Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction.

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Hum Brain Mapp. Author manuscript; available in PMC 2016 December 01. Published in final edited form as: Hum Brain Mapp. 2016 December ; 37(12): 4405–4424. doi:10.1002/hbm.23318.

Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction Paul A. Taylor1,2,3, A. Alhamud1, Andre van der Kouwe4, Muhammad G. Saleh1, Barbara Laughton5, and Ernesta Meintjes1 1

MRC/UCT Medical Imaging Research Unit, Dept. of Human Biology, Faculty of Health Sciences, University of Cape Town, South Africa

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2

African Institute for Mathematical Sciences, Muizenberg, Western Cape, South Africa

3

Scientific and Statistical Computing Core, National Institutes of Health, Bethesda, MD, USA

4

Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts

5

Children's Infection Diseases Clinical Research Unit, Dept. of Paediatrics and Child Health, Stellenbosch University, South Africa

Abstract

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Diffusion tensor imaging (DTI) is susceptible to several artifacts due to eddy currents, echo planar imaging (EPI) distortion and subject motion. While several techniques correct for individual distortion effects, no optimal combination of DTI acquisition and processing has been determined. Here, the effects of several motion correction techniques are investigated while also correcting for EPI distortion: prospective correction, using navigation; retrospective correction, using two different popular packages (FSL and TORTOISE); and the combination of both methods. Data from a pediatric group that exhibited incidental motion in varying degrees are analyzed. Comparisons are carried while implementing eddy current and EPI distortion correction. DTI parameter distributions, white matter maps and probabilistic tractography are examined. The importance of prospective correction during data acquisition is demonstrated. In contrast to some previous studies, results also show that the inclusion of retrospective processing also improved ellipsoid fits and both the sensitivity and specificity of group tractographic results, even for navigated data. Matches with anatomical white matter maps are highest throughout the brain for data that have been both navigated and processed using TORTOISE. The inclusion of both prospective and retrospective motion correction with EPI distortion correction is important for DTI analysis, particularly when studying subject populations that are prone to motion.

Keywords diffusion tensor imaging (DTI); motion correction; EPI volumetric navigator; fractional anisotropy; tractography

Please send correspondence to: Paul A. Taylor, Ph.D., Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, SOUTH AFRICA, Fax: +27 (0)21 448 7226, [email protected]. CONFLICTS OF INTEREST: The authors declare no competing financial interests.

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INTRODUCTION Image quality in any magnetic resonance imaging (MRI) modality depends significantly on both the data acquisition methods (i.e., the implemented pulse sequences) and the subsequent processing steps utilized. In diffusion tensor imaging (DTI) most standard pulse sequences are based on single-shot echo planar imaging (EPI) for reading out the MRI signal. The diffusion weighted imaging (DWI) data can be adversely affected by a combination of several artifacts due to the implementation of strong diffusion gradients to probe water diffusion and EPI schemes and to allow for fast acquisition [Le Bihan et al., 2006]. Artifacts include eddy currents induced mainly by switching the DW gradients on and off, EPI distortions induced by magnetic field (B0) inhomogeneity and subject motion [Jezzard et al., 1998; Ardekan and Sinha, 2005; Landman et al., 2007; Wu et al., 2008, Irfanoglu et al., 2012].


Complications in correcting these effects arise due to the fact that each DW volume has a different diffusion contrast, as well as relatively low signal-to-noise ratio (SNR). Several MRI pulse sequences for prospective (real-time) distortion correction have been designed to minimize the effects of eddy currents [Reese et al., 2003], subject motion [Benner et al., 2011; Kober et al., 2012; Alhamud et al., 2012] and EPI distortion [Holland et al., 2010; Alhamud et al., 2015b]. However, these sequences either may not be available on most MRI scanners or are not able to fully correct for all distortions. Therefore, several retrospective correction methods for the above distortions have been designed as an alternative means to correct or add more accuracy in the prospectively corrected data, e.g., see [Andersson et al., 2003; Rohde et al., 2004; Landman et al., 2007; Wu et al., 2008; Mukherjee et al., 2008; Irfanoglu et al., 2012]. To date, however, no optimal combination of DTI acquisition and processing has been definitively determined, and the purpose of this manuscript is to assess the efficacy of one such prospective motion correction technique together with two wellknown post-processing methods.

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The process of combining correction methods is inherently complicated: there are a wide variety of techniques to choose from for each step; the effects of each distortion are not fully independent; and, similarly, the actions of each correction technique are not independent. For example, B0 distortion causes both spatial shifts and geometrical distortions of imaged objects, particularly along the phase-encoding direction, as well as intensity variation; subject motion also results in spatial shifts, and eddy currents produce severe geometric distortion, especially in DTI images. Most processing steps depend on some form of volume alignment, such as of DWIs to a reference non-weighted (b0) or anatomical volume e.g., [Rohde et al., 2004], and therefore the effects of subject motion (and any correction steps taken) become convolved in and influence the quality of all other analysis procedures and results [Norris, 2001; Landman et al., 2007; Aksoy et al., 2008; Ling et al., 2012]. Additionally, computational processing techniques, which are often nonlinear and based on model assumptions, may introduce non-physiological factors into the outcomes, such as warping, smoothing or the modeling of noise as signal. Research remains active in this area, particularly as diffusion imaging becomes increasingly applied in clinical settings and in younger populations, who are highly prone to motion, and using greater magnetic field strengths and higher diffusion weighting (DW) factors, such as in high angular resolution

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diffusion imaging (HARDI) techniques, both of which increase the potential for nonlinear effects [Tijssen et al., 2009; Cole et al., 2014; Dubois et al., 2014; Elhabian et al., 2014; Kong et al., 2014; Kreilkamp et al., 2015].

