DCIO.2020.KGQD8189 - Revision history

Abel Maciel: Redirected page to Voxel Printing of Neuroimaging

2020-10-02T03:03:44Z

Redirected page to Voxel Printing of Neuroimaging

Abel Maciel at 16:03, 3 September 2020

2020-09-03T16:03:29Z

Abel Maciel: /* Voxel Printing of Neuroimaging */

2020-09-03T16:03:00Z

‎Voxel Printing of Neuroimaging

Abel Maciel: Changed protection level for "DCIO.2020.KGQD8189" ([Edit=] (indefinite) [Move=] (indefinite))

2020-09-03T15:23:25Z

Changed protection level for "DCIO.2020.KGQD8189" ([Edit=<protect-level-reviewer>] (indefinite) [Move=<protect-level-reviewer>] (indefinite))

Abel Maciel: Protected "DCIO.2020.KGQD8189" ([Move=] (indefinite))

2020-09-03T15:23:15Z

Protected "DCIO.2020.KGQD8189" ([Move=<protect-level-reviewer>] (indefinite))

Abel Maciel: Created page with "#REDIRECT [[]] Category:DCIO Category:DCIO2020 Category:DCIO Posters Category:DCIO WIP Category:Conferences =Voxel Printing of Neuroimaging= by LUKE HAL..."

2020-09-03T15:22:14Z

Created page with "#REDIRECT [[]] Category:DCIO Category:DCIO2020 Category:DCIO Posters Category:DCIO WIP Category:Conferences =Voxel Printing of Neuroimaging= by LUKE HAL..."

New page

#REDIRECT [[]]
[[Category:DCIO]]
[[Category:DCIO2020]]
[[Category:DCIO Posters]]
[[Category:DCIO WIP]]
[[Category:Conferences]]

=Voxel Printing of Neuroimaging=
by [[LUKE HALE]].

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.

=Reference=
[[Poster Image]]

← Older revision		Revision as of 03:03, 2 October 2020
Line 1:		Line 1:
−	#REDIRECT [[]]	+	#REDIRECT [[Voxel Printing of Neuroimaging]]
−	~~[[Category:DCIO]]~~
−	~~[[Category:DCIO2020]]~~
−	~~[[Category:DCIO Posters]]~~
−	~~[[Category:DCIO WIP]]~~
−	~~[[Category:Conferences]]~~
−
−	=Voxel Printing of Neuroimaging=
−	~~by [[LUKE HALE]].~~
−
−
−	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.
−
−	~~=Topics=~~
−	~~[[Bio-Integrated Design]], [[Digital Fabrication]], [[Future healthcare]], [[Healthcare]].~~
−
−	~~=Keywords=~~
−	~~[[Voxel printing]], [[Medical Imaging]], [[3D printing]].~~
−
−	~~=Reference=~~
−	~~[[Poster Image]]~~
−
−	~~=References=~~
−	~~[[LINK~~]]

← Older revision		Revision as of 16:03, 3 September 2020
Line 20:		Line 20:
	=Reference=		=Reference=
	[[Poster Image]]		[[Poster Image]]
		+
		+	=References=
		+	[[LINK]]

@@ Line 10: / Line 10: @@
-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
+D 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.
 i.e. either outside or inside the anatomical area of interest. Furthermore, the size of structures may be over or underestimated.
 =Reference=
 [[Poster Image]]

← Older revision	Revision as of 15:23, 3 September 2020
(No difference)