Ileana Hancu1, Ek Tsoon Tan1, Luca Marinelli1, Nathan White2, Dominic Holland2, Tim Sprenger3, and Jonathan Sperl3
1GE Global Research Center, Niskayuna, NY, United States, 2University of California San Diego, San Diego, CA, United States, 3GE Global Research Center, Munich, Germany
Synopsis
The performance of four distortion correction
algorithms was investigated in a cohort of normal volunteers. While all
approaches reduced distortion, it was found that the reversed polarity gradient
methods were inherently better than registration or B0-mapping approaches.
It was likely that the limited degrees of freedom of the registration approach could
not account for localized magnetic field inhomogeneity. The extrapolation of B0
maps in the distorted EPI space introduced errors that decreased the overall
performance of the B0-mapping method. Purpose
Correction
of susceptibility-induced distortion in EPI images has become an integral part
of brain DTI processing pipelines in the research community. There are three main
avenues for distortion correction, and many implementations of similar
approaches. Although pairwise comparisons of distortion correction performance
were previously documented [1-3], a more comprehensive comparison of multiple leading
correction algorithms remains elusive; such work would enable one to choose the
optimal strategy for a given study. The goals of this work are to evaluate four
of most commonly used distortion correction approaches, and document their
performance in a cohort of normal volunteers.
Methods
Five normal volunteers underwent scanning sessions on a
3T, GE MR750 scanner. The acquisition series included anatomical T1-weighted
3D GRE imaging, T2-weighted FSE and fat-suppressed B0
mapping using 2D GRE (TE=2.3/3.1ms). Two B0 mapping acquisitions
were performed, one at 128x128x6mm/2min acquisition time and the second one at
128x32x12mm/15sec acquisition time. Two DTI volumes (b=0 and 1000 s/m2)
were also acquired, one at R=2 (48 slices; 128x128x3mm resolution), and the
second at R=1 (72 slices; 128x128x2mm). The acquisition of the b=0 volumes of
the DTI scans was repeated using reversed polarity gradient (RPG) EPI.
The
four separate approaches tested for correcting susceptibility induced
distortion in the 10 sets of DTI images were:
a.
Separately-acquired
B0 maps: These were used as input for the correction algorithm
described in [4]. Fat suppression and a bandwidth of 600Hz ensured the absence
of phase jumps and phase wraps; fitting to cosine basis functions was used to
extend the undistorted B0 maps in the distorted EPI space.
b.
Higher-order image
registration: Similar to [5], following rigid registration of the distorted EPI
images to the anatomical, T1-weighted volumes, refinement of
alignment was performed using a 20-parameter cubic-polynomial basis-function
for only the phase-encode direction ((x',y',z')=(x,f(x,y,z),z)). As compared to
affine or nonlinear free-form deformation image registration, this approach
limits the nonlinearity to the primary direction where distortion is expected.
c.
RPG-1: The two
sets of spin-echo EPI images (acquired with forward and RPG’s) were used as
input for the algorithm of [6] to generate the field map, hence pixel
displacement.
d.
RPG-2: The same 2 sets
of EPI images described above were used as input to the minimization algorithm
employed in FSL-TOPUP [7].
The
quantitative metric of success was the cross-correlation coefficient (CCC) between
the T2-weighted FSE images and the uncorrected/corrected b=0 volume
of the DTI scans. This measure was computed using the entire imaging volume, as
well as using only the brain volume obtained after skull-stripping (implemented
using ROBEX [8]).
Results and discussion
An example of the performance of all corrections
methods on one volunteer is presented in Figure 1. Table 1 presents the average
CCC for the 5 volunteers, displayed before correction and after the four correction operations, over the head and brain areas, in the R=1 and R=2
acquisitions. Note a few important results:
- The majority
of the distortion was outside of the brain area (note the higher CCC’s over the
brain compared to over the head)
- B0
based methods performed as well as registration based methods
- The spatial
resolution of the B0 maps did not impact results significantly,
enabling one to proceed with brief B0 mapping protocols
- While all
correction methods reduced distortion, the RPG methods performed better than B0
or registration-based correction
Moreover, while the two RPG
methods were comparable on average, RPG-1 performed more consistently than
RPG-2 in the high distortion case (R=1); the difference between the highest and
lowest CCC in our 5 volunteers were 0.04/0.09 for RPG-1/RPG-2 respectively.
Moreover, our implementation of RPG-1 resulted in an average 50 second
processing time, shorter than the ~4min processing speed of RPG-2.
Conclusions
The performance of four distortion correction methods
was assessed in a cohort of normal volunteers. While all approaches reduced
distortion, it was found that the RPG methods were inherently better than
registration or B
0-based approaches. It is likely the limited degrees
of freedom of the registration approach could not account for the localized
magnetic field inhomogeneities of the human brain. Similarly, the need to
extrapolate the B
0 maps in the distorted EPI space also led to
errors/decreased performance of the B
0-mapping approach. It is hence
suggested for the prospective DTI studies to acquire the additional RPG images;
at one TR additional scan time, they add little burden to the study duration
and can significantly improve image quality.
Acknowledgements
No acknowledgement found.References
[1] Zeng et al, Magn
Reson Med. 2002,48(1):137-46.
[2] Gholipur et al, 33 Annual Int Conf IEEE EMBS,
2011, 6997-7000
[3] Wu et al, Med Image Comput Comput Assist Interv. 2008;11(Pt
2):321-9.
[4] Jezzard P, et al, Magn
Reson Med. 1995 Jul;34(1):65-73.
[5] Jenkinson et al, NeuroImage 2002, 17(2), 825-841.
[6] Holland D et al, NeuroImage 2010, 50, 175-183
[7] Andersson et al,
NeuroImage 2003, 20, 870-888. [8] Iglesias et al, IEEE TMI 2011, 30(9),
1617-1637.