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mri data  (Oxford Instruments)


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    Oxford Instruments mri data
    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
    Mri Data, supplied by Oxford Instruments, used in various techniques. Bioz Stars score: 99/100, based on 44000 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/mri+data/Imaris/bio_rxiv__64898__2026__04__13__718243-167-8-11
    Average 99 stars, based on 44000 article reviews
    mri data - by Bioz Stars, 2026-08
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    Images

    1) Product Images from "Topological defects and coherent myocardial chirality shape torsional heart contraction"

    Article Title: Topological defects and coherent myocardial chirality shape torsional heart contraction

    Journal: bioRxiv

    doi: 10.64898/2026.04.13.718243

    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on MRI data. Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
    Figure Legend Snippet: Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on MRI data. Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.

    Techniques Used: Derivative Assay, Diffusion-based Assay, Imaging



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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on <t>MRI</t> <t>data.</t> Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.
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    Image Search Results


    Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on MRI data. Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.

    Journal: bioRxiv

    Article Title: Topological defects and coherent myocardial chirality shape torsional heart contraction

    doi: 10.64898/2026.04.13.718243

    Figure Lengend Snippet: Workflow for the detection of disclination lines. (a) Bar plot representation of the fibre orientation field within the myocardium based on MRI data. Each bar indicates the local fibre direction derived from diffusion tensor imaging. (b-d) Computational pipeline for detecting disclination lines. The cleaning procedure removes artefactual features that do not represent topologically significant disclinations, including: boundary-related fluctuations near tissue surfaces, poorly resolved regions of the right ventricle (RV) where the wall thickness is too thin, and small surface loops that can be trivially resolved. (b) Frobenius norm of the disclination density tensor throughout the myocardium, showing fibre orientational disorder. (c) Extracted cores of regions where the Frobenius norm exhibits significantly large values, identifying potential singular sites. (d) Final result after the cleaning procedure, displaying only the disclination lines that are topologically relevant for cardiac fibre organisation.

    Article Snippet: Cardiac geometry was extracted from microscopy images or MRI data using Imaris software (Bitplane, version 10.0.0) to isolate the shape of the myocardium.

    Techniques: Derivative Assay, Diffusion-based Assay, Imaging

    Examples of 4D Flow MRI reference magnitude and velocity images (first column) for data from Site A. For each row, columns 2–6 show the differences between the reference and images reconstructed with models trained on 1–5 (Training Set 1–5 respectively) datasets. Note that there are no apparent systematic differences due to the size of the training set.

    Journal: Magnetic Resonance in Medicine

    Article Title: FlowVN Trained on a Single Dataset Enables Rapid Reconstruction of Highly Accelerated 4D Flow MRI Across Multiple Sites

    doi: 10.1002/mrm.70317

    Figure Lengend Snippet: Examples of 4D Flow MRI reference magnitude and velocity images (first column) for data from Site A. For each row, columns 2–6 show the differences between the reference and images reconstructed with models trained on 1–5 (Training Set 1–5 respectively) datasets. Note that there are no apparent systematic differences due to the size of the training set.

    Article Snippet: Fully‐sampled whole‐heart cartesian 4D flow MRI data were acquired at our institution (referred to as Site A) using a Philips 1.5 T Achieva scanner (Philips Healthcare, Best, The Netherlands) with software version 5.7.1, in 16 healthy volunteers during free breathing.

    Techniques: