How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

By Jesper L. R. Andersson, Stefan Skare, John Ashburner. NeuroImage, 2003

3,303 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4e5684b1ab78ff5fc1645395c26ce1d46151889f

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, positive): “These distortions are corrected by first applying the TOPUP tool (Andersson et al., 2003) from the FMRIB Software Library (FSL) (Jenkinson et al., 2012), which results in a single corrected image.”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, neutral): “While this might not provide statistical certainty for a diagnosis or interventional recommendation, it should allow for single-patient imaging to be used in a similar way to current state-of-the-art patient-tuned genetic testing.”
The impact of T1 vs EPI spatial normalization templates for fMRI data analyses (2017, methodology): “These are used to distortion correct the EPI data using the FSL topup program [Andersson et al., 2003] and compared with the images that were not distortion corrected.”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “This optimal AP/PA pair is then fed into topup 22 (Andersson et al. (2003)) in order to estimate the b=0 image ﬁeldmap and associated dMRI EPI distortions.”
The current state-of-the-art of spinal cord imaging: Methods (2013, background): “The two sets of images will exhibit distortions in the opposite direction, allowing distortion corrections to be determined (Andersson et al., 2003; Reinsberg et al., 2005; Voss et al., 2006).”
Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ) (2011, background): “DTI results may also be improved further by explicit correction of susceptibility-related distortions (Andersson et al., 2003; Gallichan et al., 2010).”
The Minimal Preprocessing Pipelines for the Human Connectome Project (2013, methodology): “This is done using the “topup” tool (Andersson et al., 2003) in FSL5.”
Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement (2017, methodology): “A ﬁ eldmap was calculated for each di ﬀ usion data set using the preceeding b =0 scan with opposing PE direction and the ﬁ rst b =0 volume of the di ﬀ usion data set using the FSL topup tool (Andersson et al., 2003).”
Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, neutral): “Each dataset was co-registered to the standard MNI space as follows: first, for each subject, the fMRI data were B0-unwarped using FSL-TOPUP (Andersson et al., 2003) and brought to the subject's anatomical space using FLIRT with boundary-based registration (Greve and Fischl, 2009) (12 degrees of fre”
An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging (2016, methodology): “However fg and f g0 will also have similar distortions so there will be little difference between the prediction and fg, which means that there is little to drive the registration.”
Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction (2018, methodology): “● Susceptibility-induced distortions : To correct for susceptibility-induced distortions, EDDY uses the off-resonance ﬁ eld estimated by the FSL TOPUP tool (Andersson et al., 2003).”
In vivo histology of the myelin g-ratio with magnetic resonance imaging (2015, methodology): “For even better registration in future acquisitions, magnetic susceptibility induced distortion could be corrected for using a blip-up blip-down phase encode strategy (Andersson et al., 2003).”
Metric learning with spectral graph convolutions on brain connectivity networks (2018, methodology): “, 2007) and distortion (Andersson et al., 2003), as well as high-pass filtered to remove temporal drift.”
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, positive): “The DW EPI images were preprocessed using the state-ofthe-art pipeline available in FSL, including susceptibility-induced distortion correction using a pair of b = 0 s/mm(2) images acquired with opposite phase encoding directions (Andersson et al., 2003) and combined eddy-current induced distortion ”