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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tisca, Cristiana; Tachrount, Mohamed; Lange, Frederik J; Wang, Chaoyue; +4 Authors

    {"references": ["Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Chakravarty, M. M. et al. (2013) 'Performing label-fusion-based segmentation using multiple automatically generated templates', Human Brain Mapping. doi: 10.1002/hbm.22092.", "Eckstein, K. et al. (2018) 'Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE)', Magnetic Resonance in Medicine, 79(6), pp. 2996\u20133006. doi: 10.1002/mrm.26963.", "Friedel, M. et al. (2014) 'Pydpiper: A flexible toolkit for constructing novel registration pipelines', Frontiers in Neuroinformatics. doi: 10.3389/fninf.2014.00067.", "Gudbjartsson, H. and Patz, S. (1995) 'The rician distribution of noisy MRI data', Magnetic Resonance in Medicine, 34(6), pp. 910\u2013914. doi: 10.1002/mrm.1910340618.", "Jenkinson, M. (2003) 'Fast, automated, N-dimensional phase-unwrapping algorithm', Magnetic Resonance in Medicine, 49(1), pp. 193\u2013197. doi: 10.1002/mrm.10354.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Li, W. et al. (2015) 'A method for estimating and removing streaking artifacts in quantitative susceptibility mapping', NeuroImage. doi: 10.1016/j.neuroimage.2014.12.043.", "Li, W., Wu, B. and Liu, C. (2011) 'Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition', NeuroImage. doi: 10.1016/j.neuroimage.2010.11.088.", "Robinson, S. D. et al. (2017) 'Combining phase images from array coils using a short echo time reference scan (COMPOSER)', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26093.", "Schweser, F. et al. (2011) 'Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?', NeuroImage. doi: 10.1016/j.neuroimage.2010.10.070.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Wang, C. et al. (2020) 'Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis', NeuroImage. Elsevier Inc., 222(May), p. 117216. doi: 10.1016/j.neuroimage.2020.117216.", "Wang, C. et al. (2022) 'Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging', Nature Neuroscience, 25(6), pp. 818\u2013831. doi: 10.1038/s41593-022-01074-w."]} This repository contains all scripts to run the ex vivo R2*- and QSM post-processing pipelines on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. This pipeline is based on the code developed by Chaoyue Wang and Benjamin Tendler and published here: https://doi.org/10.1016/j.neuroimage.2020.117216. The scripts were either written or adapted by Cristiana Tisca. The outputs of the pipeline include QSM and R2* maps. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tisca, Cristiana; Tachrount, Mohamed; Lange, Frederik J; Howard, Amy F D; +3 Authors

    {"references": ["Andersson, J. L. R., Skare, S. and Ashburner, J. (2003) 'How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging', NeuroImage. doi: 10.1016/S1053-8119(03)00336-7.", "Andersson, J. L. R. and Sotiropoulos, S. N. (2016) 'An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging', NeuroImage. doi: 10.1016/j.neuroimage.2015.10.019.", "Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Behrens, T. E. J. et al. (2007) 'Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?', NeuroImage, 34(1), pp. 144\u2013155. doi: 10.1016/j.neuroimage.2006.09.018.", "Hernandez-Fernandez, M. et al. (2019) 'Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes', NeuroImage. doi: 10.1016/j.neuroimage.2018.12.015.", "Kaden, E. et al. (2016) 'Multi-compartment microscopic diffusion imaging', NeuroImage. doi: 10.1016/j.neuroimage.2016.06.002.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Smith, S. M. et al. (2004) 'Advances in functional and structural MR image analysis and implementation as FSL', in NeuroImage. doi: 10.1016/j.neuroimage.2004.07.051.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Zhang, H. et al. (2012) 'NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain', NeuroImage. doi: 10.1016/j.neuroimage.2012.03.072."]} This repository contains all scripts to run the ex vivo diffusion-weighted MRI (dMRI) post-processing pipeline on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner and using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. The outputs of the pipeline include the standard DTI (FA, MD, V1) and DKI (FA, AK, RK, K1) outputs from FSL's dtifit and the standard NODDI outputs (OD, ICVF, ISOVF) from cuDIMOT. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Elliot Howley; Susan Francis; Denis Schluppeck;

    fRAT is an open-source python-based GUI application used to simplify the processing and analysis of fMRI data by converting voxelwise maps into ROI-wise maps. An installation of FSL is required in order to use fRAT. fRAT is designed for use with MacOS, Linux and WSL2.

