This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.
Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.
Spatial normalization of the T1w image to the MNI152NLin6Asym template.
Average b=0 that serves for reference in early preprocessing steps.
The reportlet shows a b=0 reference before and after distortion correction.
Susceptibility distortions correction (SDC).
Eddy current-induced geometric distortions and head-motion realignment parameters were estimated with the joint modeling of eddy_openmp, included in FSL.
Head-motion and eddy current corrected diffusion data
/usr/local/miniconda/bin/dmriprep /data /out participant -w /work --participant_label 0522566501 --fs-license-file /mhome/license.txt --fs-no-reconall --low-mem --output-spaces MNI152NLin6Asym:res-2We kindly ask to report results preprocessed with this tool using the following boilerplate.
Results included in this manuscript come from preprocessing performed using dMRIPrep 0.5.0 (Joseph et al. (2020); RRID:SCR_017412), which is based on Nipype 1.6.0 (Gorgolewski et al. (2011); Esteban et al. (2020); RRID:SCR_002502).
A total of 1 fieldmaps were found available within the input BIDS structure for this particular subject. The corresponding phase-map(s) were phase-unwrapped with prelude (FSL <property object at 0x7fb1622ec3b8>). A B0 nonuniformity map (or fieldmap) was estimated from the phase-drift map(s) measure with two consecutive GRE (gradient-recalled echo) acquisitions.
A total of 1 T1-weighted (T1w) images were found within the input BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection (Tustison et al. 2010), distributed with ANTs 2.3.3 (Avants et al. 2008, RRID:SCR_004757), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast (FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001). Volume-based spatial normalization to one standard space (MNI152NLin6Asym) was performed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: FSL’s MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model [Evans et al. (2012), RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym],
For each of the 1 DWI scans found per subject (across all sessions), the gradient table was vetted and converted into the RASb format (i.e., given in RAS+ scanner coordinates, normalized b-vectors and scaled b-values), and a b=0 average for reference to the subsequent steps of preprocessing was calculated. The estimated fieldmap was then aligned with rigid-registration to the target EPI (echo-planar imaging) reference run. The field coefficients were mapped on to the reference EPI using the transform. Geometrical distortions derived from the so-called Eddy-currents, and head-motion realignment parameters were estimated with the joint modeling of eddy_openmp, included in FSL 5.0.9 (eddy?).
For more details of the pipeline, see the section corresponding to workflows in dMRIPrep’s documentation.
The above boilerplate text was automatically generated by dMRIPrep with the express intention that users should copy and paste this text into their manuscripts unchanged. It is released under the CC0 license.
Results included in this manuscript come from preprocessing performed using *dMRIPrep* 0.5.0 (@dmriprep; RRID:SCR_017412), which is based on *Nipype* 1.6.0 (@nipype1; @nipype2; RRID:SCR_002502). *B0* fieldmap data preprocessing : A total of 1 fieldmaps were found available within the input BIDS structure for this particular subject. The corresponding phase-map(s) were phase-unwrapped with `prelude` (FSL). A *B0* nonuniformity map (or *fieldmap*) was estimated from the phase-drift map(s) measure with two consecutive GRE (gradient-recalled echo) acquisitions. Anatomical data preprocessing : A total of 1 T1-weighted (T1w) images were found within the input BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.3 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a *Nipype* implementation of the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823, @fsl_fast]. Volume-based spatial normalization to one standard space (MNI152NLin6Asym) was performed through nonlinear registration with `antsRegistration` (ANTs 2.3.3), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: *FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model* [@mni152nlin6asym, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym], Diffusion data preprocessing : For each of the 1 DWI scans found per subject (across all sessions), the gradient table was vetted and converted into the *RASb* format (i.e., given in RAS+ scanner coordinates, normalized b-vectors and scaled b-values), and a *b=0* average for reference to the subsequent steps of preprocessing was calculated. The estimated *fieldmap* was then aligned with rigid-registration to the target EPI (echo-planar imaging) reference run. The field coefficients were mapped on to the reference EPI using the transform. Geometrical distortions derived from the so-called Eddy-currents, and head-motion realignment parameters were estimated with the joint modeling of ``eddy_openmp``, included in FSL 5.0.9 [@eddy]. For more details of the pipeline, see [the section corresponding to workflows in *dMRIPrep*'s documentation](https://nipreps.github.io/dmriprep/master/workflows.html "dMRIPrep's documentation"). ### Copyright Waiver The above boilerplate text was automatically generated by dMRIPrep with the express intention that users should copy and paste this text into their manuscripts *unchanged*. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license. ### References
Results included in this manuscript come from preprocessing performed
using \emph{dMRIPrep} 0.5.0 (\citet{dmriprep}; RRID:SCR\_017412), which
is based on \emph{Nipype} 1.6.0 (\citet{nipype1}; \citet{nipype2};
RRID:SCR\_002502).