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In this study we investigate the effects of using different motion correction techniques within complete DTI processing pipelines that include eddy current and EPI distortion correction. In particular, we examine utilizing: 1) prospective motion correction in real-time during data acquisition; 2) retrospective correction using various computational techniques for volume registration; and 3) the combination of both prospective and retrospective methods. Previous DTI studies have compared the use of prospective and retrospective correction algorithms and reported that the latter tend to result in a significant reduction in fractional anisotropy (FA) parameter values in both gray matter (GM) and white matter (WM), even in the absence of subject motion and for a stationary phantom [Alhamud et al., 2012; Alhamud et al., 2015a]. However, these studies performed motion correction with only one technique, and they also did not look at the inclusion of retrospective correction in conjunction with EPI distortion correction, which has been shown to be important in diffusion scanning [Irfanoglu et al., 2012].

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In the present study we use two DTI acquisition methods (with and without prospective (real-time) subject motion correction during scanning) and two processing pipelines to analyze combinations of prospective and retrospective distortion correcting techniques. This includes comparing two popular, publicly available diffusion-processing tools, FSL-topup [Smith et al., 2004] and TORTOISE [Pierpaoli et al., 2010], for retrospectively correcting distortion effects (subject motion, eddy currents and EPI distortion). We hypothesize that, depending on the specific method, retrospective motion correction can be combined beneficially with prospective motion correction (e.g., using navigators) in order to improve final results of full DTI processing. By analyzing results of processing pipelines with and without explicit retrospective motion correction, one may approximately estimate the size of the effect of its inclusion. To compare the pipeline results, we use both quantitative and qualitative examinations of DTI parameters and probabilistic tractography. Differences between full processing pipelines, depending on the data acquisition and software used, are also presented and discussed. The analyzed group is comprised of pediatric subjects (approximately 7 years of age), as children are likely to engage in incidental motion throughout a scanning session, providing a particular challenge for any data acquisition and analysis [Holdsworth et al., 2012; Theys et al., 2014].

METHODS Author Manuscript

Data acquisition Six healthy children (4F/2M, age 7.20±0.06 yrs) were scanned without sedation as part of an ongoing study using an Allegra 3T MRI (Siemens Healthcare, Erlangen, Germany) in Cape Town, South Africa. Subjects were instructed not to move during scanning, though incidental motion occurred (as expected, given the ages of the subjects). Human subjects approval was obtained from the Human Research Ethics Committees of the University of Cape Town Faculty of Health Sciences and Stellenbosch University. Parents/guardians provided written informed consent and children provided oral assent at the time of scanning. Hum Brain Mapp. Author manuscript; available in PMC 2016 December 01.

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The following whole brain (WB) data were acquired for each subject, as determined by the ongoing study's protocols. A T1-weighted (T1w) anatomical image was acquired in the sagittal plane using a navigated multiecho magnetization prepared rapid gradient echo (MEMPRAGE) sequence [van der Kouwe et al., 2008; Tisdall et al., 2009]: voxel=1.3×1×1 mm3, FOV=224×224×144 mm3, matrix size 168×224×144, TR=2530 ms, TEs=(1.53, 3.19, 4.86, 6.53) ms, TI=1100 ms, flip angle=7 deg. A standard DTI set was acquired using a twice refocused SE-EPI sequence [Reese et al., 2003]: TR/TE=9500/86 ms, voxel=1.964×1.964×2.000 mm3, FOV=220×220×144 mm3, 4 b0 (b=0 s/mm2) volumes and 30 noncollinear gradients with DW factor b=1000 s/mm2. The DTI set consisted of a pair of acquisitions of opposite phase encodings (‘AP-PA’, acquired along anterior-posterior and posterior-anterior directions). Additionally, a navigated (vNav) DTI set was acquired using the same parameters as the standard set but with TR=10026 ms.


Briefly, the navigator was implemented as follows in order to track subject motion and to update the scanner gradient coordinates in real-time. The standard twice-refocused, 2D diffusion pulse sequence [Reese et al., 2003] had previously been modified to perform prospective motion correction by acquiring a single 3D, multishot EPI navigator (526 ms) following the acquisition of each diffusion volume [Alhamud et al., 2012]. Since the navigator is not diffusion weighted, the accuracy of co-registration and motion estimates are not affected by the diffusion gradients, even at high b-values. Before the start of the subsequent diffusion volume, the navigated DTI sequence receives motion parameters resulting from the co-registration of the current navigator to the reference navigator of the first diffusion volume. The rotation matrix, which depends on the slice orientation, is updated to the current position, then the matrix is used to reorient all spatial encoding gradients as well as the diffusion gradients. In this manner, the diffusion table for the next diffusion volume is kept in the correct slice orientation, and therefore the gradient table does not need to be reoriented retrospectively [Alhamud et al., 2012].

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The 3D EPI navigator was implemented with a very small flip angle of 2° to minimize the impact of signal saturation on the diffusion sequence. For fast and efficient motion tracking, with minimal distortion, the navigator's protocol was implemented for each partition in the 3D slab with a low spatial resolution (8×8×8 mm3), with minimal TR (14 ms) and TE (6.6 ms) and with a very high receiver bandwidth (3906 Hz/px). The reacquisition process was activated within the protocol, and up to five corrupted diffusion volumes were reacquired inline when motion exceeded preset thresholds for translation (>2.5 mm) or rotation (≥1 deg). The navigator would cease tracking in cases of translation >20 mm or rotation >8 deg. However, if less than five reacquisitions were activated, then the remaining reacquisitions were applied to the last volume. In each set of reacquired copies, only the last volume was kept for further processing. Data processing Data were processed using a combination of TORTOISE [Pierpaoli et al., 2010], FSL [Smith et al., 2004], AFNI [Cox, 1996], FATCAT [Taylor and Saad, 2013] and in-house scripts, as described below.


