Facilitating diffusion tensor imaging of the brain during continuous gross head motion with first and second order motion compensating diffusion gradients

Abstract

Purpose:

To demonstrate the feasibility of motion compensating diffusion gradient schemes in the acquisition of quality diffusion tensor images (DTI) of the brain during continuous gross head motion.

Methods:

Five healthy subjects were scanned using a clinical 3T MRI with and without continuous head motion. For one volunteer, DTI data was acquired using standard (M0) diffusion weighted (DW) gradients, and first (M1) and second (M2) order gradient schemes that were previously developed for use in cardiac DTI. In four additional volunteers, DTI data was acquired with M0 and M2 gradients. DTI parameters were calculated and compared with established retrospective motion corrections.

Results:

In the absence of motion, DTI parameters calculated from M0, M1, and M2 data were consistent. In the presence of motion, up to 44% of DW images acquired with M0 gradients were corrupted by signal dropout, compared to 0% of the M2 images. In voxelwise comparisons, DTI parameters calculated using motion-M0 data were elevated compared to reference data. Retrospective corrections for extreme motion applied to motion-M0 data did not improve consistency with reference data in cases where motion corrupted greater than 15% of DW images. In contrast, DTI parameters calculated with motion-M2 data were consistent with reference data.

Conclusion:

This proof-of-principle study demonstrates that motion compensating diffusion gradients can mitigate artifacts due to continuous motion in DTI of the brain and offers promise for improved DTI accessibility. Further study will be necessary to determine the robustness of the approach in patient populations with high susceptibility to head motion.

Introduction

Diffusion tensor imaging (DTI) provides useful information in neurological conditions, such as multiple sclerosis, Parkinson’s disease, stroke, and developmental disorders^1,2. However, diffusion weighted (DW) imaging techniques are susceptible to macroscopic motion artifacts due to the strong diffusion gradients employed^3,4. Bulk head motion commonly causes signal dropout in DW acquisitions, leading to corrupted DTI data^5–9. As a result, patients prone to head motion are largely excluded from DTI, despite its potential as a powerful diagnostic tool.

Many methods of motion prevention and correction have been proposed to mitigate signal dropout in DW acquisitions^4,10. While motion prevention is ideal, it can be difficult to achieve. This is particularly true in many patient populations for which DTI promises to provide significant diagnostic value, including children and patients with neurologic conditions (e.g. stroke, Parkinson’s)¹. Many motion correcting techniques rely on the acquisition of multiple volumes and the elimination or reacquisition of images containing signal dropout^8,11,12. Although successful for overcoming brief and occasional head motion, such techniques rely on the ability to eventually acquire motion free images, which may not be possible for continuously moving patients. Other motion correction techniques characterize head motion during scanning, for example using specialized hardware, navigator sequences, or field mapping, however the success of these techniques is limited when head motion and signal dropout are extreme^13–18.

The prevalence of motion artifacts in DW acquisitions has made DTI of the living heart exceedingly difficult. To combat these challenges, Nguyen et al. have developed motion compensated diffusion gradient schemes that are robust to the bulk motion of the beating heart and have reproducibly quantified cardiac DTI parameters^19–21. To our knowledge, this approach has not yet been applied to the brain²².

In this study, we investigated the feasibility of acquisitions containing first and second order motion compensated diffusion gradients, based on the work of Nguyen et al., for DTI of the brain during continuous gross head motion^19–21. Motion compensating gradient schemes were integrated into DTI acquisitions without specialized hardware or additional post-processing steps. The resulting sequences were tested using a clinical MR scanner and compared to a standard DTI acquisition and a popular retrospective correction for extreme motion.

Methods

DTI experiments were designed to evaluate first (M1) and second order (M2) motion compensated diffusion gradients in brain imaging protocols^19–21. Motion compensated gradients aim to refocus gradient-induced phase accumulated by moving spins. A spin in a gradient magnetic field, $\overrightarrow{G}$, accumulates phase according to²³

$$\varphi \left(t\right)={\overrightarrow{M}}_0\cdot {\overrightarrow{r}}_0+{\overrightarrow{M}}_1\cdot {\overrightarrow{v}}_0+\frac{1}{2}{\overrightarrow{M}}_2\cdot {\overrightarrow{a}}_0+\dots$$ ( 1 )

where ${\overrightarrow{r}}_0,{\overrightarrow{v}}_0$, and ${\overrightarrow{a}}_0$ are the spin’s initial position, velocity, and acceleration, respectively, ${\overrightarrow{M}}_n(n=0, 1, 2, \dots$) are the $n^{th}$ order gradient moments, defined by

$${\overrightarrow{M}}_n\left(t\right)=\gamma {\int }_0^t\overrightarrow{G}\left(t\right)t^{n}dt$$ ( 2 )

and $\gamma$ is the gyromagnetic ratio.

The traditional Stejskal-Tanner (M0) diffusion gradients null the zeroth-order gradient moment, so that stationary spins are rephased under on-resonance conditions²⁴. From Eq. (1) it is evident that moving spins will have non-zero residual phase, causing signal fallout. In this work, bipolar diffusion gradients were used to provide M1 motion compensation, nulling zeroth and first order gradient moments, and B1-resistant diffusion gradients proposed by Nguyen et al. were used to provide M2 motion compensation, nulling zeroth, first, and second order gradient moments²⁰. Pulse sequence diagrams of the M0, M1, and M2 sequences used in this work can be found in Supporting Material Figure S1.

Five healthy volunteers were scanned using a 3T Siemens Prisma (Siemens Healthcare, Erlangen, Germany) and a 32-channel head coil under a Cleveland Clinic Institutional Review Board approved protocol. Whole-brain DTI datasets were acquired using diffusion prepared echo planar imaging (EPI) sequences (TR/TE = 12600/89 ms; FOV = 220 × 220 mm²; 110 × 110 matrix; 2 mm slice thickness; 60 slices; 30 volumes with b=1000 s/mm², four volumes with b=0). DTI data was acquired with M0, M1, and M2 motion compensating diffusion gradients for one volunteer (Study 0), and with M0 and M2 motion compensating diffusion gradients in four additional volunteers (Study 1 – Study 4). Echo and repetition times were matched for all diffusion gradient schemes, resulting in an acquisition time of 7 minutes, 46 seconds for each sequence.

In the first volunteer (Study 0), two datasets were acquired for each motion compensating gradient scheme (M0, M1, M2) in a single session. The volunteer was instructed not to move during one acquisition, yielding the no-motion datasets. In the other acquisition, the volunteer moved his head throughout the duration of the scan, yielding the motion datasets. The motion was approximately a periodic rotation of 20° around the axis of the spine with a frequency of 1 Hz.

In the four additional volunteers (Studies 1–4), M0 datasets were acquired without motion to serve as the reference data. M0 and M2 datasets were also acquired during continuous head motion consisting of 5–20° periodic rotation around the axis of the spine with a frequency of approximately 0.1–1 Hz.

Each dataset was corrected for motion and eddy current artifacts with eddy¹³ from FSL²⁵ (M0, M1, M2 datasets). In addition, the eddy package provides specialized options to correct for motion-associated signal dropout with outlier replacement¹⁴ and slice-to-volume misregistration²⁶. As these are widely available options for dealing with extreme motion, we also generated datasets with these additional corrections (M0+, M1+, M2+), hereafter referred to as datasets with extreme motion correction. For each dataset, fractional anisotropy (FA), longitudinal diffusivity (LD), mean diffusivity (MD), and transverse diffusivity (TD) maps were calculated using the dtifit package of FSL. The maps were then coregistered to a common space using ANTs²⁷.

DTI map analysis was performed in python. For whole brain analysis, five superior and inferior edge slices were discarded due to registration errors resulting from extreme head motion. In all comparative analyses, the parameter maps derived from the no-motion-M0 data were used as references. For each dataset, density plots of the per voxel values of DTI-derived indices and per voxel differences between DTI-derived indices calculated from the motion and reference data over the whole brain were computed with Seaborn²⁸ and Matplotlib²⁹.

