Short-term gradient imperfections in high-resolution EPI lead to Fuzzy Ripple artifacts

Abstract

Purpose: High-resolution fMRI is a rapidly growing research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed here as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in inferior regions of the brain.

Methods: We characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts and off-resonance effects. To investigate their origin, we employ dual-polarity readouts.

Results: Our findings indicate that Fuzzy Ripples are primarily caused by readout-specific imperfections in k-space trajectories, which can be exacerbated by short-term eddy current, and by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual-polarity EPI or by disconnecting the third-order shim coils.

Conclusion: The proposed mitigation strategies allow overcoming current limitations in layer-fMRI protocols: Achieving resolutions beyond 0.8 mm is feasible, and even at 3T, we achieved 0.53 mm voxel functional connectivity mapping. Sub-millimeter sampling acceleration can be increased to allow sub-second TRs and laminar whole brain protocols with up to GRAPPA 8. Sub-millimeter fMRI is achievable in lower brain areas, including the cerebellum.

Keywords: 7 T acquisition; Fuzzy Ripples; layer‐fMRI; ventral brain.

FIGURE 1

Fuzzy Ripples are the reason why layer‐fMRI is confined to conventional protocols. Fuzzy Ripples are the primary reason why layer‐fMRI is restricted to conventional protocols. Standard layer‐fMRI protocols are generally limited to 0.8 mm resolution with TRs of several seconds, focusing on upper cortical brain areas. These limitations cannot be surpassed because, with more ambitious acquisition protocols. Fuzzy Ripple artifacts become too strong and too frequent. The example shown here exemplifies issues of pushing resolutions beyond 0.8 mm. They refer to 3D‐EPI readouts with planar EPI trajectories. See Figure S1 for other sampling schemes of high‐resolution fMRI that amplify Fuzzy Ripple artifacts.

FIGURE 2

Fuzzy Ripples and their impact on layer‐fMRI research. The widespread effect of Fuzzy Ripples: Representative EPI images from the top 10 layer‐fMRI laboratories (based on number of publications on www.layerfmri.com/papers): Maastricht, Nijmegen/Essen, CMRR, NIH, MGH, Amsterdam, Leipzig, Cambridge. Note Utrecht/Tübingen are excluded, as despite being among the top 10 layer‐fMRI laboratories, none of their papers include publicly available layer‐fMRI EPI data.

FIGURE 3

Concept of Fuzzy Ripples as EPI odd‐even delays with ramp sampling with readout (here k_x)‐specific phase errors. (A) Gradient imperfections in high‐resolution EPI are most prominent at the corners of trapezoids, including both the rising and falling edges. The nominal EPI trajectory is derived from SIEMENS IDEA simulations, while the measured trajectory is obtained using SKOPE on a standard SIEMENS 7T MAGNETOM with third‐order shimming, at 0.8 mm resolution. (The full protocol parameters: https://github.com/layerfMRI/Sequence_Github/blob/master/Whole_brain_layers/20211012_KE). These data were acquired with a ramp‐sampling ratio of 72%. This means that the largest trajectory error of the trapezoid corners is 28% away from the center of k‐space. This section of the EPI sampling represents relatively low spatial frequencies of the image. First‐order SKOPE coefficients were used to generate trajectories of linear gradients. (B) Despite phase correction in the image reconstruction process that are designed for line‐by‐line delays of conventionally discussed odd‐even artifacts, residual imperfections persist in the part of k‐space that encodes lower spatial frequencies. These residual errors, shown as deviations in k_x, manifest as irregular k‐space grids for odd and even lines. Such odd‐even errors are expected to produce EPI ghosting artifacts in low spatial frequencies. This study introduces a strategy to address Fuzzy Ripple artifacts by employing a dual‐polarity EPI approach that alternates the read direction every other TR (see Section 3). EPI images with opposite read directions are anticipated to produce ghosts with opposite phases.