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In preparation for DWI registration in TORTOISE, the T1w images were passed through an in-house inversion algorithm to create a volume with approximate T2-weighted (T2w) contrast (which is the recommended format for anatomical reference in this tool). A description of the algorithm, with example images, is shown in Appendix A. This T2w-like volume was used solely for providing TORTOISE with an anatomical reference with requisite contrast similar to the DTI b0 volume, and it did not enter into any further step of processing or comparison. The FSL tools did not require an anatomical reference. Additionally, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) tissue masks were calculated from the T1w images using AFNI-3dSeg for later comparisons.

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For comparison, the two primary DTI tools, FSL-topup (v5.0.8) and TORTOISE (v2.1.0), were implemented separately in this study to provide post-acquisition distortion correction for each diffusion data set. Prior to any processing, all diffusion data were visually inspected, and any corrupted volumes with signal dropout were removed. Then, FSL-topup (TOP) was applied after FSL-eddy_correct. Processing with TORTOISE (TORT) was performed separately in parallel using the DIFF_PREP and DR-BUDDI tools.

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For each processing tool default settings and steps provided by the software's basic help documentation were utilized. The final DWI data produced by the TORTOISE pipeline had 1.5 mm isotropic voxel resolution, as part of the standard settings, and the FSL output remained at 2 mm isotropic resolution. However, parameter distributions and WM maps were compared in each subject's 1 mm isotropic anatomical space, and tractography results were compared in the same standard template space (described below). In the final comparisons there were therefore a total of four processed outputs per subject, each having: either Standard or vNav acquisition, and a standard implementation of either TOP or TORT tools. The labels for each describe the combined acquisition and processing, e.g., ‘vNav_TOP’ is a data set acquired using a navigator and processed with FSL-topup; ‘TORT’ applies to both navigated and non-navigated data sets that were processed in TORTOISE. Analysis and comparisons of data

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After processing, AFNI and FATCAT were used to average the b0 volumes of each data set (for TOP data; the TORT data had been averaged during processing), to fit diffusion tensors (DTs) nonlinearly and to calculate DTI parameters. Additionally, FA uncertainty, ΔFA, and the orientation uncertainty of the first eigenvector toward the orthogonal second and third eigenvectors, Δe12 and Δe13, respectively, were estimated using 3dDWUncert with 300 Monte Carlo iterations. FA maps were linearly warped to each subject's T1w anatomical space using six degrees of freedom (rigid body parameters: three translation and three rotation). Distributions of FA and eigenvector parameters both within the WB and T1wsegmented WM volumes were calculated; in previous studies, comparisons of FA distributions have been used to show the relative amounts of smoothing present in procedures, with more smoothing decreasing FA values (i.e., shifting the distribution leftward) in WM and increasing some FA values in GM (for the latter, particularly near tissue boundaries) [Alhamud et al., 2012; Kreilkamp et al., 2015]. Additionally, WM masks derived from DTI parameter values (where FA>0.2, the standard proxy for WM in adult humans) were compared for overlap with T1w-WM masks, both visually and quantitatively


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using Dice coefficients [Dice, 1945]. In order to examine how physiological noise and nonphysiological factors such as distortion vary throughout the image [Irfanoglu et al., 2012; Walker et al., 2011], the Dice coefficients were calculated separately for each coronal slice throughout the whole brain.

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We also examined the basic alignment quantities calculated by each processing tool, for a comparison of processing features of the analysis tools themselves. Specifically, we investigated the values of the six rigid body registration parameters determined by each program. While each tool was implemented with more degrees of freedom (DOF) in their alignment procedures (e.g., shearing and/or higher order terms), the exact implementations across the programs differ significantly, and therefore only these basic 6 DOF are directly comparable. We also calculated the root mean square (RMS) deviation for each subject, which combines the six rigid body parameters into a single estimator of mean displacement [Jenkinson, 1999; Reuter et al., 2015]:

(1)

where t is the translation vector, M is (here) the 3×3 rotation matrix, I is the identity matrix, tr[] is the trace operator, and r is the approximate spherical radius of the brain (here, estimated from the data to be 65 mm). The rigid body parameters and RMS values of the navigator logs were also examined.


In order to examine the practical consequences of DWI acquisition and processing, network tractography was performed, as this procedure simultaneously depends on ellipsoid orientation and shape, both locally and across regions of voxels. From the anatomical parcellation of the Haskins pediatric template [Molfese et al., 2015], 10 bilateral GM ROIs across the cerebral cortex and associated with the default mode network (DMN) were selected to form a network of targets: the left and right precuneus, posterior cingulate cortex, inferior parietal cortex, medial orbitofrontal cortex and pars opercularis. This GM network was mapped nonlinearly to each subject's T1w anatomical using 3dQwarp, and then with a linear affine transform (12 degrees of freedom) to each subject's native diffusion space. Each region was inflated by one voxel in subject diffusion space using 3dROIMaker to ensure mapped regions maintained contact with FA>0.2 WM (regions already within FA-WM were not expanded, and target ROI overlap was prohibited). Probabilistic tracking was performed using 3dTrackID [Taylor et al., 2012; Taylor and Saad, 2013] with standard settings for all data sets: FA>0.2 threshold; tract length >20 mm; turning angle 8 deg rotation), and therefore only the AP set was navigated for this subject. Whole brain and white matter DTI parameter comparisons

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Fig. 1 displays normalized FA distributions for each of the six subjects (A-F) throughout the WB (upper panel) and within T1w-WM (lower panel). The FA distribution mean and standard deviation values are also presented in Table 1; see the Supporting Information for tables of additional DTI parameter values of the mean diffusivity (MD) and principal eigenvalue (L1). In the WB case the vNav_TORT distributions appear to be slightly leftshifted with respect to the Standard_TORT ones for D-F. Compared to TORT in C-E, TOP distributions tend to be left-shifted. Within the T1w-WM, the vNav and Standard TORT distributions are nearly identical in each subject and are among the most right-shifted. Fig. 2 displays the uncertainty distributions for the angular orientation of the DT's first eigenvector, with the mean (bias; upper panel) and standard deviation (stdev; lower panel) distributions of Δe12 plotted. All bias distributions have a peak around zero, with vNav data producing the narrowest linewidths for each of TOP and TORT. In the low-motion cases ‘AC’, biases were similar across all methods; in ‘D-F’, the Standard acquisitions showed the widest bias distributions. Similarly, the vNav-acquired data showed the most left-shifted peaks among stdev distributions, with vNav_TORT producing the highest peak at the smallest values.

