Shape-based regularization of electron tomographic reconstruction

Ajay Gopinath, Guoliang Xu, David Ress, Ozan Öktem, Sriram Subramaniam, Chandrajit Bajaj

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Article number6275494
Pages (from-to)2241-2252
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number12
StatePublished - 2012
Externally publishedYes


  • Bayesian methods
  • electron microscopy
  • reconstruction
  • shape-based regularization
  • tomography

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software


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