Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results
Highlights
► We propose a framework to assess the effects of EPI distortions on tractography. ► We show that distortions in typical clinical 3 T scans greatly affect path trajectory. ► Path trajectory artifacts lead to incorrect conclusions about brain connectivity. ► We show that simple corrections can be successfully implemented.
Introduction
Diffusion Tensor Imaging (DTI) (Basser et al., 1994) and other diffusion MRI modalities have been extensively used to investigate the structure and architecture of the human brain. In addition to scalar quantities such as fractional anisotropy and mean diffusivity, vectorial information contained in the diffusion displacement profile has been exploited to gain information on the orientation of white matter fibers (Pajevic and Pierpaoli, 1999). DTI-based fiber “tractography” (Basser et al., 2000, Mori et al., 1999, Wedeen and Reese, 1997) methods have been used to extract plausible trajectories of major white matter pathways non-invasively (Catani and Schotten, 2008). More recently, tractography methods that aim at resolving multiple fiber bundles with different orientations in a voxel have been developed. These methods that generally require diffusion weighted images with a large number of diffusion sensitizing directions and relatively high b-values, go under the collective name of high angular resolution diffusion imaging (HARDI) (e.g., diffusion spectrum imaging (DSI) (Tuch, 2002), Q-Ball imaging (Tuch, December, 2004), (Frank, 2002, Tuch et al., 1999), PASMRI (Jansons and Alexander, 2003)). The reader is referred to Tournier et al. (2011) for a comprehensive review of these methods. In addition to recognizing the role of diffusion MRI as an important tool for segmenting major white matter tracts, within the neuroscience community, there is a widespread belief that these methods can play a key role in elucidating anatomical connectivity in the human brain non-invasively. The extensive list of approaches for multi-fiber based connectivity includes but is not limited to the works of Tuch (2002), Jansons and Alexander (2003), Blyth et al. (2003), Chen et al. (2004), Behrens et al. (2007), Anderson and Ding (2002), Tournier et al. (2004), Özarslan et al. (2006), Parker and Alexander (2005), Descoteaux et al. (2009), Jian and Vemuri (2007), Jeurissen et al. (2011) and Qazi et al. (2009). Given the prominent role of these techniques in neuroscience, it is relevant to investigate factors that may affect tract accuracy and reproducibility. Regardless of the particular post-processing method, diffusion weighted images used for diffusion MRI tractography are generally acquired with echo planar imaging (EPI) (Turner and Le Bihan, February, 1990). EPI has the advantage of being a very efficient acquisition modality, with excellent signal to noise per unit time. However, a well-known problem in echo planar images is the presence of geometrical and intensity distortions along the phase-encode direction caused by field inhomogeneities (Jezzard and Balban, 1995) and concomitant fields (Du et al., 2002). It should be noted that these EPI distortions are different from eddy current distortions, which are caused by the rapid switching of the diffusion sensitizing gradients. Eddy current distortions affect only diffusion weighted images, but not the images acquired with no diffusion sensitization (so called b = 0 s/mm2 images). EPI distortions affect all images in the datasets, independently from their level of diffusion sensitization. Eddy current distortions have been recognized as a problem since the early days of diffusion MRI and they are now typically corrected in most diffusion MRI processing pipelines. EPI distortions, however, have been largely ignored. Correction of EPI distortion generally requires acquisition of additional data for B0 mapping (Jezzard and Balban, 1995). Approaches to EPI distortion correction that do not require B0 mapping using a dedicated T1 or T2 weighted structural target have been proposed (Kybic et al., 2000, Tao et al., 2009, Wu et al., 2008). They have been shown to perform similarly to B0 mapping (Wu et al., 2008), but despite improved user friendliness, their implementation is not widespread.
Previous works suggest that EPI distortions may impact DTI fiber tractography negatively, but a systematic investigation is lacking. Improvements on fiber tracts after B0-mapping type corrections have been reported (Lee et al., 2004, Andersson et al., 2004, and Pintjens et al., 2008) as well as improvements in tensor-derived scalar maps (Wu et al., 2008). In their work, Lee et al. (2004) employed a field-mapping based EPI distortion correction scheme to investigate for improvements in voxel-wise correspondences among fractional anisotropy (FA) images and their corresponding distortion-free anatomical T1W images. They also showed improvements in continuity and symmetry of prefrontal tracts of one subject using streamline tractography. In the same year, Andersson et al. (2004) showed that after EPI distortion correction, probabilistic thalamic–frontal cortex connections appeared anatomically improved. Embleton et al. (2010) investigated the effects of eddy currents and susceptibility induced distortions on tractography and functional MRI (fMRI) by employing a distortion correction scheme that is a variant of the reversed direction k-space traversal method (Bowtell et al., 1994). They showed improvements on both streamline and probabilistic tractography, as well as fMRI statistics. Pintjens et al. (2008) showed that susceptibility distortion correction with their proposed B0 map acquisition improves streamline tractography on a synthetic fiber dataset. Techavipoo et al. (2009) showed that with field inhomogeneity based EPI geometric distortion correction, tractography on optic nerves was feasible with healthy volunteers and multiple sclerosis patients. Gui et al. (2008) proposed a “distortion-free” pulse sequence, namely Turboprop, to show the improvements to streamline tractography of several anatomical fiber bundles.
In this work, we propose a framework that can be used to assess the severity of the effects of EPI distortions on virtually any fiber pathway that may be of interest. We study the effect of EPI distortions on association, commissural, and projection pathways in a population of healthy subjects on data collected on a 3 T clinical scanner. Finally we test the ability of an easy to implement image-based correction scheme to improve accuracy and reproducibility of the reconstructed fiber trajectories.
Section snippets
Dataset
Five healthy volunteers aged 32 to 55 years, two males, three females, were scanned on a 3 Tesla GE Excite scanner using a sixteen channel coil (GE Medical Systems, Milwaukee, WI). All volunteers signed an informed consent under NIMH protocol t00M0085. Whole brain single-shot EPI DWI datasets were acquired with FOV = 24 × 24 cm, slice thickness = 2.5 mm, matrix size = 128 × 128 (zero filled from 96 × 96), 66 axial slices, with parallel imaging factor of two and TR/TE of 20566/75 ms. No cardiac gating was
Results
The original distorted data, the EPI distortion corrected version, the employed ROIs, the probabilistic tract images, and the population average tract images can be downloaded from:http://science.nichd.nih.gov/confluence/display/nihpd/Download.
Discussion
In this work we investigated the effects of echo planar imaging distortions caused by magnetic susceptibility differences and concomitant fields on diffusion tensor imaging based fiber tractography. These distortions manifest themselves as displacements along the phase encode direction, therefore we used data with different phase encoding directions to evaluate their effects on tracts. We also evaluated the effects of an easily usable and simple distortion correction protocol.
The primary
Acknowledgments
This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH). Support for this work included funding from Department of Defense in the Center for Neuroscience and Regenerative Medicine (CNRM). The authors would also like to thank Liz Salak for editing this manuscript and the Henry M. Jackson Foundation (HJF) for their administrative support.
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