Mesh optimization for Monte Carlo based optical tomography

被引:0
|
作者
Edmans, Andrew [1 ]
Intes, Xavier [1 ]
Smith, Cameron [2 ]
机构
[1] Rensselaer Polytech Inst, Dept Biomed Engn, Troy, NY 12180 USA
[2] Rensselaer Polytech Inst, Sci Computat Res Ctr, Troy, NY 12180 USA
来源
2014 40TH ANNUAL NORTHEAST BIOENGINEERING CONFERENCE (NEBEC) | 2014年
关键词
Monte Carlo; optical tomography; mesh optimization; fluorescence molecular tomogrpahy; fluorescence; preclinical imaging;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Fluorescence Molecular Tomography aims to reconstruct the 3D distribution of fluorescent markers in bio-tissues based on 2D surface measurements of emitted photons. This technique requires an accurate model of light propagation, the gold standard of which is created by the Monte Carlo ( MC) method. One drawback of MC is the computational burden associated with the need to simulate large packet of photons to sample the volume to be imaged with accuracy. Recent developments in MC formulation and massively parallel computing geared towards optical tomogrpahy have aloo0wed to alleviate this issue. Especially, mesh based MC techniques have shown favorable computational costs compared to voxel-based MC. Herein, we investigate the potential of mesh optimization strategies for computationally efficient and accurate Monte Carlo based optical tomography. Using a mouse model created from mu CT data and average murine optical properties, we investigate the potential of an iterative mesh refinement strategy. Performances of the method are evaluated in the image space. Our preliminary results indicate that accuracy improves over several iterations when mesh refinement is employed.
引用
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页数:2
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