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    Authors: Mazurenko, Stanislav; Jauhiainen, Jyrki; Valkonen, Tuomo;

    These are the Julia codes for the diffusion tensor imaging experiments of the manuscript “Primal-dual block-proximal splitting for a class of non-convex problems” by S. Mazurenko, J. Jauhiainen, and T. Valkonen (arXiv:1911.06284). The codes were written by T. Valkonen. Prerequisites These codes we written for Julia 1.1 but are known to work with Julia 1.2. The Julia package prequisites are from May 2019 when our experiments were run, and have not been updated to maintain the same environment we used to do the experiments in the manuscript. You may get Julia from https://julialang.org/. To generate the Helix test data (the data we have generated are included), the Teem toolkit is needed. You may download it from https://sourceforge.net/projects/teem/. Please note that the version included in Homebrew is missing the emap binary that is required for the visualisation; indeed as of May 2019 the building of this binary is disabled in Teem. You will therefore need to build Teem manually from source, applying the included teem-1.11.0-emap_build_fix.patch. In your unix shell, in the top-level directory of the Teem toolkit source codes, run: $ patch -p1 < path_to_these_codes/teem-1.11.0-emap_build_fix.patch Then build and install Teem according to instructions. The visualisation further requires the open -g to open the generated PDF file in a PDF viewer that will correctly refresh the file on further launches. This is the case on MacOS with Preview or Skim. Using Navigate your unix shell to the directory containing this README.md and then run: $ julia --project=BlockPDPS Afterwards in the Julia shell, type: > using BlockPDPS This may take a while as Julia builds any missing dependencies. Then, to run the default experiments, run: > test_dti_helix(visualise=true) This will use any cached data in the data/ subdirectory of the current working directory. If no cached data is found, new data will be generated using Teem. If you use provided data and set visualise=false, the Teem dependency will be removed. The results are saved under the img/ subdirectory of the current working directory. The test_dti_helix function has further parameters; please see the source code for details.

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    ZENODO
    Software . 2019
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Software . 2019
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Software . 2019
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Software . 2019
      License: CC BY
      Data sources: Datacite
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The following results are related to Neuroinformatics. Are you interested to view more results? Visit OpenAIRE - Explore.
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tisca, Cristiana; Tachrount, Mohamed; Lange, Frederik J; Wang, Chaoyue; +4 Authors

    {"references": ["Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Chakravarty, M. M. et al. (2013) 'Performing label-fusion-based segmentation using multiple automatically generated templates', Human Brain Mapping. doi: 10.1002/hbm.22092.", "Eckstein, K. et al. (2018) 'Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE)', Magnetic Resonance in Medicine, 79(6), pp. 2996\u20133006. doi: 10.1002/mrm.26963.", "Friedel, M. et al. (2014) 'Pydpiper: A flexible toolkit for constructing novel registration pipelines', Frontiers in Neuroinformatics. doi: 10.3389/fninf.2014.00067.", "Gudbjartsson, H. and Patz, S. (1995) 'The rician distribution of noisy MRI data', Magnetic Resonance in Medicine, 34(6), pp. 910\u2013914. doi: 10.1002/mrm.1910340618.", "Jenkinson, M. (2003) 'Fast, automated, N-dimensional phase-unwrapping algorithm', Magnetic Resonance in Medicine, 49(1), pp. 193\u2013197. doi: 10.1002/mrm.10354.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Li, W. et al. (2015) 'A method for estimating and removing streaking artifacts in quantitative susceptibility mapping', NeuroImage. doi: 10.1016/j.neuroimage.2014.12.043.", "Li, W., Wu, B. and Liu, C. (2011) 'Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition', NeuroImage. doi: 10.1016/j.neuroimage.2010.11.088.", "Robinson, S. D. et al. (2017) 'Combining phase images from array coils using a short echo time reference scan (COMPOSER)', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26093.", "Schweser, F. et al. (2011) 'Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?', NeuroImage. doi: 10.1016/j.neuroimage.2010.10.070.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Wang, C. et al. (2020) 'Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis', NeuroImage. Elsevier Inc., 222(May), p. 117216. doi: 10.1016/j.neuroimage.2020.117216.", "Wang, C. et al. (2022) 'Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging', Nature Neuroscience, 25(6), pp. 818\u2013831. doi: 10.1038/s41593-022-01074-w."]} This repository contains all scripts to run the ex vivo R2*- and QSM post-processing pipelines on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. This pipeline is based on the code developed by Chaoyue Wang and Benjamin Tendler and published here: https://doi.org/10.1016/j.neuroimage.2020.117216. The scripts were either written or adapted by Cristiana Tisca. The outputs of the pipeline include QSM and R2* maps. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z