\begin{description}
\item[\emph{B0} fieldmap data preprocessing]
A total of 1 fieldmaps were found available within the input BIDS
structure for this particular subject. The corresponding phase-map(s)
were phase-unwrapped with \texttt{prelude} (FSL \textless property
object at 0x7fb1622ec3b8\textgreater). A \emph{B0} nonuniformity map (or
\emph{fieldmap}) was estimated from the phase-drift map(s) measure with
two consecutive GRE (gradient-recalled echo) acquisitions.
\item[Anatomical data preprocessing]
A total of 1 T1-weighted (T1w) images were found within the input BIDS
dataset.The T1-weighted (T1w) image was corrected for intensity
non-uniformity (INU) with \texttt{N4BiasFieldCorrection} \citep{n4},
distributed with ANTs 2.3.3 \citep[RRID:SCR\_004757]{ants}, and used as
T1w-reference throughout the workflow. The T1w-reference was then
skull-stripped with a \emph{Nipype} implementation of the
\texttt{antsBrainExtraction.sh} workflow (from ANTs), using OASIS30ANTs
as target template. Brain tissue segmentation of cerebrospinal fluid
(CSF), white-matter (WM) and gray-matter (GM) was performed on the
brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
RRID:SCR\_002823,][]{fsl_fast}. Volume-based spatial normalization to
one standard space (MNI152NLin6Asym) was performed through nonlinear
registration with \texttt{antsRegistration} (ANTs 2.3.3), using
brain-extracted versions of both T1w reference and the T1w template. The
following template was selected for spatial normalization: \emph{FSL's
MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain
Stereotaxic Registration Model} {[}\citet{mni152nlin6asym},
RRID:SCR\_002823; TemplateFlow ID: MNI152NLin6Asym{]},
\item[Diffusion data preprocessing]
For each of the 1 DWI scans found per subject (across all sessions), the
gradient table was vetted and converted into the \emph{RASb} format
(i.e., given in RAS+ scanner coordinates, normalized b-vectors and
scaled b-values), and a \emph{b=0} average for reference to the
subsequent steps of preprocessing was calculated. The estimated
\emph{fieldmap} was then aligned with rigid-registration to the target
EPI (echo-planar imaging) reference run. The field coefficients were
mapped on to the reference EPI using the transform. Geometrical
distortions derived from the so-called Eddy-currents, and head-motion
realignment parameters were estimated with the joint modeling of
\texttt{eddy\_openmp}, included in FSL 5.0.9 \citep{eddy}.
\end{description}
For more details of the pipeline, see
\href{https://nipreps.github.io/dmriprep/master/workflows.html}{the
section corresponding to workflows in \emph{dMRIPrep}'s documentation}.
\hypertarget{copyright-waiver}{%
\subsubsection{Copyright Waiver}\label{copyright-waiver}}
The above boilerplate text was automatically generated by dMRIPrep with
the express intention that users should copy and paste this text into
their manuscripts \emph{unchanged}. It is released under the
\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
\hypertarget{references}{%
\subsubsection{References}\label{references}}
\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/dmriprep/data/boilerplate.bib}
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