FIGURE 4

Interaction of Fuzzy Ripples with other common EPI artifacts: GRAPPA ghosts and static off‐resonance ghosts. This figure illustrates EPI acquisitions with different combinations of ramp sampling, poor B₀ shim, and GRAPPA. The unusually large FOV was purposefully chosen to detect peripheral ghost artifacts. Signal differences between reverse EPI polarity images are shown to emphasize spatial ghost patterns that might be too subtle to observe with conventional image intensity windowing. The read direction in left–right, phase encoding direction is anterior–posterior. (A) Without ramp sampling, imaging data are acquired only during the flat top of the gradient waveform. This minimizes the impact of large gradient errors, resulting in relatively weak Fuzzy Ripples in the EPI images. (B) With ramp sampling enabled, EPI becomes more sensitive to the largest gradient errors, causing Fuzzy Ripples to intensify. These ripples appear as aliasing of low spatial frequencies, with no sharp edges evident in the phase encoding direction. (C) GRAPPA, which relies on a known aliasing pattern, is affected by erroneous Fuzzy Ripple ghosts and thus amplifies their impact. (D) This differs from static off‐resonance effects. For instance, with suboptimal shimming (deliberately altered in this case), the off‐resonance effects do not amplify the low‐spatial frequency Fuzzy Ripples. Instead, they introduce edge ghosts at high spatial frequencies, which differ from Fuzzy Ripples in their appearance. (E) The dual‐polarity averaging approach effectively mitigates both sources of artifacts. The resulting images are nearly artifact‐free. Acquisition parameters of data presented here are mentioned in methods Section 3.3. See Figures S2, S12, and S13 for the reproducibility of these results in participants and on another scanners.

FIGURE 5

Impact of third‐order shim‐induced Fuzzy Ripples on fMRI activation detectability. This figure demonstrates how Fuzzy Ripples, induced by the third‐order shim, can affect the detectability of fMRI activation. These data refer to 2D‐EPI with block designed auditory activation with NORDIC denoising. When the third‐order shim is connected, the Fuzzy Ripples can be so pronounced that they mask parts of the auditory activation, preventing it from reaching the detection threshold. White arrows indicate areas where the Fuzzy Ripples are more intense with the third‐order shim engaged. These comparisons are performed with single‐polarity EPI acquisitions. Although Fuzzy Ripples are still present when the third‐order shim is disconnected, they are less severe. These residual Fuzzy Ripples that are potentially arising from uncorrected short‐term eddy currents can potentially be reduced with dual‐polarity averaging. Acquisition parameters of data presented here are mentioned in methods Section 3.4. See Figure S3 for a replication of these findings in a different participant.

FIGURE 6

Third‐order shim‐induced Fuzzy Ripples as a function of echo spacing, dual‐polarity averaging, third‐order shim. (A) The Fuzzy Ripple artifact varies with the echo spacing of the EPI readout. Consequently, the strength of this artifact can be reduced by adjusting the readout protocol, although such adjustments may compromise TE and readout efficiency. The adjustment of echo spacing comes along with a different ramp sampling ratio, which can also affect the amount of Fuzzy Ripples. (B) The Fuzzy Ripples induced by the third‐order shim can be mitigated by disconnecting its circuit. Opening this circuit reduces the inductive coupling between the third‐order shim and the gradient and reduces Fuzzy Ripple artifacts. (C) As indicated by Figures 3 and 4, dual‐polarity averaging can counteract the Fuzzy Ripples induced by the third‐order shim. This approach can mitigate Fuzzy Ripples, even for the most problematic echo spacings with the third‐order shim still connected. Though, faint residual Fuzzy Ripples remain. Acquisition parameters of data presented here are mentioned in methods Section 3.5. Figure S4 presents a reproduction of the results shown here.

FIGURE 7

Dual‐polarity averaging with respect to other popular sequences. All sequences are used with the same resolution, echo spacing, and acceleration parameters. (A) This panel shows the CMRR multiband sequence with these protocols, where off‐resonance effects and Fuzzy Ripple artifacts are clearly visible. (B) This panel displays the MGH simultaneous multi‐slice sequence with the option of dual‐polarity GRAPPA. While off‐resonance effects are mitigated, Fuzzy Ripple artifacts, though reduced, remain visible. For more in‐depth investigations of the capability of DPG to account for Fuzzy Ripple artifacts, see Figures S10 and S11. (C) This panel illustrates the same protocols using 3D‐EPI. Due to its different Mz steady‐state behavior, 3D‐EPI inherently has a higher SNR. Additionally, off‐resonance effects are less noticeable, as they are smeared and partially averaged out. However, 3D‐EPI still suffers from Fuzzy Ripples. (D) This panel depicts 3D‐EPI with dual‐polarity averaging. It is visible that Fuzzy Ripples are effectively mitigated. Acquisition parameters of data presented here are mentioned in methods Section 3.5. Figures S5 and S6 presents a reproduction of the results shown here.