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Average wild-type C57BL/6J mouse 3D MRI non-symmetric brain image

The University of Queensland
Charles Watson (Associated with) Dr Andrew Janke (Author) Dr Andrew Janke (Author) Dr Andrew Janke (Associated with) Dr Jeremy Ullmann (Author)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doidoi:10.14264/uql.2015.863&rft.title=Average wild-type C57BL/6J mouse 3D MRI non-symmetric brain image&rft.identifier=doi:10.14264/uql.2015.863&rft.publisher=The University of Queensland&rft.description=15um non-symmetric average mouse model in Waxholm space from 16.4T 30um images. Digital MRI atlases serve to integrate data from differing modalities, stereotaxic localisation, automatic region identification, automated segmentation and direct comparisons between individuals. While paper atlases can provide exquisite detail of delineated structures, they are typically based upon an individual subjects histology and as such make it difficult to identify structures in novel subjects in an automated fashion. Improvements in field and gradient strength has led to enhanced resolution and the number of segmented regions in MRI atlases. Arguably, the best current atlas is that of Dorr et al in 20083, acquired at 7T with a final resolution of 32um and 62 segmented structures. The data in this MRI atlas was acquired at 16.4T and created using a specific adaptation of a nonlinear averaging technique that resulted in a final resolution of 15um and is thus approaching histological clarity.&rft.creator=Dr Andrew Janke&rft.creator=Dr Andrew Janke&rft.creator=Dr Jeremy Ullmann&rft.creator=Dr Marianne Keller&rft.creator=Dr Nyoman Kurniawan&rft.creator=Emeritus Professor Perry Bartlett&rft.creator=Gary Egan&rft.creator=Professor Charles Watson&rft.creator=Professor David Reutens&rft.creator=Professor Graham Galloway&rft.creator=Steven Petrou&rft.date=1970&rft_rights=The content of the data collection is licensed under a Creative Commons Attribution 3.0 Australia License http://creativecommons.org/licenses/by/3.0/au/&rft_subject=Animal Neurobiology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=ZOOLOGY&rft_subject=NEUROSCIENCES&rft_subject=MEDICAL AND HEALTH SCIENCES&rft_subject=Central Nervous System&rft.type=dataset&rft.language=English Access the data
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The content of the data collection is licensed under a Creative Commons Attribution 3.0 Australia License
http://creativecommons.org/licenses/by/3.0/au/

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15um non-symmetric average mouse model in Waxholm space from 16.4T 30um images. Digital MRI atlases serve to integrate data from differing modalities, stereotaxic localisation, automatic region identification, automated segmentation and direct comparisons between individuals. While paper atlases can provide exquisite detail of delineated structures, they are typically based upon an individual subjects histology and as such make it difficult to identify structures in novel subjects in an automated fashion. Improvements in field and gradient strength has led to enhanced resolution and the number of segmented regions in MRI atlases. Arguably, the best current atlas is that of Dorr et al in 20083, acquired at 7T with a final resolution of 32um and 62 segmented structures. The data in this MRI atlas was acquired at 16.4T and created using a specific adaptation of a nonlinear averaging technique that resulted in a final resolution of 15um and is thus approaching histological clarity.

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Related Publications

  • Associated with An MRI atlas of the mouse basal ganglia
    doi : 10.1007/S00429-013-0572-0

    Ullmann, Jeremy F. P., Watson, Charles, Janke, Andrew L., Kurniawan, Nyoman D., Paxinos, George and Reutens, David C. (2013). An MRI atlas of the mouse basal ganglia. Brain Structure and Function, 219 (4), 1343-1353. doi: 10.1007/s00429-013-0572-0

  • Associated with An MRI atlas of the mouse basal ganglia
    uri : espace.library.uq.edu.au/view/UQ:303149

    Ullmann, Jeremy F. P., Watson, Charles, Janke, Andrew L., Kurniawan, Nyoman D., Paxinos, George and Reutens, David C. (2013). An MRI atlas of the mouse basal ganglia. Brain Structure and Function, 219 (4), 1343-1353. doi: 10.1007/s00429-013-0572-0

  • Associated with Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images
    uri : espace.library.uq.edu.au/view/UQ:275803

    Ullmann, Jeremy F. P., Keller, Marianne D., Watson, Charles, Janke, Andrew L., Kurniawan, Nyoman D., Yang, Zhengyi, Richards, Kay, Paxinos, George, Egan, Gary F., Petrou, Steven, Bartlett, Perry, Galloway, Graham J. and Reutens, David C. (2012). Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images. NeuroImage, 62 (3), 1408-1414. doi: 10.1016/j.neuroimage.2012.05.061

  • Associated with Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images
    doi : 10.1016/J.NEUROIMAGE.2012.05.061

    Ullmann, Jeremy F. P., Keller, Marianne D., Watson, Charles, Janke, Andrew L., Kurniawan, Nyoman D., Yang, Zhengyi, Richards, Kay, Paxinos, George, Egan, Gary F., Petrou, Steven, Bartlett, Perry, Galloway, Graham J. and Reutens, David C. (2012). Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images. NeuroImage, 62 (3), 1408-1414. doi: 10.1016/j.neuroimage.2012.05.061

  • Associated with Segmentation of the mouse hippocampal formation in magnetic resonance images
    uri : espace.library.uq.edu.au/view/UQ:247409

    Richards, Kay, Watson, Charles, Buckley, Rachel F., Kurniawan, Nyoman D., Yang, Zhengyi, Keller, Marianne D., Bearem, Richard, Bartlett, Perry F., Egan, Gary F., Galloway, Graham J., Paxinos, George, Petrou, Steven and Reutens, David C. (2011). Segmentation of the mouse hippocampal formation in magnetic resonance images. NeuroImage, 58 (3), 732-740. doi: 10.1016/j.neuroimage.2011.06.025

    • Show all 6 related publications

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Animal Neurobiology | Biological Sciences | Central Nervous System | Medical and Health Sciences | Neurosciences | Zoology |

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