Results

Sample diffusion weighted images from Study 0, acquired during head motion using M0, M1, and M2 diffusion gradients, with and without retrospective motion correction for extreme motion, are shown in Figure 1(b–g). Reference images acquired without deliberate motion using traditional M0 diffusion gradients are shown in Figure 1(a). Results of visual signal dropout assessment in all diffusion weighted images across the whole brain for all volunteers are summarized in Table 1.

Figure 1.

Diffusion weighted images (b=1000 s/mm²) from Study 0 showing a representative slice and 10 representative diffusion directions. (a) Reference images acquired with traditional M0 diffusion gradients, no deliberate head motion, and no retrospective corrections for extreme motion. (b-g) Diffusion weighted images acquired with (b, c) M0, (d, e) M1, and (f, g) M2 motion compensated diffusion gradients, acquired during continuous gross head motion without (b, d, f) and with (c, e, g) retrospective corrections for extreme motion.

Table 1.

Number (percent) of diffusion weighted images from the whole brain (1500 total images from the central 50 of 60 total slices and 30 diffusion directions) corrupted by continuous gross head motion for each volunteer.

Number (Percent) of images corrupted by head motion
	M0	M0+	M1	M1+	M2	M2+
Study 0	662 (44%)	671 (45%)	109 (7%)	16 (1%)	0 (0%)	0 (0%)
Study 1	132 (9%)	12 (1%)	–	–	0 (0%)	0 (0%)
Study 2	598 (39%)	807 (53%)	–	–	0 (0%)	0 (0%)
Study 3	27 (2%)	0 (0%)	–	–	0 (0%)	0 (0%)
Study 4	197 (13%)	4 (0%)	–	–	0 (0%)	0 (0%)

Counts are reported for images acquired during head motion with M0, M1, and M2 motion-compensating diffusion gradients, without (M0, M1, M2) and with (M0+, M1+, M2+) retrospective correction for extreme motion. Note that M1/M1+ images were only acquired in Study 0.

In Figure 1(b–c), severe signal dropout due to head motion is evident in the motion-M0/M0+ DW images. Based on visual inspection of the Study 0 data, greater than 40% of the motion-M0/M0+ images were corrupted by signal dropout. In Figure 1(d), some signal dropout is also present in the motion-M1 diffusion weighted images, although it is less frequent and severe than that observed in the motion-M0/M0+ images. Applying retrospective extreme motion correction to the motion-M1 images further reduced signal dropout from 7% to 1% in the motion-M1+ data (Figure 1(e)). As is represented in Figure 1(f–g), no substantial signal dropout was observed in the motion-M2/M2+ diffusion weighted images. These results were consistent across all volunteers, with no significant signal dropout observed in any of the images acquired with M2 sequences (see Table 1).

Figure 2 shows representative Directionally Encoded Color Fractional Anisotropy (DEC-FA) maps from Study 0, calculated with motion and no-motion M0, M1, and M2 datasets, with and without retrospective correction for extreme motion. Voxelwise absolute differences between motion and reference maps are also shown. A similar analysis of the Study 0 LD, MD, and TD maps can be found in the Supporting Material Figures S2–S4.

Figure 2:

Directionally Encoded Color Fractional Anisotropy (DEC-FA) maps from a representative slice in the corpus callosum region, calculated using DTI data acquired without and with intentional motion, and the differences between motion and reference no-motion-M0 maps. Results are presented for Study 0 acquisitions using M0, M1, and M2 motion compensating gradient schemes, both without (left) and with (right) added retrospective correction for extreme motion.

Visual assessment of the no-motion DEC-FA maps in Figure 2 indicates consistency among the M0, M1, and M2 data in the absence of motion. In contrast, the motion-M0/M0+ DEC-FA maps are largely corrupted by head motion, with both maps visibly overestimating FA in the white and gray matter compared to the reference. The motion-M1/M1+ maps demonstrate markedly improved consistency with the reference. It is noted that the retrospective corrections used to yield the motion-M1+ data improved the fidelity of the resulting DEC-FA map, with reduced blurring and overestimation of FA throughout the white and gray matter. The motion-M2/M2+ DEC-FA maps are the most consistent with the reference, demonstrating the robustness of the M2 diffusion gradients to motion during data acquisition.

Representative DEC-FA maps for all five volunteers and reference no-motion-M0, motion-M0/M0+, and motion-M2 data are shown in Figure 3. Difference maps with respect to the reference data are also presented for all motion datasets. Similar results for the LD, MD, and TD maps acquired in Studies 1–4 can be found in Supporting Material Figure S5.

Figure 3:

DEC-FA maps from all five volunteers using data acquired with M0 diffusion gradients without head motion, M0 diffusion gradients during head motion, and M2 diffusion gradients during head motion. Results are presented for data processed with (M0+) and without (M0) retrospective corrections for extreme motion for data acquired with M0 diffusion gradients. Difference maps ($\Delta$FA) taken with respect to the reference no-motion-M0 data are presented for the motion M0, M0+, and M2 data. Note that some data from Figure 2 (Study 0) is repeated for ease of comparison between the five studies.

While four of the five motion-M0 DEC-FA maps are noticeably corrupted by head motion compared to the reference maps, all motion-M2 maps are consistent with the reference data. In cases where less than 15% of DW images acquired with M0 gradients are corrupted by head motion (Studies 1, 3, and 4), retrospective corrections for extreme motion were able to improve the quality of the motion-M0+ DEC-FA maps, but in the cases where greater than 15% of the motion-M0 DW images were corrupted by motion (Studies 0 and 2), the retrospective corrections for extreme motion were unable to improve the quality of the resulting motion-M0+ DEC-FA maps. In all cases, the motion-M2 data is most consistent with the reference, as is evident in the difference maps.

Density plots of the FA distributions and voxelwise error in FA over the whole brain for each acquisition and all volunteers are shown in Figure 4. The results of Study 0 are shown in Figure 4(a–c), and the results of Studies 1–4 are shown in Figure 4(d). Similar analysis for LD, MD, and TD can be found in the Supporting Material Figures S2–S5. Study 0 DTI parameter analysis from a region of interest in the corpus callosum is presented in Supporting Material Table S1.

Figure 4:

Distributions of FA in the whole brain, calculated with Study 0 DTI data acquired (a) without intentional motion and (b) with intentional motion, both without (left) and with (right) retrospective correction for extreme motion. In the distributions for data acquired with motion, the reference distribution calculated from no-motion-M0 data is shown in dashed black. (c) Distributions of the per voxel differences in FA maps calculated with motion data and the reference (no-motion-M0) data. (d) FA distributions (left) and errors (right) calculated with respect to the reference data for Studies 1–4.

From Figure 4(a), parameter distributions derived from Study 0 no-motion-M1/M2 datasets demonstrate high levels of agreement with data acquired using standard M0 diffusion gradients in the absence of motion.

In Figure 4(b), Study 0 FA distributions derived from motion-M0/M0+ datasets (blue) are significantly right-shifted compared to the reference (dashed black). In comparison, the motion-M1/M1+ distributions (green) have significantly improved consistency with the reference and the motion-M2/M2+ distributions (orange) are the most consistent.

Figure 4(c) presents distributions of the per voxel differences in FA maps calculated from Study 0 data acquired during motion compared to reference maps. Both without (left) and with (right) retrospective motion correction for extreme motion, the motion-M0 datasets resulted in the largest bias and variance in the voxelwise difference in FA. While both the motion-M1 and motion-M2 difference distributions yielded low bias, the motion-M1+ datasets generally achieved the lowest bias, and the motion-M2 datasets yielded the lowest variance.

A comparison of the standard (dashed black), motion-M0 (blue), motion-M0+ (orange), and motion-M2 (green) FA maps from Studies 1–4 can be found in Figure 4(d). Whole brain FA (left) and voxelwise FA error (right) distributions are consistent with the results in Figure 3 and 4(a–c). The degree of corruption of the motion-M0/M0+ data varies between different studies, with very significant variance and bias observed in Study 2 and minimal variance and bias observed in Study 3. In all cases, however, it is observed that the motion-M2 FA maps are consistent with the reference data, with FA distributions that align with the reference (left) and narrow difference distributions that are sharply peaked around zero (right).

Discussion

In this work, motion compensating diffusion gradients were proposed to mitigate artifacts due to large degrees of motion. Experiments in healthy volunteers demonstrated considerably improved consistency in DTI parameters from data obtained during head motion when M1 and M2 diffusion gradients were employed in place of traditional M0 diffusion gradients.

