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Human brain deformation simulation method based on multiple GPUs

A simulation method, the technology of the human brain, applied to the details of 3D image data, details of image processing hardware, image data processing, etc., can solve problems such as slow calculation speed

Inactive Publication Date: 2016-03-09
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The calculation speed is too slow, which becomes the bottleneck that prevents the nonlinear model from being put into practical application

Method used

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  • Human brain deformation simulation method based on multiple GPUs
  • Human brain deformation simulation method based on multiple GPUs
  • Human brain deformation simulation method based on multiple GPUs

Examples

Experimental program
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Embodiment Construction

[0029] Such as figure 1 , 2 Shown, concrete steps of the present invention are as follows:

[0030] (1) Read in the human brain model data, use the breadth search to analyze the topological structure relationship to obtain the set of adjacency units, and save it in the hash table. Initialize the cell access bit and node access bit list to false.

[0031] (2) Select the starting reference node of the group, set it as the current access node, and set the node access identifier as true. Set the current group as the first group.

[0032] (3) Extract the volume units contained in the current node from the hash table storing the collection of adjacent volume units, push all units whose access bits are true into the current unit grouping, and set the unit access bits of these units to be true, Then push the nodes whose node access bits are false in other nodes of the contained unit into the node queue in turn.

[0033](4) Judging whether all nodes have completed the traversal, i...

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Abstract

The invention relates to a human brain deformation simulation method based on multiple GPUs. A total Lagrangian Method, a central difference method and a nonlinear finite element Neo-Hookean model are used for carrying out explicit iterative simulation on brain deformation, the speed of simulation calculation is quickened through parallel algorithm at GPU ends, after the parallel ability is further improved through introducing the multiple GPUs, a breadth first search method is adopted to carry out data clustering on original data and renumber and minimize a data correlation between nodes, an extra data structure is used to enable a one-to-one correspondence relationship to exist between input data in each node and the calculation process, a stream transmission is further used to realize parallelism between calculation and data transmission, and finally, a multi-core calculation architecture mixing CPUs / GPUs is used for accelerating, and precision and speed requirements in human brain deformation simulation are met. Subtlety and spatio-temporal continuity features in human brain deformation are made full use of, and the brain deformation process can be vividly simulated in a virtual environment constructed by a computer.

Description

technical field [0001] The invention relates to a multi-GPU-based human brain deformation simulation method, which belongs to the field of computer virtual reality. Background technique [0002] The navigation system of neurosurgery appeared in the 1990s. By combining modern neuroimaging (MRI, CT and other medical images), computer three-dimensional image processing, brain stereotaxic and microneurosurgery, the accuracy and accuracy of neurosurgery can be improved. safety. But brain shift is always a difficult technical problem for conventional navigation systems. In actual surgery, brain deformation often occurs due to the influence of biomechanical properties of brain tissue, gravity, changes in intracranial pressure, and surgical operations. Intraoperative brain deformation will lead to a decrease in navigation and positioning accuracy, which will seriously affect the reliability of the positioning system if it exceeds the medically acceptable range. Therefore, the res...

Claims

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Application Information

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IPC IPC(8): G06T17/30G06T1/20
CPCG06T1/20G06T17/30G06T2200/04G06T2200/28
Inventor 胡勇田野沈旭昆
Owner BEIHANG UNIV
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