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Surface mapping brain function on 3D models
Source: IEEE Computer Graphics and Applications 1990 Dec;10(5):33-41.
Author: Payne BA, Toga AW.
Abstract:
As neuroscientists gained a more comprehensive understanding of brain function, they came to rely on quantitative, physiological indicators in all three spatial dimensions. Although it is possible to construct a quantitative volume of brain data, we still need methods to generate informative and realistic digital images from these volumes. We present a flexible graphics system for displaying functional and anatomic data on arbitrary collections of surfaces on or within the brain. The resulting displays provide quantitative information on the magnitude of functional activity as well as accurate perception of surface form. This mapping is demonstrated on a new type of surface, the removed surface. These techniques have greatly improved our ability to study the structure-function relationships in the whole brain. METHODS: We have divided functional mapping into 3 basic stages: surface modeling, the formation of surface whose quantitative behavior we will examine; volume modeling, the formation of the volume of density data to supply the quantitative information; and surface viewing, the synthesis of digital images from these models. The major surfaces we modeled were brain cortex, subcortex at a constant subsurface depth, internal structures, andother anatomical forms. Planes were also used in cutaway views. From quantitative data for mapping onto the surfaces, we constructed a solid-texture function. The original sections supplied density data for the same planes in space for which contours were available. Surface viewing required distinct stages. 1st, object specification designated various parameters controlling the viewing of objects, wtih respect to such things as color, transparency, hardness, and priority. Next, we applied geometric transformations for rotation, translation, scaling, and perspective. The final rendering process displayed surfaces of triangular models with an a-buffer method and applied solid texturing where appropriate. CONCLUSION: We have shown a system for displaying neurobiological surface models with reference to structure and function. We combined quantitative data with surface rendering and shading algorithms. The functional mapping allows both form and function to be appreciated in 3D. By removing the outer surface, we displayed interior surfaces, and thus, we can view the internal structure and function of the brain.
Source: IEEE Computer Graphics and Applications 1990 Dec;10(5):33-41.
Author: Payne BA, Toga AW.
Abstract:
As neuroscientists gained a more comprehensive understanding of brain function, they came to rely on quantitative, physiological indicators in all three spatial dimensions. Although it is possible to construct a quantitative volume of brain data, we still need methods to generate informative and realistic digital images from these volumes. We present a flexible graphics system for displaying functional and anatomic data on arbitrary collections of surfaces on or within the brain. The resulting displays provide quantitative information on the magnitude of functional activity as well as accurate perception of surface form. This mapping is demonstrated on a new type of surface, the removed surface. These techniques have greatly improved our ability to study the structure-function relationships in the whole brain. METHODS: We have divided functional mapping into 3 basic stages: surface modeling, the formation of surface whose quantitative behavior we will examine; volume modeling, the formation of the volume of density data to supply the quantitative information; and surface viewing, the synthesis of digital images from these models. The major surfaces we modeled were brain cortex, subcortex at a constant subsurface depth, internal structures, andother anatomical forms. Planes were also used in cutaway views. From quantitative data for mapping onto the surfaces, we constructed a solid-texture function. The original sections supplied density data for the same planes in space for which contours were available. Surface viewing required distinct stages. 1st, object specification designated various parameters controlling the viewing of objects, wtih respect to such things as color, transparency, hardness, and priority. Next, we applied geometric transformations for rotation, translation, scaling, and perspective. The final rendering process displayed surfaces of triangular models with an a-buffer method and applied solid texturing where appropriate. CONCLUSION: We have shown a system for displaying neurobiological surface models with reference to structure and function. We combined quantitative data with surface rendering and shading algorithms. The functional mapping allows both form and function to be appreciated in 3D. By removing the outer surface, we displayed interior surfaces, and thus, we can view the internal structure and function of the brain.