Diffusion Tensor Imaging of the Brain 34

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Diﬀ usion Tensor Imaging of the Brain

379

T. Benner, PhD; R. Wang, MS; V. J. Wedeen, MD

MGH/MIT/HMS Athinoula A. Martinos Center for Functional and Structural Biomedical Imaging, Building 149 13th Street (2301), Charlestown, MA 02129, USA

C O N T E N T S

34.1 Introduction 379

34.2 Beneﬁ ts of Parallel Imaging 380 34.2.1 Reduced Distortions 380 34.2.2 Faster Imaging 380 34.2.3 Higher Resolution 382

34.3 DTI Protocols 386

34.4 Fiber Tracking 386

34.4.1 Methodical Considerations 386 34.4.2 Clinical Applications 388

34.5 Conclusion 391

References 391

Diffusion Tensor Imaging of the Brain 34

Thomas Benner, Ruopeng Wang and Van J. Wedeen

34.1 Introduction

The basic principles of simple, i.e., non-tensor diffusion imaging (Wesbey et al. 1984; Le Bihan 1991; Basser 1995) have been known for many years and since then have been used most frequently in the ﬁ eld of acute cerebral ischemia (Le Bihan et al. 1988; Moseley et al. 1990); cf. Chap. 33. Diffusion-anisotropy and diffu- sion-tensor imaging (DTI) were introduced only years later (Basser et al. 1994; Pierpaoli and Basser 1996;

Mattiello et al. 1997). Diffusion imaging – non- tensor and tensor – and mapping of apparent diffu- sion coefﬁ cient (ADC) and fractional anisotropy (FA) has become a widespread tool in many research ﬁ elds

such as neurodevelopment (Neil et al. 2002; Beau- lieu et al. 2005; Snook et al. 2005), aging (Nusbaum et al. 2001; Moseley 2002; Salat et al. 2005), degen- erative diseases (Shiga et al. 2004; Choi et al. 2005;

Fellgiebel et al. 2005), tumors (Cruz and Sorensen 2005; Nguyen et al. 2005; Nimsky et al. 2005) and stroke (Warach et al. 1992; Hossmann and Hoehn- Berlage 1995; Baird and Warach 1998; Sotak 2002).

With the availability of diffusion-tensor imaging, ﬁ ber tracking or tractography of white-matter ﬁ ber tracts was made possible (Wedeen et al. 1996; Conturo et al. 1999; Mori et al. 1999). More recently, advanced dif- fusion-imaging techniques such as high-angular-reso- lution diffusion imaging (HARDI), q-ball imaging, q- space imaging and diffusion-spectrum imaging have gained popularity, but are not yet widespread, mostly because of the high hardware-performance require- ments and the long scan times required (Wedeen et al. 2000; Frank 2001; Basser 2002; Tournier et al.

2004; Tuch 2004; Wedeen et al. 2005).

Especially for ﬁ ber tracking, image quality is a major concern. One of the main problems is image distortion caused by eddy currents induced by the diffusion gra- dients. Minimization of these eddy-current artefacts at the time of acquisition is important and can be done on the hardware level, e.g., by using speciﬁ cally opti- mized gradient coils and on the software level, e.g., by using a twice-refocused spin-echo sequence (Reese et al. 2003). Further post-processing might be required to correct for remaining eddy-current artefacts as well as subject motion. Software tools are available for this purpose, e.g., the Oxford Centre for Functional Mag- netic Resonance Imaging of the Brain (FMRIB) Soft- ware Library (FSL) (Jenkinson and Smith 2001) or Automated Image Registration (AIR) (Woods et al.

1998). However, such tools are not yet integrated into

the clinical workﬂ ow. Dependent on the area of inter-

est and the spatial resolution, pulsatile motion of the

brain has to be taken into account (Norris 2001). Use

of cardiac triggering might therefore be necessary for

some applications at the cost of increased scan time.

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34.2 Beneﬁ ts of Parallel Imaging

With the availability of multi-channel receive coils, new parallel-imaging methods such as GRAPPA and SENSE were introduced (Pruessmann et al. 1999;

Griswold et al. 2002), cf. Chap. 2. These methods provide a number of beneﬁ ts for their use in DTI with echo-planar-imaging (EPI) techniques: reduction of susceptibility artefacts, faster imaging, and the feasi- bility of acquisitions with high spatial resolution.