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    Authors: Tisca, Cristiana; Tachrount, Mohamed; Lange, Frederik J; Howard, Amy F D; +3 Authors

    {"references": ["Andersson, J. L. R., Skare, S. and Ashburner, J. (2003) 'How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging', NeuroImage. doi: 10.1016/S1053-8119(03)00336-7.", "Andersson, J. L. R. and Sotiropoulos, S. N. (2016) 'An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging', NeuroImage. doi: 10.1016/j.neuroimage.2015.10.019.", "Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Behrens, T. E. J. et al. (2007) 'Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?', NeuroImage, 34(1), pp. 144\u2013155. doi: 10.1016/j.neuroimage.2006.09.018.", "Hernandez-Fernandez, M. et al. (2019) 'Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes', NeuroImage. doi: 10.1016/j.neuroimage.2018.12.015.", "Kaden, E. et al. (2016) 'Multi-compartment microscopic diffusion imaging', NeuroImage. doi: 10.1016/j.neuroimage.2016.06.002.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Smith, S. M. et al. (2004) 'Advances in functional and structural MR image analysis and implementation as FSL', in NeuroImage. doi: 10.1016/j.neuroimage.2004.07.051.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Zhang, H. et al. (2012) 'NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain', NeuroImage. doi: 10.1016/j.neuroimage.2012.03.072."]} This repository contains all scripts to run the ex vivo diffusion-weighted MRI (dMRI) post-processing pipeline on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner and using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. The outputs of the pipeline include the standard DTI (FA, MD, V1) and DKI (FA, AK, RK, K1) outputs from FSL's dtifit and the standard NODDI outputs (OD, ICVF, ISOVF) from cuDIMOT. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z

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    Authors: Elliot Howley; Susan Francis; Denis Schluppeck;

    fRAT is an open-source python-based GUI application used to simplify the processing and analysis of fMRI data by converting voxelwise maps into ROI-wise maps. An installation of FSL is required in order to use fRAT. fRAT is designed for use with MacOS, Linux and WSL2.

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    Authors: Mazurenko, Stanislav; Jauhiainen, Jyrki; Valkonen, Tuomo;

    These are the Julia codes for the diffusion tensor imaging experiments of the manuscript “Primal-dual block-proximal splitting for a class of non-convex problems” by S. Mazurenko, J. Jauhiainen, and T. Valkonen (arXiv:1911.06284). The codes were written by T. Valkonen. Prerequisites These codes we written for Julia 1.1 but are known to work with Julia 1.2. The Julia package prequisites are from May 2019 when our experiments were run, and have not been updated to maintain the same environment we used to do the experiments in the manuscript. You may get Julia from https://julialang.org/. To generate the Helix test data (the data we have generated are included), the Teem toolkit is needed. You may download it from https://sourceforge.net/projects/teem/. Please note that the version included in Homebrew is missing the emap binary that is required for the visualisation; indeed as of May 2019 the building of this binary is disabled in Teem. You will therefore need to build Teem manually from source, applying the included teem-1.11.0-emap_build_fix.patch. In your unix shell, in the top-level directory of the Teem toolkit source codes, run: $ patch -p1 < path_to_these_codes/teem-1.11.0-emap_build_fix.patch Then build and install Teem according to instructions. The visualisation further requires the open -g to open the generated PDF file in a PDF viewer that will correctly refresh the file on further launches. This is the case on MacOS with Preview or Skim. Using Navigate your unix shell to the directory containing this README.md and then run: $ julia --project=BlockPDPS Afterwards in the Julia shell, type: > using BlockPDPS This may take a while as Julia builds any missing dependencies. Then, to run the default experiments, run: > test_dti_helix(visualise=true) This will use any cached data in the data/ subdirectory of the current working directory. If no cached data is found, new data will be generated using Teem. If you use provided data and set visualise=false, the Teem dependency will be removed. The results are saved under the img/ subdirectory of the current working directory. The test_dti_helix function has further parameters; please see the source code for details.

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    ZENODO
    Software . 2019
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    Software . 2019
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