Shape-Based Regularization of Electron Tomographic Reconstruction

被引:8
作者
Gopinath, Ajay [1 ]
Xu, Guoliang [2 ]
Ress, David [3 ]
Oktem, Ozan [4 ]
Subramaniam, Sriram [5 ]
Bajaj, Chandrajit [6 ,7 ]
机构
[1] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA
[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
[3] Univ Texas Austin, Neurobiol & Imaging Res Ctr, Austin, TX 78712 USA
[4] KTH Royal Inst Technol, Ctr Ind & Appl Math, Dept Math, SE-10044 Stockholm, Sweden
[5] NCI, Cell Biol Lab, NIH, Bethesda, MD 20892 USA
[6] Univ Texas Austin, Dept Comp Sci, Austin, TX 78712 USA
[7] Univ Texas Austin, Inst Computat Engn & Sci, Austin, TX 78712 USA
基金
美国国家卫生研究院;
关键词
Bayesian methods; electron microscopy; reconstruction; shape-based regularization; tomography; LEVEL SET METHOD; ANATOMICAL INFORMATION; ALGORITHM;
D O I
10.1109/TMI.2012.2214229
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We introduce a tomographic reconstruction method implemented using a shape-based regularization technique. Spatial models of known features in the structure being reconstructed are integrated into the reconstruction process as regularizers. Our regularization scheme is driven locally through shape information obtained from segmentation and compared with a known spatial model. We demonstrated our method on tomography data from digital phantoms, simulated data, and experimental electron tomography (ET) data of virus complexes. Our reconstruction showed reduced blurring and an improvement in the resolution of the reconstructed volume was also measured. This method also produced improved demarcation of spike boundaries in viral membranes when compared with popular techniques like weighted back projection and the algebraic reconstruction technique. Improved ET reconstructions will provide better structure elucidation and improved feature visualization, which can aid in solving key biological issues. Our method can also be generalized to other tomographic modalities.
引用
收藏
页码:2241 / 2252
页数:12
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