13 research outputs found

    Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images

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    <div><p>Introduction</p><p>Diffusion Weighted Imaging (DWI), which is based on Echo Planar Imaging (EPI) protocols, is becoming increasingly important for neurosurgical applications. However, its use in this context is limited in part by significant spatial distortion inherent to EPI.</p><p>Method</p><p>We evaluated an efficient algorithm for EPI distortion correction (EPIC) across 814 DWI scans from 250 brain tumor patients and quantified the magnitude of geometric distortion for whole brain and multiple brain regions.</p><p>Results</p><p>Evaluation of the algorithm’s performance revealed significantly higher mutual information between T1-weighted pre-contrast images and corrected b = 0 images than the uncorrected b = 0 images (<i>p</i> < 0.001). The distortion magnitude across all voxels revealed a median EPI distortion effect of 2.1 mm, ranging from 1.2 mm to 5.9 mm, the 5<sup>th</sup> and 95<sup>th</sup> percentile, respectively. Regions adjacent to bone-air interfaces, such as the orbitofrontal cortex, temporal poles, and brain stem, were the regions most severely affected by DWI distortion.</p><p>Conclusion</p><p>Using EPIC to estimate the degree of distortion in 814 DWI brain tumor images enabled the creation of a topographic atlas of DWI distortion across the brain. The degree of displacement of tumors boundaries in uncorrected images is severe but can be corrected for using EPIC. Our results support the use of distortion correction to ensure accurate and careful application of DWI to neurosurgical practice.</p></div

    EPI distortion histograms.

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    <p>Anterior-posterior displacement across all voxels within the template brain (black), frontal lobe (cyan), parietal lobe (green), occipital lobe (blue), temporal lobe (red), and brainstem (magenta). Frequency is reported as the fraction of voxels in the region of interest undergoing a given displacement.</p

    Gene expression data of the Allen Human Brain Atlas were mapped onto the 12 genetically based cortical regions in the MR space.

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    <p>A) Resulting volume registration between FreeSurfer surface (fsaverage) and Allen brain MNI coordinates displayed as a point cloud, with a slice of the MRI imaging at the bottom (colin27). B) After the volume registration, gene expression data points are mapped to FreeSurfer surface vertices by assigning each surface vertex the gene expression of the closest (Euclidean distance) Allen brain data point using nearest neighbor interpolation. If two vertices have the same closest Allen brain data point, they belong to the same patch and the patch id is displayed as color. Thus, the color patches illustrate the local density of data points. The color patches with similar sizes across the cortex represent an even distribution of Allen brain data points and their surface correspondences. Colors of the dots in both (A) and (B) panels represent cortical regions to which they were assigned, corresponding to the color schemes in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g001" target="_blank">Fig 1B</a>.</p

    Illustrative example of a tumor’s displacement with and without correcting for EPI distortions.

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    <p>Top left, T1-weighted image with contrast. Bottom left, Fluid Attenuated Inversion Recovery image. Middle: ADC calculated from corrected diffusion image (top) and uncorrected diffusion image (bottom) with the regions of diffusion restriction outlined (green/purple). Top right, corrected (purple) and uncorrected diffusion restriction outline (green) overlaid on T1-weighted image with contrast. Bottom right, corrected and uncorrected diffusion restriction outline overlaid on the displacement map.</p

    MSJ765671_supplementary_material_3 ‚Äď Supplemental material for Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis

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    <p>Supplemental material, MSJ765671_supplementary_material_3 for Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis by Piotr Sowa, Hanne F Harbo, Nathan S White, Elisabeth G Celius, Hauke Bartsch, P√•l Berg-Hansen, Stine M Moen, Atle Bj√łrnerud, Lars T Westlye, Ole A Andreassen, Anders M Dale and Mona K Beyer in Multiple Sclerosis Journal</p

    MSJ765671_supplementary_material_4 ‚Äď Supplemental material for Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis

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    <p>Supplemental material, MSJ765671_supplementary_material_4 for Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis by Piotr Sowa, Hanne F Harbo, Nathan S White, Elisabeth G Celius, Hauke Bartsch, P√•l Berg-Hansen, Stine M Moen, Atle Bj√łrnerud, Lars T Westlye, Ole A Andreassen, Anders M Dale and Mona K Beyer in Multiple Sclerosis Journal</p

    Region-specific gene expression profiles in each lobe.

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    <p>A) The majority of genes were ubiquitously expressed in the cortical surface areas of all four lobes of the brain. A small percentage of the genes were either distinctively expressed in one lobe or co-expressed in multiple but not all four lobes of the brain. The frontal lobe exhibits the most distinctively expressed genes. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s006" target="_blank">S5 Table</a> for the lists of genes and locations. B) The distribution of functional annotations of the transcripts distinctively expressed in the frontal lobe. ‚ÄúAll‚ÄĚ indicates the distribution of all transcripts included in our analysis, irrespective of their expression levels and anatomical locations. There are a higher proportion of intergenic transcripts in the frontal lobe (22% compared to 14%). C) A gene network analysis for the frontal lobe (excluding intergenic transcripts). The yellow-colored genes belong to the most significantly associated pathway: interferon-gamma-mediated signaling pathway, related to immunity (FDR = 3.2 x10<sup>-4</sup>). Half of the genes were originally from the transcripts distinctively expressed in the frontal lobe. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s008" target="_blank">S7 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s009" target="_blank">S8 Table</a> for the complete list of associated pathways.</p

    Applying the genetically based cortical parcellations to independent data.

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    <p>A) The phenotypic correlation matrix of VETSA twin cohort versus the phenotypic correlation matrix of combined-5-cohort (C5C). The Mantel test confirmed that the similarity between them was highly significant (<i>p</i> = 0.0001). B) Cortical brain phenotypes‚ÄĒsurface area measures of 12 cortical regions after controlling for total surface area. The cortex was parceled into 12 genetically based regions of maximal shared genetic influence derived from the VETSA sample [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.ref016" target="_blank">16</a>]. 1. motor & premotor; 2. dorsolateral prefrontal; 3. dorsomedial frontal; 4. orbitofrontal; 5. pars opercularis & subcentral; 6. superior temporal; 7. posterolateral temporal; 8. anteromedial temporal; 9. inferior parietal; 10. superior parietal; 11. precuneus; 12. occipital. C) The phenotypic correlation versus the genetic correlations (<i>r</i><sub><i>g</i></sub>) matrices of VETSA. The correlation of the two matrices was also highly significant (<i>p</i> < 0.0001), suggesting high genetic contributions to the cortical patterning. Correlation coefficients are listed in Supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s002" target="_blank">S1 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s003" target="_blank">S2 Table</a>.</p

    MSJ765671_supplementary_material_1 ‚Äď Supplemental material for Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis

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    <p>Supplemental material, MSJ765671_supplementary_material_1 for Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis by Piotr Sowa, Hanne F Harbo, Nathan S White, Elisabeth G Celius, Hauke Bartsch, P√•l Berg-Hansen, Stine M Moen, Atle Bj√łrnerud, Lars T Westlye, Ole A Andreassen, Anders M Dale and Mona K Beyer in Multiple Sclerosis Journal</p
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