Because patient motion during DW imaging is one of the most frequent sources of error in DTI derived measures, many techniques have been proposed to provide motion correction, but they are generally effective only when head motion and signal dropout are limited^{3,4,9,10,15–18}. In contrast, the proposed use of motion compensating diffusion gradients during data acquisition inherently reduces the occurrence of motion corruption in the acquired images, even in the presence of continuous, large-scale head motion. In the present study, M1 gradients reduced and M2 gradients completely eliminated significant signal drop out in DW images acquired during head motion, allowing for quantification of DTI parameters despite the motion.

M1 and M2 diffusion gradient sequences were directly compared to FSL eddy’s correction for outliers and slice-to-volume misregistration, which are widely used to address significant subject motion^10,13,14. While FSL eddy with extreme motion correction worked well when DW image corruption was moderate (e.g., Study 0 motion-M1+, Study 4 motion-M0+) it was unable to correct for severe dropout (e.g., Study 0 and Study 2 motion-M0+). This is consistent with the work of Andersson et al., in which the FSL framework was reliable only when no more than 10% of the DW images were affected by significant signal dropout¹⁴. As a result, the motion-M1/M2 datasets dependably resulted in more consistent estimation of DTI parameters than the M0+ datasets (Figures 2–4 and Supporting Material Figures S2–S5).

From an examination of the data acquired during head motion in Study 0, M2 gradients were observed to eliminate signal dropout in the DW images, whereas some signal dropout remained in the images acquired with M1 gradients. The motion-M1 results presented in Figures 2, 4, S2–S4, and Table S1 show elevated quantification of DTI parameters when additional retrospective motion correction was not employed. In contrast, the motion-M2 DTI parameters generally had less bias and variance compared to the reference parameters without the need for additional retrospective corrections.

Motivated by the results of Study 0, Studies 1–4 focused on the use of M0 and M2 diffusion gradients, with and without retrospective extreme motion correction. Across the five volunteers scanned in this work, the motion-M2 DW images consistently exhibited no significant signal dropout and the resulting DTI parameter maps were found to have superior agreement with the reference data compared to M0+ data. In contrast, the degree of corruption in the motion-M0 images and DTI parameter maps was observed to depend on the details of the motion exhibited by the individual volunteers (see Table 1, Figures 2–3). To understand this intersubject variability, motion parameters and outliers were studied, but were found to be unreliable due to the extreme corruption of the motion-M0/M0+ data, (see Supplemental Material Figure S6). Further investigation of motion details in relationship to the corruption of M0 DTI data, although outside the scope of this study, is warranted in future work. Despite the varying quality of DTI results obtained using traditional methods, when a subject is prone to head motion this work finds that it is possible to obtain quality DTI parameter maps with motion compensated diffusion gradients.

One cost of using motion compensated diffusion gradients is the resulting increase in TE and total scan time compared to traditional sequences, which also increases opportunities for motion. While TE and TR were matched at TE/TR = 89/12600 ms for all sequences in this study, the minimum possible echo and repetition times on our system were TE/TR = 47/5900 ms, 76/10400, and 88/11400 ms, yielding minimum acquisition times of 3 min 38 s, 6 min 25 s, and 7 min 2 s, for M0, M1, and M2 respectively. However, the diffusion gradients employed in this study were not optimized for minimum TE, and it is possible to reduce the time penalty associated with M1 and M2 gradients by employing TE-optimized waveforms^30–32.

While DTI parameters from no-motion M0, M1, and M2 data in this work were generally consistent and data obtained with M1 and M2 gradients during head motion was significantly improved compared to the motion-M0/M0+ data, some bias was observed in no-motion-M1/M2 data relative to no-motion-M0 data. In a region of interest in the body of the corpus callosum for Study 0, the percent error in the mean for FA, LD, MD, and TD was 8% or less for no-motion-M1 and 12% or less for no-motion-M2, compared to 6% or less in rescan data acquired with the standard no-motion-M0 sequence (see Supporting Material Table S1). Diffusion time has been shown to affect DTI measures and could contribute to the observed bias³³. In addition, motion compensated diffusion gradients have been shown to reduce signal attenuation due to perfusion, particularly at low b-values, therefore affecting DTI quantification^34–36. A detailed study on the effects of M1 and M2 gradients on perfusion signals is warranted in future work, potentially investigating the use of low but non-zero b-value reference images as has been proposed in cardiac DTI^37,38.