34.2.1 Reduced Distortions

One major beneﬁ t of parallel imaging is the reduc- tion of distortions caused by susceptibility artefacts as shown in Figs. 34.1, 34.2 and 34.3; cf. Chap. 10. The data for these ﬁ gures was acquired on a 1.5-T Siemens Avanto whole-body MR scanner (Siemens Medical Solutions, Erlangen, Germany) using a custom-built head coil with 23 receive elements (Wiggins et al.

2005). This coil provides a signiﬁ cantly better signal- to-noise ratio (SNR) at the cortex and still improved SNR for deep brain structures. The study was done on a normal subject using auto-align for slice positioning (van der Kouwe et al. 2005). As a result of the different echo times and repetition times for non-accelerated and accelerated scans, the number of non-diffusion- weighted images and diffusion-weighted images was adjusted for the three scans to result in approximately the same scan time for all three scans (see Table 34.1 for a complete list of imaging parameters).

Figure 34.1 shows two slices of mean non-diffu- sion-weighted images and color-coded FA maps for scans done without acceleration and with accelera- tion factors R=2 and R=3, respectively. Severe distor- tions can be seen in the non-accelerated images (left column) in areas of air-tissue interfaces, i.e., near the brain stem, the ear canals and the frontal sinus. An increase of the acceleration factor decreases the dis- tortions (middle and right column). This can also be observed in the color-coded FA maps as well as in the boxoid representation (Fig. 34.2), which are cal- culated from the whole data set and therefore exhibit the same distortion artefacts. Note also, that due to the comparable scan times SNR is similar for non- accelerated scans and accelerated scans. Although the effect of reduced distortions is not visible in ﬁ ber tracking as shown in Fig. 34.3, the same image distor-

tions shown in Figs. 34.1 and 34.2 affect the location and direction of the ﬁ ber tracts.

34.2.2 Faster Imaging

Another advantage of using parallel imaging in EPI is the resulting shorter echo times, which in turn result in higher signal and shorter repetition times.

Without acceleration, scan times would be about 15%

to 60% longer as compared to an acceleration factor R=2 (Table 34.2). For DTI, this translates into either shorter scan times, e.g., to achieve ultra-fast imaging, or into more coverage at the same scan duration, or into more acquisition volumes, i.e., a higher number of non-diffusion-weighted and diffusion-weighted images. The latter has been shown beneﬁ cial for the robust estimation of anisotropy, tensor orientation, and mean diffusivity (Jones 2004).

Figure 34.4 shows the images from an ultra-fast DTI scan with whole-brain coverage acquired in only 20 s (see Table 34.2 for imaging parameters). This full tensor acquisition allows the calculation of FA maps as well as ﬁ ber tracking. However, given the very anisotropic voxel size and low number of dif- fusion-gradient directions, any ﬁ ber-tracking result would be sub-optimal. Without acceleration, the scan time for a similar tensor acquisition would increase

Table 34.1. Protocol parameters for DTI scans shown in Figs. 34.1, 34.2, 34.3 and 34.7

Acceleration factor (R) 1 2 3

Parallel-imaging algorithm - GRAPPA GRAPPA Number of reference lines - 30 60

TR (s) 9.2 7.2 6.6

TE (ms) 89 77 74

Number of slices 60 60 60

FOV (mm) 256 256 256

Slice thickness (mm) 2.0 2.0 2.0

Gap (%) 0.0 0.0 0.0

Matrix size 128 128 128

b-value (s/mm²) 700 700 700

Bandwidth (Hz/Px) 1628 1628 1628

Echo spacing (ms) 0.69 0.7 0.72

Non-diff.-weighted images 8 10 11

Diff.-weighted images 48 60 66

Number of averages 1 1 1

Acquisition time (min:s) 8:44 8:31 8:55

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381

Fig. 34.1. Two slices of mean non-diffusion-weighted images (top panel) and color-coded fractional anisotropy (FA) maps (bottom panel) for non-accelerated (left column) and accelerated scans with factor R=2 (middle column) and R=3 (right column). Notice the effect of parallel-imaging accelera- tion in typical areas of susceptibility distortions near the brain stem, ear canals, and frontal sinus.