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Fig. 3 provides a quantitative comparison of the overlap of T1w- and FA-WM maps, plotting Dice coefficients for each coronal slice in the brain volume. These direct measures were calculated for each coronal slice and then plotted along the anterior-posterior (i.e., phaseencoding) axis, in order to highlight relative effects of EPI distortion in particular. The vNav_TORT data consistently showed the highest matches across the brain, and Standard_TORT were roughly equal or slightly lower. The vNav_TOP data often showed relatively high Dice values in the middle slices (excepting in C and D), which decreased quickly at the anterior and posterior edges.


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For examples of visual comparison, highlighting locations of mismatches due to either false negatives or positives, the relative overlaps for FA- and T1w-WM masks are displayed for subjects F and C in Figs. 4 and 5, respectively. Three axial slices are shown for each processed data set, with colors labelling relative matching of the FA-WM to the reference T1w-WM: direct overlap in red; false positives in green (i.e. regions incorrectly included as WM on FA masks); false negatives in blue (i.e. WM regions not present on FA masks). Consistent with the Dice coefficients in Fig. 3, in subject ‘F’ the relative amount of overlap and false negatives/positives are generally similar across the data sets. The vNav acquisitions tend to show less false positive (green) than the respective Standard slices. Also in agreement with Fig. 3, the anterior regions of the vNAV_TORT data show the greatest amount of overlap (red) with relatively little false positive/negative.

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In contrast, for subject ‘C’ in Fig. 5, there are much more extensive overlap differences among the different acquisition and processing methods. Standard_TOP shows large sections of false positives and negatives, particularly in the posterior regions, and vNav_TOP shows large regions of false negatives in lateral and anterior/posterior domains. Again, vNav_TORT shows the largest amount of direct overlap with the relatively smallest amount of false negatives and positives. Motion estimates during acquisition and processing

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Fig. 6 presents boxplots comparing a) the six rigid body motion parameters calculated for all of the data sets and b) the total RMS deviation for each subject. Values for each of the phase encoding sets (AP and PA) are shown separately. Rotation values were uniformly much smaller than translation (for example, converting rotation to length units with a rough approximation of a 65 mm head radius), particularly for rotation around the x-axis. Translation in the z-direction tended to have the largest parameter values. As observed in the RMS deviation, subjects ‘C’, ‘D’ and ‘E’ appeared to have the largest overall registration values, suggesting that these subjects exhibited the largest motion.

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The magnitudes of motion correction parameter estimates differed greatly between the software tools. Typically, the mean magnitudes and ranges of estimated motion parameters were much higher for TOP processing than for TORT, in both the Standard and the vNav acquisitions and particularly for the translation parameters. Mean RMS values for TOP were greater than the voxel edge length (2 mm) for each set, while the mean for TORT surpassed that threshold for only one set (PA for ‘E’). For TOP, the means of the vNav RMS values were approximately greater than or equal to those of the Standard data in both the AP and PA sets for subjects ‘A’, ‘B’, ‘C’ and ‘D’. For TORT, the RMS means for vNav tended to be less than those of the Standard acquisition in the AP sets, and greater than or equal for most of the PA sets (though generally both were much less than 1 mm). Additionally, the navigation logs of the vNav data were examined, for raw acquisitions as well as for a selected subset after reacquisition and visual inspection. Fig. 7 shows the RMS values of the logs for all subjects, with the AP- and PA-encoded sets shown separately (NB: the PA log file for subject ‘E’ was not saved; the full times series of the logs are provided in the Supporting Information). Navigator values for all acquired volumes are shown in red; the subset excluding the volumes with large motion detected by the navigator (see Methods) are Hum Brain Mapp. Author manuscript; available in PMC 2016 December 01.

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shown in black; and the final subset (used for analysis) which further excluded any volumes with dropout slices or without a matching AP and PA DW gradient are shown in cyan. These motion profiles are typical of incidental motion, generally being 0.2) for each pipeline were compared to each other, and each was also quantitatively compared to a T1-segmented WM (T1-WM) mask using Dice coefficients [Dice, 1945] of coronal slices across the whole brain.

Results Author Manuscript

Figure A2 shows axial slices of the test set's T2w volume (top row) and the inverted T1w volume (second row), which were separately used as reference volumes during the DWI distortion correction. The relative tissue contrasts are similar, with the T1w volume showing higher contrast magnitudes in some locations. In the lower panels are slices of the TORTOISE processed b0 volumes from each pipeline at the same locations, which show nearly identical contrasts and anatomical patterns.


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Figure A3 shows normalized distributions of the main DTI scalar parameters (FA, MD and L1) for each of the processed sets, both in the WB volume (top row) and within T1segmented WM (bottom row). The distributions are uniformly similar, with the largest differences occurring where FA ≈ 0.2, which is the threshold value for WM boundaries. Figure A4 shows that the differences in DTI-derived WM masks (FA>0.2 masks) when using T2w or inverted T1w volumes are quite small, typically plus or minus within a voxel of the boundary edge. The largest differences predominantly occur in very inferior or superior slices outside of the main cortical regions, as well as locations within one voxel of the border. The Dice coefficient comparisons in Figure A5 between each pipeline's FAderived WM and the T1-segmented WM also show only minor differences, with the largest differences located in the slices at the anteriormost edge.

