Motion is a major cause of artifacts in DWI in clinical and research settings. Physiologic motion (respiration, vessel and CSF pulsation) usually causes only minor artifacts in specific locations of the brain and can be mitigated with existing equipment in a straightforward manner by use of gating at the cost of increased acquisition time (10
). However, there is currently no standard technology available on MRI scanners to correct for slow and fast bulk subject motion causing mis-registration, image blurring, edge artifacts and signal dropout artifacts.
Retrospective correction of slow bulk motion in diffusion data can be done e.g. by using FLIRT (27
). If motion correction is done offline, care has to be taken to reorient the diffusion gradient direction i.e. b-matrix before processing (13
). If the motion correction is done prospectively and the diffusion gradient directions are corrected with the image acquisition as demonstrated here, the errors without reorientation of the diffusion gradients would be comparatively small, limited to the amount of motion from one TR to the next.
In contrast to slow bulk motion, signal dropout artifacts caused by fast bulk motion cannot be corrected retrospectively. To prevent artifacts in the calculated diffusion maps and in fiber tracking, any corrupted images have to be discarded before processing. This leads to loss in SNR and bias in the tensor calculation if the remaining diffusion gradient directions are unevenly distributed. In the case of acquisitions with only one average of six diffusion gradient directions, the data becomes unusable for tensor analysis. To mitigate these effects, specially crafted sets of diffusion gradient directions have been proposed (29
). Manual selection of corrupted images is also cumbersome and error prone since artifacts are not always easily visible in magnitude data and phase data is rarely available.
In multi-shot acquisitions, motion artifacts can be corrected using various methods (5
). However, these do not apply to single-shot EPI. If a slightly longer acquisition time is acceptable and the percentage of corrupted images is low enough, these corrupted images can be reacquired during or at the end of the acquisition. This requires identification of affected images in real-time as has been shown before for different sequence types (31
), single-shot EPI (18
), multi-shot methods (17
) and readout-segmented EPI with navigators (6
). Our proposed reacquisition method is based on magnitude and phase data compared to the echo peak location and magnitude in k-space as described in Shi et al. (18
). Shi's method requires acquisition of multiple averages where the first average serves as reference for the corresponding further averages of non diffusion-weighted and diffusion-weighted volumes. In contrast, our method requires only the first non diffusion-weighted volume as reference volume for estimating the corruption score, defining, which images need to be reacquired.
The values of the score calculation parameters were based on a number of retrospectively analyzed diffusion data sets acquired with different protocols (data not shown). The value of the background threshold factor f1 for the magnitude data was selected to capture only the most severe artifacts due to large bulk motion. In this case, the score calculation based on phase data would fail because of rapid phase wrap. The score calculation based on the phase data captures less severe artifacts. However, it was not specifically designed to address subtle signal loss artifacts like those caused by CSF pulsation. In these cases, non-linear phase errors may confound the linear fitting process and a higher order fit may provide more robust estimates. The adjustment of the background threshold from non diffusion-weighted images to diffusion-weighted images using the background scaling factor D may cause images to be labeled as corrupt in subjects with a very large fraction of CSF compared to overall brain volume e.g. in subjects with severe atrophy or hydrocephalus. The value for the phase slope threshold f2 was selected to exclude small phase slope values caused by eddy currents even without motion. The magnitude of the effect varies depending on the diffusion gradient direction and is likely to be different from scanner to scanner depending on the gradient coil design and eddy current compensation being used. More accurate estimates for f2 could be determined with a phantom calibration step. However, depending on the sensitivity to the diffusion gradient direction, this calibration step would have to be repeated for each protocol with a different set of diffusion gradient directions and was deemed impractical. The 5% threshold that excludes slices with very little or no anatomy was also based on the retrospectively analyzed data. Variation of this threshold within a reasonable range will have little effect on the scoring. The three different protocols used in this study demonstrate that the selected score parameters work well for a range of protocols. Overall, the values of the score calculation parameters mainly determine how many images will be labeled as affected by motion and lead to an overall shift in scores while the relative order of scores would be mostly unaffected. A change in the parameters would therefore affect the likelihood of the maximum allowed number of reacquisitions being played out or the reacquisition phase ending early because fewer images were labeled as corrupt.
The proposed DW-PACE method could be improved in several ways. For data sets with many slices and high resolution and therefore long TR, the motion correction update rate can be slow since the update is done only once per TR. Providing feedback up to once per slice once the first two complete volumes are acquired could increase the update rate. Every newly acquired slice would then replace the corresponding slice in the target volume before co-registration and motion correction is applied. The update rate could also be increased by a factor of at least two with the use of simultaneous image refocusing (SIR) (35
) or controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) (36
), since these methods allow acquisition of multiple slices per diffusion encoding.
Since separate reference volumes are used for each b-value, the first (i.e. reference) volume of each b-value step is not motion corrected. To allow co-registration of volumes acquired with different b-values, a different co-registration algorithm would have to be used that is insensitive to the changes caused by different b-values e.g. a mutual information-based method. Mutual information-based methods might also provide more accurate results when aligning diffusion-weighted data from different diffusion gradient directions. However, the algorithm would have to be fast enough to provide feedback within a short time frame, typically about a few tens of milliseconds. In scans where the data throughput is very high (e.g. many slices with high resolution or a high number of receive coil elements) and image reconstruction is complex, e.g. due to the use of acceleration using GRAPPA or sensitivity encoding (SENSE) (37
), the image reconstruction and following co-registration might not be performed fast enough to provide updated values before the next volume. In these cases, motion correction may not be applied to each volume.
Finally, the DW-PACE method is only applicable as long as the SNR of the images is sufficient and the diffusion contrast does not change too much from diffusion gradient direction to diffusion gradient direction. This limits the application of the DW-PACE method to b-values below approximately 2,000 s/mm2
. Most clinical application fall in this range, however, Q-space imaging (QSI) (38
), Q-ball imaging (39
) and diffusion spectrum imaging (DSI) (40
) are incompatible with this method. For scans with high b-values and therefore little remaining tissue signal i.e. low SNR, motion tracking would have to rely on either separate navigators or external motion tracking devices e.g. by optical means (14
). In contrast, the proposed method does not require any external hardware or software and acquisition time is only increased by the amount of time needed to reacquire the corrupted images.
The combination of motion correction using DW-PACE with reacquisition worked well for all subjects included in this study. It is expected to be beneficial in patient and other subject populations, as long as motion is slow or limited to short fast motions sufficiently separated in time so that the selected maximum number of reacquired images is enough to replace the corrupted images. Given the limited range of experiments, it should be noted that this is a proof-of-principle study and that further studies are warranted to fully understand the best application of this method. In conclusion, the proposed method allows calculation of artifact free diffusion maps in the presence of slow and fast bulk subject motion at the cost of only slightly increased scan time.