Distortion artefacts decrease with increasing acceleration factor. Also notice decrease in signal- to-noise ratio with increasing acceleration factor

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Fig. 34.2. Detailed view of images shown in Fig. 34.1 in boxoid representation. Non-accelerated scan is shown in left column; accelerated scans using factor R=2 and R=3 are shown in the middle and right column, respectively. Each boxoid per voxel represents the major eigenvector in direction as well as color coding. Notice effect of susceptibility artefacts on boxoid orientation in the anterior part of the brain stem (top row) as well as slight change in shape (bottom row). Overall, tensor orientation in all images looks comparable

by 30%. This difference in duration can be important in a clinical setting where shorter scan durations allow higher patient throughput and better image quality for patients who are likely to move during a long acquisition. While SNR is somewhat lower in the images from the accelerated scan, the overall image quality is comparable to the data without accelera- tion. Additionally, the accelerated data beneﬁ t from reduced distortion artefacts as described before.

34.2.3 Higher Resolution

Because of the long readout times for large matrix

sizes, single-shot EPI is usually limited to maximum

matrix sizes of around 128×128 and to maximum

isotropic spatial resolutions of 2×2×2 mm³. Parallel

imaging allows pushing this limit to larger matrix sizes

and therefore to higher spatial resolutions. Figure 34.5

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383

Fig. 34.3a–c. Region-of-interest-based ﬁ ber tracking for non-accelerated scan a and accelerated scans using factor R=2 b and R=3 c, respectively. View is from anterior-right-superior. Identical regions were positioned in the brain stem. Im- ages show good visual agreement of ﬁ ber tracks for all three scans

b a

c

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Table 34.2. Protocol parameters for DTI scans, some of which are shown in the ﬁ gures. Scan time increase for acceleration factor R=1 compared to R=2

Ultra-fast Clinical Very high res. HARDI DSI

Acceleration factor (R) 1 2 1 2 1 2 1 2 1 2

Parallel imaging algorithm - GRAPPA - GRAPPA - GRAPPA - GRAPPA - GRAPPA

Number of reference lines - 30 - 30 - 48 - 42 - 44

TR (s) 3.3 2.5 3.7 2.9 17.5 11.1 10.9 8.9 5.3 4.7

TE (ms) 86 73 94 82 145 97 129 111 159 148

Number of slices 21 21 23 23 64 64 60 60 25 25

FOV (mm) 230 230 220 220 208 208 211 211 192 192

Slice thickness (mm) 5.0 5.0 5.0 5.0 1.0 1.0 2.2 2.2 2.0 2.0

Gap (%) 30.0 30.0 20.0 20.0 0.0 0.0 0.0 0.0 0.0 0.0

Matrix size 128 128 128 128 208 208 96 96 96 96

b-value (s/mm²) 1,000 1,000 1,000 1,000 700 700 3,000 3,000 8,500 8,500

Bandwidth (Hz/Px) 1,446 1,446 1,628 1,628 1,046 1,046 1,628 1,628 1,628 1,628

Echo spacing (ms) 0.76 0.78 0.69 0.7 1.03 1.04 0.69 0.7 0.69 0.7

Non-diff.-weighted images 1 1 5 5 10 10 1 1 1 1

Diff.-weighted images 6 6 30 30 60 60 122 122 514 514

Number of averages 1 1 1 1 1 1 1 1 1 1

Acquisition time (min:s) 0:26 0:20 2:13 1:44 20:43 13:08 22:33 18:25 45:35 40:25 Scan-time increase at R=1

compared to R=2 (%)

30 28 58 22 13

Fig. 34.4. Mean non-diffusion-weighted image, mean diffusion-weighted image, apparent-diffusion-coefﬁ cient (ADC) map and fractional-anisotropy (FA) map (from left to right) using no acceleration (top row) and using an acceleration factor R=2 (bot- tom row). The scan times for a full tensor scan were 26 s and 20 s, respectively. Notice slight loss in signal-to-noise ratio for the accelerated scan, but otherwise comparable image quality. Background noise in FA maps is caused by automatic threshold and using intensity normalization. See Table 34.2 for complete acquisition parameters

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385

Fig. 34.5. Mean non-diffusion-weighted images, mean diffusion-weighted images and color-coded fractional anisotropy (FA) map (top row, left to right) from one slice of a high-resolution scan with 1×1×1-mm³ isotropic resolution as well as whole- brain ﬁ ber tracking. Only ﬁ bers intersecting two transverse slices are shown for clarity. View is from anterior-right-superior.