Discussion and Conclusions Author Manuscript

The DTI results after distortion correction with TORTOISE when using the IRCT T1w volume were quite similar to those obtained using a standard T2w volume. Similarly consistent DTI results were obtained for all subjects in the analysis in the main text, where the inverted volumes were used as reference anatomicals within TORTOISE. The IRCT T1w volume was only used for the specific purpose of aiding alignment within TORTOISE processing, and it is not recommended for use in any other step in the analysis (e.g., tissue segmentation or quantitative comparison).

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It should be noted that the IRCT process depends on having a reliable skull stripping method, and this step in particular may require refinement of options by the user in certain circumstances. In the demo example here and in the data sets in the main text, the default settings of AFNI's 3dSkullStrip were satisfactory, but these could be adjusted easily depending on specific data. Further refinements of the algorithm may include using a squaring or other function on the final inverted T2w volume to decrease the observed tissue contrast, which may increase the similarity of the final volume to the reference b0 volume of the DWI data set. In the present work, this did not appear necessary due to the similarity of observed results, but it may apply to other data sets. In summary, as shown in the main text and figures, the results of the output DTI data provide further evidence that the IRCT volume served as a useful anatomical reference in TORTOISE. This may aid researchers in preprocessing diffusion weighted data in cases where a T2w volume has not been acquired, but only a T1w volume exists as a reference image. Similarly, it may save scanner time when acquiring data, by reducing the number of necessary scans for a study.

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APPENDIX B An additional difference between retrospective methods is that TORTOISE includes a small degree of upsampling as part of its standard procedure, and therefore the final resolutions of the TORT- and TOP-processed DWI sets differed. While there may be varied degrees of smoothing in the final tensor estimates, these marginal effects are likely to be small compared with the effects of correcting subject motion, eddy currents and EPI distortions.


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For example, the Dice coefficients between T1w- and FA-derived WM maps were similar within the medial slices of the low-motion subjects ‘A’ and ‘B’ for all processing streams, instead of showing uniformly systematic differences with final processing resolution. The greatest differences in Dice values for these subjects occurred mainly in the anterior and posterior regions, where larger corrections for EPI distortions would be required. Furthermore, all comparisons were made at a uniform resolution, so that the total smoothing effects would be equivalent: voxelwise comparisons (for FA and Δe12 values) were upsampled to match each subject's anatomical volume, and tractographic results were transformed to the Haskins template space.

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A difference between acquisition approaches is that in three subjects the vNav data had more DWIs for processing than the Standard data (30 vs 19, 25 vs 22 and 29 vs 22), due to a smaller number of dropout volumes and the reacquisition of volumes. However, the subjects with fewer DWIs (due to motion during acquisitions) did not necessarily show uniformly higher registration parameter distributions nor uniformly biased DTI parameters. This is consistent with previous DTI studies in which, for data with high signal-to-noise ratio and well-distributed gradients, parameter values changed slowly with the number of acquired DWIs [Landman et al., 2007; Heiervang et al., 2006]. Additionally, it should be noted that in all cases the final number of DWIs remained much greater than the minimal number needed for tensor estimation, allowing for significant noise reduction in the fitting process. Moreover, within-subject comparisons between the navigated and Standard data showed that the latter did not necessarily have wider uncertainty or bias; the amount of motion present, and the methods for motion correction, had a much stronger influence on results.

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Fig. A1.

An example of the inversion of the relative contrast of tissues (IRCT) method for generating a T2w-like image from a T1w volume. The brightness values of the original volume (A) are ceilinged (B) based on the whole brain distribution. (C) WM and GM tissues are each intensity normalized to approximately uniform intensities. (D) The intracranial volume is extracted, and (E) the linear brightness inversion is performed. The resulting volume has similar relative tissue contrast to an acquired T2w volume, as well as to a standard DTI b0 (a similar slice is shown in panel F).

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Author Manuscript Author Manuscript Fig. A2.

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The T2w and inverse T1w data sets used as anatomical references in TORTOISE are shown in the top rows, respectively (axial slices, left=left). The bottom two rows show each of the average b0 volumes at the same locations after being processed by TORTOISE, and these volumes show a very high degree of similarity.

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Fig. A3.

Normalized distributions of DTI parameters after TORTOISE processing are shown from across the whole brain (WB, top row) and within the T1-segmented WM (bottom row). The results of TORTOISE processing using the inverted T1w volume (red) and the standard T2w volume (black) are nearly identical. Hum Brain Mapp. Author manuscript; available in PMC 2016 December 01.

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Fig. A4.

Dice coefficients showing the overlap in WM masks defined using DTI parameters (where FA>0.2) and T1w segmentation. The Dice coefficient was calculated separately for each coronal slice in the aligned volumes, in order to show any local changes. The results of processing using TORTOISE and either a standard T2w volume (red) or an inverted T1w volume (black) are uniformly high and similar to each other across the brain, with the greatest difference apparent at the posterior-most slices of the brain.

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Fig. A5.

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Axial slices (left=left) compare the overlap of WM masks derived from post-TORTOISE DTI data, where FA>0.2, for pipelines using either a standard T2w volume or an inverted T1w volume. The volumes show a high degree of overlap (red) across the cortex and much of the brain. Small differences (green and blue) are noticeable mainly within one voxel of the GM-WM boundary, and in the most inferior slices, as well as in posterior regions.

