also vary with the assignment of the slice and phase encode axes relative to the brain. (See the previous post for some examples.) Indeed, the assignment of the slice and phase encode axes is important even in the absence of motion because the layout of the detector loops in the 32-channel head coil is asymmetric, and we should therefore expect that performance of both SMS and iPAT will change if coronal or sagittal slices are used.

Given the caveats, what can we say with any certainty? As far as they go, my observations on a phantom are consistent with the findings of Cauley et al. and Todd et al. in brains. The combination of SMS = 6 and iPAT = 2 is at or past the limit of what can be done with axial slices using a 32-channel coil at 3 T. Image artifacts become quite prominent when using Leak Block (aka Split Slice GRAPPA) with SMS = 6 and iPAT = 2. If SMS = 6 and iPAT = 2 are deemed essential then I would suggest not using Leak Block. Stick with the default Slice GRAPPA reconstruction. Alternatively, if you want to use SMS = 6 and you don't need iPAT then by all means use Leak Block. Or, if you decide that iPAT = 2 and Leak Block are essential then I would reduce the SMS factor below 6. Todd suggests using an SMS factor below 4 if iPAT = 2.

As it happens, I'm not a big fan of iPAT for fMRI because of its motion sensitivity. (See posts here and here.) There are recent developments that aim to improve the robustness of the ACS to motion in GRAPPA, such as FLEET, but these methods aren't yet widely available for conventional EPI or SMS-EPI. Methods like FLEET attempt to reduce the motion sensitivity of the ACS, but as yet I've not seen any methods that address the potential for mismatch between the ACS and the under-sampled time series. So my preference for fMRI would be to eliminate iPAT and use an SMS factor up to 6, with Leak Block enabled.

For diffusion-weighted imaging, on the other hand, the use of iPAT is all but required in order to keep TE reasonable. While I have yet to run any specific tests for DW-SMS-EPI, the results above suggest that a moderate SMS factor of 2-4, with  iPAT = 2 and Leak Block enabled should be acceptable. I'll present the results of DW-SMS-EPI tests in a future post. In the next post I want to assess the impact of motion on SMS-EPI for fMRI applications.

Until then, Happy New Year!

_______________________

Notes

1.  From Todd et al.:

MB factors 2, 4, and 6 all used an in-plane CAIPI shift of FOV/3 that was automatically set by the sequence.

This is because they used CMRR's sequence version R011a for which a CAIPI factor of FOV/3 was the default. But this was changed from R012 on, when for GRAPPA with R=2 acceleration the CAIPI factor was increased to FOV/4. It is still FOV/3 when in-plane GRAPPA isn't used, however. See the R014 release notes for more details. In MGH's Blipped CAIPI the default is FOV/3 but the factor can be changed by the user.


2.  The single shot ACS uses a k-space increment in the phase encode direction that corresponds to the full FOV; no aliasing. Being single shot, it's fast and is somewhat robust to motion. But it means there is a difference in the distortion of the ACS and the under-sampled EPI data that comprise the fMRI time series because the latter use a k-space increment that is twice as big, resulting in a total echo train length half as long (and a FOV half as big, creating aliasing). Such a mismatch in distortion properties creates artifacts in regions of strong magnetic susceptibility gradients.


3.  I did a comparison of MB=3 to MB=6, using both iPAT=2 and iPAT=3, just in case the interaction of high MB factor and iPAT=2 is a special case. It's not. Top-left: SBRef images from MB=3, no iPAT, as a gold standard. Top-right: MB=3, iPAT=2, segmented ACS, Leak Block on. Bottom-left: MB=6, iPAT=2, segmented ACS, Leak Block on. Bottom-right: MB=6, iPAT=3, segmented ACS, Leak Block on. Only MB=3, iPAT=2 is artifact-free. Accelerating to higher rates of MB x iPAT isn't advisable.

4.  I was able to produce similar artifacts using the MGH sequence, Blipped CAIPI. It uses Split Slice GRAPPA reconstruction by default when iPAT acceleration is enabled.

As for MB-EPI, iPAT alone was artifact-free, and only the interaction of iPAT and Split Slice GRAPPA produces artifacts. Here are the results for SMS = 6:

Top-left is MB-EPI with iPAT = 2, Leak Block off. Top-right is MB-EPI with iPAT = 2 and with Leak Block enabled. Bottom-left is Blipped CAIPI, no iPAT. Note the absence of artifacts. Bottom-right is Blipped CAIPI with iPAT = 2, and now Split Slice GRAPPA recon is being used we see artifacts that are somewhat similar (but clearly not identical) to those with MB-EPI, iPAT = 2 and Leak Block enabled. I wouldn't expect identical performance because there are a number of other implementation differences between MB-EPI and Blipped CAIPI. The important point to recognize is that the use of Split Slice GRAPPA (or Leak Block, if you prefer) instead of Slice GRAPPA reconstruction has fundamental consequences regardless of the particular implementation. The artifacts produced by the interaction of SMS, in-plane GRAPPA and Split Slice GRAPPA reconstruction are a feature, not a bug, in accord with the comments in Cauley et al. (2014).

5.  Okay, fine. I'll start. Control Of Awful Separation To Eradicate Replicas. COASTER.

References

Cauley SF, Polimeni JR, Bhat H, Wald LL, Setsompop K.
Interslice leakage artifact reduction technique for simultaneous multislice acquisitions.
Magn Reson Med. 72(1):93-102 (2014).
doi: 10.1002/mrm.24898

Todd N, Moeller S, Auerbach EJ, Yacoub E, Flandin G, Weiskopf N.
Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts.
Neuroimage. 124(Pt A):32-42 (2016)
doi: 10.1016/j.neuroimage.2015.08.056