Note detail in mean non-diffusion-weighted image and clear delineation between gray and white matter. Increased signal intensity in the middle of the brain most prominently in the mean diffusion-weighted image is due to intensity normalization. Noisy FA map can be attributed to low signal-to-noise ratio given the high spatial resolution. See Table 34.2 for complete acquisition parameters

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shows the results of a 1-mm³ isotropic DTI scan using an acceleration factor R=2 acquired within 13 min (see Table 34.2 for imaging parameters). Without accelera- tion, the acquisition time would increase by nearly 60% to over 20 min due to an increase of TE from 97 ms to 145 ms and a resulting increase of TR from 11.1 s to 17.5 s. At these spatial resolutions and the given scan time, the coverage in this example is lim- ited to a thin slab of only 64 mm. While the SNR is low – as can be appreciated most easily in the FA map – the mean non-diffusion-weighted and mean diffusion-weighted images show fi ne detail and good contrast, e.g., between grey and white matter. Fiber tracking – while limited to a thin slab – resolves more detail and takes advantage of reduced partial-volume effects. Of course, such experiments would strongly benefi t from higher SNR, which could be gained by using MR scanners at higher fi eld strength without an increase in scan time.

34.3 DTI Protocols

Tables 34.1 and 34.2 list the protocol parameters for a number of DTI scans, ranging from ultra-fast 20-s acquisitions over clinical-style protocols to research protocols used for high-resolution imaging, FA map- ping and ﬁ ber tracking. An ultra-fast acquisition with an acquisition time of less than 30 s is ideally suited for emergency-room patients or uncoopera- tive patients where a routine DTI scan over 2 to 3 min would not be feasible or would result in unusable images. A clinical-style scan can be done with accel- eration in 1:44 min compared to 2:13 min without acceleration – an increase of nearly 30%.

Accurate estimation of FA and tensor at isotropic resolution (here 2.0×2.0×2.0 mm³) with whole-brain coverage requires scan times in the order of 10 min.

Data from these scans are better suited for co-registra- tion to other scan types, e.g., MPRAGE scans, because of sufﬁ cient SNR and the isotropic spatial resolution.

Co-registration is necessitated in most studies that investigate changes in FA over time or differences in FA over patient groups. Furthermore, these data allow more accurate tensor-based ﬁ ber tracking because of the larger number of diffusion-weighted volumes.

More sophisticated techniques like high-angular- resolution diffusion imaging (HARDI), q-space imag- ing, q-ball imaging and diffusion-spectrum imaging

(DSI) allow estimation of more than one ﬁ ber orienta- tion per voxel as they are typically found in the sam- pled voxels at currently used voxel sizes (Wedeen et al.

2000; Frank 2001; Basser 2002; Tournier et al. 2004;

Tuch 2004; Wedeen et al. 2005). Figure 34.6 shows an example of the same anatomical region represented as diffusion tensor boxoids and DSI probability-density functions (PDFs). While the tensor representation only resolves one major diffusion direction as the average of the ﬁ ber populations in that voxel, the PDF represen- tation resolves multiple ﬁ ber orientations per voxel.

Crossings of fi bers can therefore not be resolved using a simple tensor model, but require more sophisticated imaging techniques like DSI and the corresponding processing methods. Since fi ber crossings are abun- dant in the human brain, only a fraction of all fi bers can be found using the tensor model, i.e., mostly major fi ber tracts with a very dominant fi ber orientation per voxel. Furthermore, a higher number of anatomically incorrect fi bers are found due to the restrictions of the tensor model.