Study Limitations:

One limitation of the present study arises from its focus on gross head motion. Slight signal loss due to physiological motion (e.g. cardiac pulsatility, respiratory motion), can be easily missed but might cause biases in quantitative analyses that can lead to false inferences. Future work should investigate the ability of motion compensating diffusion gradients to eliminate subtle artifacts associated with physiological motion.

Another limitation of this work is the controlled range and frequency of head motions tested. Throughout the motion scans, volunteers moved their heads with consistent rhythms and in regular patterns. Future work testing a larger variety of head motions is required to ensure the continued robustness of M2 diffusion gradients to motion.

This work was additionally limited in its participant population. Future studies should be designed to test the large-scale application of motion compensating diffusion gradients to DTI for a range of patient populations that are susceptible to head motion, including children and adults with neurological conditions such as Parkinson’s and stroke.

Finally, it will be important to perform studies in future work that test the generalizability of the results presented herein. Although this work primarily focused on the use of M2 diffusion gradients due to their ability to eliminate signal dropout without additional retrospective corrections for extreme motion, additional work is necessary to provide guidelines regarding the optimal degree of diffusion gradient motion compensation (i.e., M1, M2), possibly in combination with additional retrospective motion corrections, for given populations. In such studies, the benefit of reduced signal dropout gained with increasing gradient motion compensation will need to be weighed against increased TE and scan time, as well as any potential bias in quantified DTI parameters.

Conclusion

This study has shown that quality diffusion tensor images can be acquired in the presence of large degrees of head motion with motion compensating diffusion gradient schemes. Unlike most motion correction techniques, which rely on the limitation of head motion duration and scope to be effective, diffusion encoding using first or second order motion compensation can reduce and even eliminate the incidence of signal dropout in diffusion weighted images acquired during continuous head motion. This technique can be applied on a conventional clinical MR scanner using only modifications to the pulse sequence. As a result, the use of motion compensating diffusion gradients has the potential to make DTI routine to a larger patient population.

Supplementary Material

Supinfo

Figure S1: Pulse sequence diagrams for the (a) M0 traditional, Stejskal-Tanner²⁴, (b) M1first-order motion compensated (c) and B1-resistant M2²⁰ second-order motion compensated diffusion gradient schemes used in this study. Due to the additional lobes of the M1 and M2 diffusion gradients, the minimum achievable TE is increased for the motion compensated sequences compared to the traditional M0 sequence. While a fixed TE = 89 ms was used for all sequences in our study, the minimum TE achievable with our system is noted for each gradient scheme (47 ms, 76 ms, and 88 ms for the M0, M1, and M2 sequences, respectively).

Figure S2: (a) LD maps ($\times {10}^{-3}$ mm²/s) from a representative slice in the corpus callosum region calculated using data acquired without and with intentional motion and the differences between motion maps and reference, no-motion-M0 maps. Results are presented for acquisitions using M0, M1, and M2 gradient schemes, both without (left) and with (right) added retrospective correction for extreme motion. Distributions of LD in the whole brain, calculated with data acquired (b) without intentional motion and (c) with intentional motion, both without (top) and with (bottom) retrospective correction for extreme motion. For data acquired with motion, the reference no-motion-M0 data is shown in dashed black. (d) Distributions of per voxel differences in LD maps calculated with motion data and the reference data.

Figure S3: (a) MD maps ($\times {10}^{-3}$ mm²/s) from a representative slice in the corpus callosum region calculated using data acquired without and with intentional motion and the differences between motion maps and reference, no-motion-M0 maps. Results are presented for acquisitions using M0, M1, and M2 gradient schemes, both without (left) and with (right) added retrospective correction for extreme motion. Distributions of MD in the whole brain, calculated with data acquired (b) without intentional motion and (c) with intentional motion, both without (top) and with (bottom) retrospective correction for extreme motion. For data acquired with motion, the reference no-motion-M0 data is shown in dashed black. (d) Distributions of per voxel differences in MD maps calculated with motion data and the reference data.

