Difference between revisions of "Voxel Printing of Neuroimaging"
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[[Category:DCIO]] | [[Category:DCIO]] | ||
[[Category:DCIO2020]] | [[Category:DCIO2020]] | ||
| − | [[Category:DCIO | + | [[Category:DCIO Proceedings]] |
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[[Category:Conferences]] | [[Category:Conferences]] | ||
| + | [http://wiki.designcomputation.org/home/index.php/DC_I/O_2020#Posters DC I/O 2020 poster] by [[LUKE HALE]]. | ||
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=Abstract= | =Abstract= | ||
3D printing is becoming a widespread and useful technology in medicine with applications in simulation, teaching, surgical planning and patient-specific prostheses. Typically, in order to 3D print neuroimaging data, anatomical areas of interest must first be identified on individual 2D slices, then isolated (either manually or via thresholding), and then converted to a 3D mesh. This time-consuming ‘segmentation’ typically requires commercial software and, in forming this 3D mesh, the rest of the scan data is lost and reduced to a binary representation i.e. either outside or inside the anatomical area of interest. Furthermore, the size of structures may be over or underestimated. Voxels (volume pixels) represent a value on a 3-dimensional grid, with 3D printing outputting these values to a 3D printed model. Voxel printing can obviate the need for segmentation and 3D mesh generation, with effectively lossless printing of whole neuroimaging datasets. Here, 7T MRI brain images were converted to bitmap images and printed on a Stratasys 760M printer at the printer's native resolution. SimpleITK, an open source library for imaging analysis, was used to interpolate between imaging slices to achieve the required 800dpi slice resolution; a Floyd-Steinberg dithering algorithm was used to convert images to two pixel values representing clear and opaque resin. The resulting printed model has preservation of the delicate cerebral vasculature and differentiation between grey / white matter. Voxel printing of neuroimaging therefore offers exciting possibilities with more accurate visualisation of complex neurological structures, with the added possibility of multi-material gradients which simulate native tissue. | 3D printing is becoming a widespread and useful technology in medicine with applications in simulation, teaching, surgical planning and patient-specific prostheses. Typically, in order to 3D print neuroimaging data, anatomical areas of interest must first be identified on individual 2D slices, then isolated (either manually or via thresholding), and then converted to a 3D mesh. This time-consuming ‘segmentation’ typically requires commercial software and, in forming this 3D mesh, the rest of the scan data is lost and reduced to a binary representation i.e. either outside or inside the anatomical area of interest. Furthermore, the size of structures may be over or underestimated. Voxels (volume pixels) represent a value on a 3-dimensional grid, with 3D printing outputting these values to a 3D printed model. Voxel printing can obviate the need for segmentation and 3D mesh generation, with effectively lossless printing of whole neuroimaging datasets. Here, 7T MRI brain images were converted to bitmap images and printed on a Stratasys 760M printer at the printer's native resolution. SimpleITK, an open source library for imaging analysis, was used to interpolate between imaging slices to achieve the required 800dpi slice resolution; a Floyd-Steinberg dithering algorithm was used to convert images to two pixel values representing clear and opaque resin. The resulting printed model has preservation of the delicate cerebral vasculature and differentiation between grey / white matter. Voxel printing of neuroimaging therefore offers exciting possibilities with more accurate visualisation of complex neurological structures, with the added possibility of multi-material gradients which simulate native tissue. | ||
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| + | =Keywords= | ||
| + | [[Visuospatial]], [[Visual Perception]], [[Visual Quantification]], [[Cyberception]], [[Deep Learning]], [[Neural Networks]], [[Machine learning]], [[Human Navigation]], [[Big Data]], [[Cognising Spaces]]. | ||
| + | |||
| + | =Keyphrases= | ||
| + | N/A | ||
=Topics= | =Topics= | ||
| − | + | Adaptive ML, DigitalOps, Architecture, Artificial Intelligence in Design, Assisted Design Decision Making, Calculation and Design Analysis, Computational Creativity, Data Visualization and Analysis for design, Design Cognition, Responsive computer-aided design, Urban Design | |
| − | = | + | =Reference= |
| − | + | DOI: https://doi.org/10.47330/DCIO.2020.KGQD8189 | |
| − | + | Video Presentation: https://youtu.be/BlOp0FPfGHQ | |
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| − | + | Full text: [https://www.designcomputation.org/dcio2020 Maciel, A. (Ed.), 2020. Design Computation Input/Output 2020, 1st ed. Design Computation, London, UK. ISBN: 978-1-83812-940-8, DOI:10.47330/DCIO.2020.QPRF9890] | |
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Latest revision as of 23:32, 14 December 2020
DC I/O 2020 poster by LUKE HALE.
Contents
Abstract
3D printing is becoming a widespread and useful technology in medicine with applications in simulation, teaching, surgical planning and patient-specific prostheses. Typically, in order to 3D print neuroimaging data, anatomical areas of interest must first be identified on individual 2D slices, then isolated (either manually or via thresholding), and then converted to a 3D mesh. This time-consuming ‘segmentation’ typically requires commercial software and, in forming this 3D mesh, the rest of the scan data is lost and reduced to a binary representation i.e. either outside or inside the anatomical area of interest. Furthermore, the size of structures may be over or underestimated. Voxels (volume pixels) represent a value on a 3-dimensional grid, with 3D printing outputting these values to a 3D printed model. Voxel printing can obviate the need for segmentation and 3D mesh generation, with effectively lossless printing of whole neuroimaging datasets. Here, 7T MRI brain images were converted to bitmap images and printed on a Stratasys 760M printer at the printer's native resolution. SimpleITK, an open source library for imaging analysis, was used to interpolate between imaging slices to achieve the required 800dpi slice resolution; a Floyd-Steinberg dithering algorithm was used to convert images to two pixel values representing clear and opaque resin. The resulting printed model has preservation of the delicate cerebral vasculature and differentiation between grey / white matter. Voxel printing of neuroimaging therefore offers exciting possibilities with more accurate visualisation of complex neurological structures, with the added possibility of multi-material gradients which simulate native tissue.
Keywords
Visuospatial, Visual Perception, Visual Quantification, Cyberception, Deep Learning, Neural Networks, Machine learning, Human Navigation, Big Data, Cognising Spaces.
Keyphrases
N/A
Topics
Adaptive ML, DigitalOps, Architecture, Artificial Intelligence in Design, Assisted Design Decision Making, Calculation and Design Analysis, Computational Creativity, Data Visualization and Analysis for design, Design Cognition, Responsive computer-aided design, Urban Design
Reference
DOI: https://doi.org/10.47330/DCIO.2020.KGQD8189
Video Presentation: https://youtu.be/BlOp0FPfGHQ