The duration of q-ball or DSI scans is in the range of 20 min to 1 h due to the high b-values (>3,000 s/

mm

²

) and the large number of diffusion-gradient directions that are required. Full brain coverage can often not be achieved in reasonable scan times at a spatial resolution that still provides sufﬁ cient ana- tomical detail. MR-scanner gradient performance is the major limiting factor for these types of scans. To make these scans possible in a clinically acceptable time, MR gradient performance has to increase sig- niﬁ cantly. A typical whole-body clinical MR scanner today offers a gradient strength of about 40 mT/m and a slew rate of about 200 T/m/s. To minimize the diffusion encoding time and therefore scan time, gra- dient coils with twice or more the currently available gradient strength are required. This performance can be achieved with switchable body gradient coils or special head-only gradient coils.

34.4 Fiber Tracking

34.4.1 Methodical Considerations

Tensor-model-based ﬁ ber tracking relies on the

assumption that the principal eigenvector per voxel

represents the orientation of the dominant axonal

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387

Fig. 34.6a,b. Detailed view of diffusion tensor boxoid representation a compared to diffusion spectrum imaging probability density function (PDF) representation b of the same anatomical region. Note the multiple separate peaks of the PDFs while the boxoids only show the average diffusion direction of the ﬁ ber population found in that voxel

b a

(Fig. 34.7). This can be done by either limiting the seed points to only a region of interest or by retro- spective selection of ﬁ bers from the whole-brain ﬁ ber-tracking results according to certain criteria.

These criteria might require that fi bers cross a cer- tain slice or a number of slices or that fi bers have a certain length or orientation or correlate to each other (Wedeen et al. 2005). A further help is the color coding of fi bers, which can either be done on a seg- ment-by-segment basis or using the same color for all segments of a fi ber to allow natural grouping of fi ber bundles. Colors can be assigned according to a local measure like FA, ADC, or curvature or according to a global measure like overall orientation or curvature.

Figures 34.5, 34.7 and 34.8 show applications of some of these criteria.

Currently, most fi ber-tracking algorithms and vis- ualization tools do not provide features to assess the uncertainty of fi ber orientation. However, uncertainty can be large in areas of, e.g., low SNR, eddy-current artefacts, motion artefacts or pulsatile motion. There- fore, measures of fi ber-orientation uncertainty are important. One approach uses the bootstrap method to assign confi dence values to results obtained with deterministic tracking algorithms (Jones and Pierpaoli 2005).

While ﬁ ber tracking can provide insight into indi- vidual normal or diseased anatomy – mostly by visual ﬁ bers in that voxel. Once the tensors are calculated

from the diffusion data, fi ber tracking (or tractog- raphy) can be performed. The most frequently used technique for fi ber tracking uses line propagation to create fi ber tracts like that used in FACT (fi ber assign- ment by continuous tracking) (Mori et al. 1999;

Xue et al. 1999). Starting from a seed point, a line is propagated by following the local principal-eigenvec- tor direction. A sub-voxel step size is required for successful fi ber tracking. Line propagation is usually terminated based on local criteria like the anisotropy or the angle between adjacent fi ber segments. Voxels with low anisotropy are found in CSF and gray matter where no or little coherent axonal fi ber orientation is found. An appropriate angle threshold depends on the region of interest and the spatial resolution.

Fiber tracking can beneﬁ t from parallel imaging in a number of ways: reduced distortions result in ﬁ ber tracts that more closely match true anatomy as well as other, less distorted images; faster imaging allows more diffusion directions to be sampled resulting in a more robust tensor estimation and enables advanced imaging techniques like HARDI, q-ball imaging, and DSI; higher spatial resolution reduces partial-volume effects and results in a more detailed depiction of the anatomy.

To visualize ﬁ ber-tracking results, the large

number of calculated ﬁ ber tracts has to be reduced

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Fig. 34.7. Frontal view of whole-brain fi ber tracking for accelerated scans using acceleration factor of R=2. Only every forth of all fi bers with a length of more than 40 mm are shown for readability. Color coding is according to local fi ber segment orientation

comparison of tracks in the affected hemisphere to the tracks of the contralateral hemisphere – serial studies looking at multiple time points of the same subject and comparative studies trying to fi nd simi- larities and differences between subjects are currently limited due to the non-scalar nature of the tractogra- phy data. To allow straightforward comparison and evaluation, the fi ber-tracking data would have to be turned into one or more scalar values similar to an FA map or new methods would have to be developed to allow direct comparison of fi ber-tracking results.