REFERENCES

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Aksoy M, Liu C, Moseley ME, Bammer R. Single-step nonlinear diffusion tensor estimation in the presence of microscopic and macroscopic motion. Magn Reson Med. 2008; 59(5):1138–50. [PubMed: 18429035] Alhamud A, Taylor PA, Laughton B, Hasan KM, van der Kouwe AJW, Meintjes EM. Motion artifact reduction in pediatric diffusion tensor imaging using fast prospective correction. J Magn Reson Imaging. 2015a; 41(5):1353–64. [PubMed: 24935904] Alhamud A, Taylor PA, van der Kouwe AJW, Meintjes EM. Real-time measurement and correction of both B0 changes and subject motion in diffusion tensor imaging using a double volumetric navigated (DvNav) sequence. NeuroImage. 2015b in press. Alhamud A, Tisdall MD, Hess AT, Hasan KM, Meintjes EM, van der Kouwe AJW. Volumetric navigators for real-time motion correction in diffusion tensor imaging. Magn Reson Med. 2012; 68:1097–1108. [PubMed: 22246720] Andersson JL, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003; 20(2):870–88. [PubMed: 14568458] Ardekani S, Sinha U. Geometric distortion correction of high-resolution 3 T diffusion tensor brain images. Magn Reson Med. 2005; 54(5):1163–71. [PubMed: 16187289] Benner T, van der Kouwe AJW, Sorensen AG. Diffusion imaging with prospective motion correction and reacquisition. Magn Reson Med. 2011; 66:154–167. [PubMed: 21695721] Cole JH, Farmer RE, Rees EM, Johnson HJ, Frost C, Scahill RI, Hobbs NZ. Test-Retest Reliability of Diffusion Tensor Imaging in Huntington's Disease. PLOS Currents Huntington Disease. 2014; 6 pii: ecurrents.hd.f19ef63fff962f5cd9c0e88f4844f43b. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996; 29:162–173. 1996. [PubMed: 8812068] Dice L. Measures of the amount of ecologic association between species. Ecology. 1945; 26:207–302. Dubois J, Kulikova S, Hertz-Pannier L, Mangin JF, Dehaene-Lambertz G, Poupon C. Correction strategy for diffusion-weighted images corrupted with motion: application to the DTI evaluation of infants’ white matter. Magn Reson Imaging. 2014; 32(8):981–92. [PubMed: 24960369] Elhabian S, Gur Y, Vachet C, Piven J, Styner M, Leppert IR, Pike GB, Gerig G. Subject-Motion Correction in HARDI Acquisitions: Choices and Consequences. Front Neurol. 2014; 9(5):240. doi: 10.3389/fneur.2014.00240. Graham MS, Drobnjak I, Zhang H. Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques. Neuroimage. 2016; 125:1079–94. [PubMed: 26549300] Heiervang E, Behrens TEJ, Mackay CE, Robson MD, Johansen-Berg H. Between session reproducibility and between subject variability of diffusion MR and tractography measures. NeuroImage. 2006; 33:867–877. [PubMed: 17000119] Holdsworth SJ, Aksoy M, Newbould RD, Yeom K, Van AT, Ooi MB, Barnes PD, Bammer R, Skare S. Diffusion tensor imaging (DTI) with retrospective motion correction for large-scale pediatric imaging. J Magn Reson Imaging. 2012; 36(4):961–71. [PubMed: 22689498] Holland D, Kuperman JM, Dale AM. Efficient correction of inhomogeneous static magnetic fieldinduced distortion in Echo Planar Imaging. Neuroimage. 50:175–183. Irfanoglu MO, Walker L, Sarlls J, Marenco S, Pierpaoli C. Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage. 2012; 61(1):275–88. [PubMed: 22401760]


Taylor et al.