Figure S4: (a) TD maps ($\times {10}^{-3}$ mm²/s) from a representative slice in the corpus callosum region calculated using data acquired without and with intentional motion and the differences between motion maps and reference, no-motion-M0 maps. Results are presented for acquisitions using M0, M1, and M2 gradient schemes, both without (left) and with (right) added retrospective correction for extreme motion. Distributions of TD in the whole brain, calculated with data acquired (b) without intentional motion and (c) with intentional motion, both without (top) and with (bottom) retrospective correction for extreme motion. For data acquired with motion, the reference no-motion-M0 data is shown in dashed black. (d) Distributions of per voxel differences in TD maps calculated with motion data and the reference data.

Table S1. In Study 0, seven datasets were collected in a single session: Reference no-motion-M0, Rescan no-motion-M0, no-motion-M1, no-motion-M2, motion-M0, motion-M1, and motion-M2. The rescan no-motion-M0 data was collected at the end of the scanning session to assess variability in the reference scan. The subject was brought out of the MRI and allowed to get up off the table for approximately five minutes before re-entering the MRI to collect the rescan data. As described in the Methods, all data was corrected for motion and eddy current artifacts with eddy from FSL. DTI parameters (FA, LD, MD, TD) were calculated using dtifit from FSL and the resulting maps were coregistered with the reference data using. For comparison to a popular retrospective motion correction technique, FSL eddy’s outlier replacement and slice-to-volume misregistration corrections were additionally applied, yielding the motion-M0+, motion-M1+, and motion-M2+ datasets.

Table S1 compares FA, LD, MD, and TD in a region of interest drawn in the body of the corpus callosum for Study 0. Means and standard deviations of the DTI parameters are presented for all datasets.

From the no-motion data, the absolute difference in the mean relative to the standard is observed to be comparable although slightly elevated for the no-motion-M1/M2 data compared to the rescan data. Specifically, the rescan data are observed to have a 0% - 6% absolute difference in the mean relative to the reference data. By comparison, the absolute difference in the mean relative to the reference ranges from 0%−8% for no-motion-M1 and 1%−12% for no-motion-M2.

In the motion datasets, DTI parameter characterization is very corrupt when traditional gradients are used, with up to 91% error in the mean for the motion-M0 data and retrospective corrections for extreme motion failed to improve the fidelity of the data, in fact increasing the error in the DTI parameters to up to 120%. The motion-M1 and motion-M2 data shows greatly improved DTI parameter quantification, with up to 26% error in the mean for motion-M1 parameters and less than 20% error in the mean for motion-M2.

Figure S5: (a-c) DTI parameter maps for a representative slice in the region of the corpus callosum for Studies 1–4, showing (a) LD ($\times {10}^{-3}$ mm²/s), (b) MD ($\times {10}^{-3}$ mm²/s), and (c) TD ($\times {10}^{-3}$ mm²/s) calculated using reference data (no-motion-M0), motion-M0 data, motion-M0+ data, and motion-M2 data. Differences between the reference data and each of the other datasets are also shown. (d-f) DTI parameter distributions in the whole brain from studies 1–4 are shown for (d) LD, (e) MD, (f) TD. (left) Distributions of DTI parameters, calculated with motion-M0 data (blue), motion-M0+ data (orange) and motion-M2+ compared to the reference no-motion-M0 data (dashed black). (right) Distributions of per voxel differences in DTI parameter maps calculated with motion data and the reference data.

Figure S6: Motivated by the intersubject variability observed in the motion-M0 and motion-M0+ FA maps (Figure 3), an investigation of the per-subject motion was performed using the RMS movement and outlier report generated by FSL Eddy. RMS motion parameters for the motion-M0+, and motion-M2+ data for all subjects are shown in Figure S6(a). A count of the number of outliers detected by FSL Eddy compared to the number of images identified as having signal dropout through visual inspection for the motion-M0+ and motion-M2+ data for each subject are reported in Figure S6(b).