34.4.2 Clinical Applications

Figure 34.8 shows images of a patient 5 days after a

stroke in the left centrum semiovale, which is clearly

identiﬁ able in mean non-diffusion-weighted and

mean diffusion-weighted images. While the color-

coded FA map hints at a disturbance of anisotropy

in the area of the lesion, whole-brain ﬁ ber tracking

shows no clear evidence of affected ﬁ bers in the area

of the lesion in comparison with the contralateral

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389

Fig. 34.8. Mean non-diffusion-weighted image, mean diffusion-weighted image and color-coded fractional anisotropy (FA) map (left column, top to bottom) from one slice of a patient with infarct in the left centrum semiovale as well as whole-brain fi ber tracking. Only fi bers intersecting a few transverse and coronal slices through the lesion are shown for clarity. View is from anterior-right-inferior. The infarcted area is clearly visible on mean non-diffusion-weighted and mean diffusion-weighted images and can also be seen in the background plane of the whole-brain fi ber tracking image. No obvious differences between infarcted area and contralateral side are visible in fi ber tracking

hemisphere. While mean non-diffusion-weighted and mean diffusion-weighted images are easy to interpret, maps of FA or color-coded FA are harder to read due to the strong contrast between gray and white matter and the higher level of noise. Fiber- tracking results are even harder to interpret since in most cases the only reference for visual comparison is the contralateral hemisphere. However, right-left symmetry is not perfect, making any comparison uncertain. Furthermore, while typical MR images can

be adjusted in a fairly limited number of ways (e.g., window level and center for display), visualization of ﬁ ber tracking results offers a nearly endless range of adjustable parameters, making a close inspection very time consuming.

Fiber tracking can be more straightforward to inter-

pret if the changes are more pronounced as can be the

case in tumors. Figure 34.9 shows the results of ﬁ ber

tracking in a number of stages during and 3 months

after surgery of an oligoastrocytoma (Nimsky et al.

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Fig. 34.9a–j. Intraoperative tractography visu- alizes a marked outward shifting of the right pyramidal tract during resection of a right tem- poroparietal oligoas-trocytoma WHO Grade III in a 29-year-old male patient (a,c,e,g,i) T1-weighted coronal MRI scans; (b,d,f,h,j) tractography of the pyramidal tracts; a and b, preoperative; c,d–g,h, during tumor resection with G and H after completed tumor removal;

I and J, 3 months after surgery. Note that the color coding of the anteroposterior direction is exchanged with the left/right direction because of the horizontal placement of the head for sur- gery during imaging in d,f,h [reprinted with permission from (Nimsky et al. 2005)]

c

b a

d

e f

g h

i j

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391 2005). During the course of the surgery, multiple DTI data sets were acquired within 5 min, each covering the whole brain at a isotropic spatial resolution of 1.9×1.9×1.9 mm³. In this study, comparison of preop- erative and intra-operative ﬁ ber tracking visualized shift and deformation of major white matter tracts caused by the tumor resection. The position of white matter tracts relative to the resection cavity margin is important to minimize the risk of injury to these essential motor ﬁ bers. This study also demonstrates the need for an intra-operative update of navigation systems during such procedures.

34.5 Conclusion

Diffusion-tensor imaging requires the acquisition of a large number of mostly diffusion-weighted vol- umes, and echo-planar imaging is the method of choice for the imaging of the brain. Parallel-imag- ing techniques provide a number of major beneﬁ ts for this application: (1) decreased distortion in EPI scans, i.e., better match with true anatomy and other images and therefore easier co-registration;

(2) shorter echo times and resulting shorter repeti- tion times that make otherwise prohibitively long scans feasible and allow ultra-fast imaging or more coverage or additional measurements at the same scan time; (3) feasibility of high-spatial-resolution imaging, providing anatomical details that would be hard to achieve otherwise. With reduction in signal- to-noise ratio being the major disadvantage, parallel imaging has become a standard tool for diffusion- tensor imaging of the brain given the widespread availability of phased-array head coils.

Acknowledgements. The authors would like to thank Cristina Granziera, Chris Melinosky, A. Gregory Sorensen, André J. W. van der Kouwe, Larry L. Wald and Graham C. Wiggins.

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