Page 21

Author Manuscript Author Manuscript Author Manuscript Author Manuscript

Irfanoglu MO, Nayak A, Jenkins J, Hutchinson EB, Sadeghi N, Thomas CP, Pierpaoli C. DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures. Neuroimage. 2016; (132):439–454. Jenkinson M. Measuring transformation error by RMS deviation. Tech. Rep. TR99MJ1, Oxford Center for Functional Magnetic Resonance Imaging of the Brain (FMRIB. 1999 Jezzard P, Barnett AS, Pierpaoli C. Characterisation of and correction for eddy current artefacts in echo planar diffusion imaging. Magn Reson Med. 1998; 39:801–812. [PubMed: 9581612] Kober T, Gruetter R, Krueger G. Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain. NeuroImage. 2012; 59:389–398. [PubMed: 21763773] Kong XZ, Zhen Z, Li X, Lu HH, Wang R, Liu L, He Y, Zang Y, Liu J. Individual differences in impulsivity predict head motion during magnetic resonance imaging. PLoS One. 2014; 22(9) (8):e104989. doi: 10.1371/journal.pone.0104989. Kreilkamp BA, Zacà D, Papinutto N, Jovicich J. Retrospective head motion correction approaches for diffusion tensor imaging: Effects of preprocessing choices on biases and reproducibility of scalar diffusion metrics. J Magn Reson Imaging. 2015 doi: 10.1002/jmri.24965. [Epub ahead of print]. Landman BA, Farrell JA, Jones CK, Smith SA, Prince JL, Mori S. Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. Neuroimage. 2007; 36(4):1123–38. [PubMed: 17532649] Lazar M, Alexander AL. An error analysis of white matter tractography methods: synthetic diffusion tensor field simulations. Neuroimage. 2003; 20(2):1140–1153. [PubMed: 14568483] Le Bihan D, Poupon C, Amadon A, Lethimonnier F. Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging. 2006; 24(3):478–88. [PubMed: 16897692] Leemans A, Jones DK. The B-Matrix Must Be Rotated When Correcting for Subject Motion in DTI Data. Magn Reson Med. 2009; 61:1336–1349. [PubMed: 19319973] Ling J, Merideth F, Caprihan A, Pena A, Teshiba T, Mayer AR. Head injury of head motion? Assessment and quantification of motion artifacts in diffusion tensor imaging studies. Hum Brain Mapp. 2012; 33:50–62. 2012. [PubMed: 21391258] Molfese PJ, Glen D, Mesite L, Pugh K, Cox RW. The Haskins pediatric brain atlas. 21st Annual Meeting of the Organization for Human Brain Mapping (OHBM). 2015 Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol. 2008; 29(5):843–52. 2008. [PubMed: 18339719] Norris DG. Implications of bulk motion for diffusion-weighted imaging experiments: Effects, mechanisms, and solutions. J Magn Reson Imaging. 2001; 13:486–495. [PubMed: 11276093] Pierpaoli, C.; Walker, L.; Irfanoglu, MO.; Barnett, A.; Basser, P.; Chang, L-C.; Koay, C.; Pajevic, S.; Rohde, G.; Sarlls, J.; Wu, M. TORTOISE: An Integrated Software Package for Processing of Diffusion MRI Data.. ISMRM (2010) 18th Annual Meeting; Stockholm, Sweden. 2010. p. 1597 Reese T, Heid O, Weisskoff R, Wedeen V. Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magn Reson Med. 2003; 49:177–182. [PubMed: 12509835] Reuter M, Tisdall MD, Qureshi A, Buckner RL, van der Kouwe AJ, Fischl B. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage. 2015; 107:107–15. [PubMed: 25498430] Rohde GK, Barnett AS, Basser PJ, Marenco S, Pierpaoli C. Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn Reson Med. 2004; 51(1):103–14. [PubMed: 14705050] Smith S, Jenkinson M, Woolrich M, Beckmann C, Behrens T, Johansen-Berg H, Bannister P, De Luca M, Drobnjak I, Flitney D, Niazy R, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady J, Matthews P. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004; 23:208–219. Taylor PA, Cho KH, Lin CP, Biswal BB. Improving DTI tractography by including diagonal tract propagation. PLoS One. 2012; 7:e43415. [PubMed: 22970125]


Taylor et al.

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Taylor PA, Saad ZS. FATCAT: (an efficient) Functional And Tractographic Connectivity Analysis Toolbox. Brain Connect. 2013; 3:523–525. [PubMed: 23980912] Theys C, Wouters J, Ghesquière P. Diffusion Tensor Imaging and Resting-State Functional MRIScanning in 5- and 6-Year-Old Children: Training Protocol and Motion Assessment. PLoS ONE. 2014; 9(4):e94019. doi:10.1371/journal.pone.0094019. [PubMed: 24718364] Tijssen RH, Jansen JF, Backes WH. Assessing and minimizing the effects of noise and motion in clinical DTI at 3 T. Hum Brain Mapp. 2009; 30(8):2641–55. [PubMed: 19086023] Tisdall M, Hess AT, van der Kouwe AJ. MPRAGE using EPI navigators for prospective motion correction. Proc. Int. Soc. Magn Reson Med. 2009; 29(17):4656. van der Kouwe AJW, Benner T, Salat DH, Fischl B. Brain morphometry with multiecho MPRAGE. Neuroimage. 2008; 40:559–569. 2008. [PubMed: 18242102] Walker L, Chang L-C, Koay CG, Sharma N, Cohen L, Ragini V, Pierpaoli C. Effects of physiological noise in population analysis of diffusion tensor MRI data. NeuroImage. 2011; 54:1168–1177. [PubMed: 20804850] Wu M, Chang LC, Walker L, Lemaitre H, Barnett AS, Marenco S, Pierpaoli C. Comparison of EPI distortion correction methods in diffusion tensor MRI using a novel framework. MICCAI. 2008; 11(2):321–9. [PubMed: 18982621]


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Fig. 1.

Normalized distributions of FA values in the whole brain (WB; top) and in T1w-WM (bottom) for all six subjects (A-F). In the WB cases Standard and TOP distributions tended to be the most rightward. In T1w-WM TORT results were the least left-shifted, suggesting that these sets had the least amount of smoothing.


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Fig. 2.

Normalized distributions of bias (top) and standard deviation (bottom) of the angular uncertainty, Δe12 (i.e., the first eigenvector projected along the second eigenvector), in T1wWM for the same subjects shown in Fig. 1.


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Author Manuscript Fig. 3.

Author Manuscript

Dice coefficients of overlap between WM masks defined using FA and T1w images (same subjects as Figs. 1-2). Individual Dice coefficients were calculated for each coronal slice in the overlaid volumes. Dice values are relatively constant across the volumes, with vNav_TORT consistently showing the highest values.


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Author Manuscript

Comparison of FA (>0.2) and T1w (segmented) WM for subject ‘F’, whose Dice coefficient curves were similar across acquisition and processing pipelines (Fig. 3). Locations where the FA-WM overlaps with T1w-WM are shown in red, with false positive and negative FA-WM shown in green and blue, respectively. In each panel the axial slices are arranged inferior (left) to superior (right). Standard images tend to show a relatively larger number of false positives.


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Comparison of FA (>0.2) and T1w (segmented) WM for subject C, whose Dice coefficient curves showed significant variation among the acquisition and processing pipelines (Fig. 3). Locations where the FA-WM overlaps with T1w-WM are shown in red, with false positive and negative FA-WM shown in green and blue, respectively. In each panel the axial slices are arranged inferior (left) to superior (right). Standard_TOP images show systematic differences in WM locations.


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Author Manuscript Author Manuscript Fig. 6.

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a) Rigid body registration parameters (translation in mm and rotation in deg) for each subject as estimated by TOP and TORT programs. Results for each phase encoding direction (AP and PA) are shown separately. The mean of each distribution is shown with a black line, the color block covers the 25-75 percentile interval, whiskers extend to 1.5× the interquartile range, and dots represent outliers. In nearly every case, the TOP distributions have the largest magnitude of mean and widest extent. b) Distributions of the overall root mean square (RMS) deviations of the registration parameters are shown for each subject. Subjects ‘C’, ‘D’ and ‘E’ exhibit the largest values; in the latter case, large registration values appear even in the navigated data set.

Author Manuscript Hum Brain Mapp. Author manuscript; available in PMC 2016 December 01.

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Overall root mean square (RMS) deviations of the rigid body subject motion parameters, as estimated by the navigator during data acquisition. Results for each phase encoding direction (AP and PA) are shown separately. Values for all acquired volumes are shown in red; volumes in black were kept after accounting for reacquisition (i.e., eliminating repetitions due either to excessive motion or post-acquisition protocol; see Methods); and the set of cyan volumes were used for analysis after visual examination for dropout slices. Subjects ‘C’ and ‘E’ exhibited the largest amount of motion, with several outliers filtered out from each set for final analysis.


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Author Manuscript

The top panel shows a map of the target ROIs (based on the default mode network) in the same slice, with each cortex labeled with a unique color: medial orbitofrontal (red), posterior cingu-late (green), precuneus (blue) and inferior parietal (violet). To examine the similarities and differences in tractographic results for each processing method, the mask of each subject's estimated intra-network WM has been summed across the group in panels ‘a-d’. Regions in the summation maps where all group members exhibited WM are shown in red, and regions where only one subject had WM are in blue. Locations of high sensitivity for individual methods are highlighted with magenta arrows. In each panel sagittal images are arranged medial (left) to lateral (right). Quantitative comparisons of the summed masks are provided in Fig. 9.


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Quantitative comparisons of ‘summation maps’ of tractographic results. All masks of intranetwork connections were mapped from each diffusion space to the Haskins pediatric template to create ‘summation maps’ (shown in Fig. 8). Panel A shows the number of tracked voxels shared across a given percent of the group are shown. Panel B shows the same values, scaled by the number of nonzero values in the summation mask.


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Table 1

Author Manuscript

Basic DTI set characteristics. The number of DWIs (NDWI) in each subject's final, analyzed set is shown for both the vNav and Standard acquisitions. Additionally, the means and standard deviations (stdev) of FA values within WB and T1w-WM distributions (see Fig. 1) are presented for each subject and processing stream. The Supporting Information provides tables of mean diffusivity (MD) and principal eigenvalue (L1) values. Subject ID A

B

C

D

E

F

vNav

29

30

28

30

25

29

Standard

29

30

29

19

22

22

NDWI

FA in WB (mean ± stdev)

Author Manuscript

vNav_TORT

0.23±0.16

0.23±0.15

0.22±0.15

0.24±0.16

0.25±0.16

0.22±0.15

vNav_TOP

0.21±0.15

0.22±0.14

0.18±0.12

0.23±0.15

0.23±0.15

0.21±0.14

Standard_TORT

0.22±0.16

0.23±0.15

0.22±0.15

0.26±0.15

0.27±0.16

0.24±0.16

Standard_TOP

0.20±0.15

0.22±0.14

0.20±0.14

0.22±0.14

0.23±0.15

0.22±0.14

FA in T1w-WM (mean ± stdev) vNav_TORT

0.36±0.17

0.34±0.17

0.33±0.16

0.35±0.18

0.36±0.17

0.34±0.18

vNav_TOP

0.32±0.16

0.31±0.16

0.25±0.15

0.29±0.18

0.33±0.17

0.31±0.16

Standard_TORT

0.36±0.17

0.34±0.17

0.33±0.17

0.36±0.17

0.37±0.17

0.36±0.17

Standard_TOP

0.32±0.16

0.31±0.16

0.27±0.16

0.31±0.16

0.29±0.17

0.32±0.17


Prospective motion correction of segmented diffusion weighted EPI.

Evaluation of EPI distortion correction methods for quantitative MRI of the brain at high magnetic field.

Correction of B 0-induced geometric distortion variations in prospective motion correction for 7T MRI.

Nonlinear distortion correction of diffusion MR images improves quantitative DTI measurements in glioblastoma.

Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction.

Highly accelerated Point-Spread Function mapping based on Finite Rate of Innovation for EPI distortion correction.

Motion correction in MRI of the brain.

Motion correction for functional MRI with three-dimensional hybrid radial-Cartesian EPI.

Reverse retrospective motion correction.

Free-breathing myocardial T2* mapping using GRE-EPI and automatic non-rigid motion correction.

Correction strategy for diffusion-weighted images corrupted with motion: application to the DTI evaluation of infants' white matter.

Motion correction in optoacoustic mesoscopy.

Homogeneous coordinates in motion correction.

An investigation of motion correction algorithms for pediatric spinal cord DTI in healthy subjects and patients with spinal cord injury.

Joint correction of Nyquist artifact and minuscule motion-induced aliasing artifact in interleaved diffusion weighted EPI data using a composite two-dimensional phase correction procedure.

Computer vision distortion correction of scanning probe microscopy images.

Presence of and correction for interviewer error on an instrument assessing cognitive function of older adults.

Simulation of the influence of magnetic field inhomogeneity and distortion correction in MR imaging.

Harmonic analysis for the characterization and correction of geometric distortion in MRI.

PET motion correction using MR-derived motion parameters.

PET motion correction using PRESTO with ITK motion estimation.

moco: Fast Motion Correction for Calcium Imaging.

Quantitative framework for prospective motion correction evaluation.

Correction on the distortion of Scheimpflug imaging for dynamic central corneal thickness.