Data

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

The University of Queensland
Charles Watson (Associated with) Dr Andrew Janke (Aggregated by) Dr Andrew Janke (Aggregated by) Dr Andrew Janke (Aggregated by)
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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:doi10.14264/uql.2015.863&rft.title=Average wild-type C57BL/6J mouse 3D MRI non-symmetric brain image&rft.identifier=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=Australian Mouse Brain Mapping Consortium&rft.date=2015&rft_rights=2012, The University of Queensland&rft_rights= http://creativecommons.org/licenses/by/3.0/deed.en_US&rft_subject=eng&rft_subject=Mouse brain mapping&rft_subject=Magnetic Resonance Imaging&rft_subject=Non-symmetric brain image&rft_subject=biological science&rft_subject=Zoology&rft_subject=Neuroscience&rft_subject=Medical and health sciences&rft_subject=Central nervous system&rft_subject=Animal neurobiology&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_subject=Biostatistics&rft_subject=MATHEMATICAL SCIENCES&rft_subject=STATISTICS&rft_subject=Biomedical Engineering not elsewhere classified&rft_subject=ENGINEERING&rft_subject=BIOMEDICAL ENGINEERING&rft_subject=Bioinformatics&rft_subject=BIOCHEMISTRY AND CELL BIOLOGY&rft.type=dataset&rft.language=English Access the data
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http://creativecommons.org/licenses/by/3.0/deed.en_US

2012, The University of Queensland

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Contact Information

a.janke1@uq.edu.au

Full 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.

Issued: 2015

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Subjects
Animal Neurobiology | Animal neurobiology | Biochemistry and Cell Biology | Biological Sciences | Biomedical Engineering | Bioinformatics | Biomedical Engineering Not Elsewhere Classified | Biostatistics | Central Nervous System | Central nervous system | Engineering | Mathematical Sciences | Medical and Health Sciences | Magnetic Resonance Imaging | Medical and health sciences | Mouse brain mapping | Neurosciences | Neuroscience | Non-symmetric brain image | Statistics | Zoology | Zoology | biological science | eng |

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Other Information
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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

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Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images

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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

Research Data Collections

local : UQ:289097

Centre for Advanced Imaging Publications

local : UQ:3927

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