Statistical Reconstruction Methods for 3D Imaging of Biological Samples with Electron Microscopy
|Kustantaja||Tampere University of Technology|
|Tila||Julkaistu - 18 toukokuuta 2018|
|Nimi||Tampere University of Technology. Publication|
Physical and mechanical limitations can prevent to acquire projection images that cover the projection angle space completely and uniformly. Incomplete and non-uniform sampling of the projection angles results in anisotropic resolution in the image plane and generates artifacts. Another problem is that the total applied dose of electrons is limited in order to prevent the radiation damage to the biological target. Therefore, limited number of projection images with low signal to noise ratio can be used in the reconstruction process. This affects the resolution of the reconstructed image significantly. This study presents statistical methods to overcome these major challenges to obtain precise and high resolution images in electron microscopy.
Statistical image reconstruction methods have been successful in recovering a signal from imperfect measurements due to their capability of utilizing a priori information. First, we developed a sequential application of a statistical method for ET. Then we extended the method to support projection angles freely distributed in 3D space and applied the method in SPR. In both applications, we observed the strength of the method in projection gap filling, robustness against noise, and resolving the high resolution details in comparison with the conventional reconstruction methods. Afterwards, we improved the method in terms of computation time by incorporating multiresolution reconstruction. Furthermore, we developed an adaptive regularization method to minimize the parameters required to be set by the user. We also proposed the local adaptive Wiener filter for the class averaging step of SPR to improve the averaging accuracy.
The qualitative and quantitative analysis of the reconstructions with phantom and experimental datasets has demonstrated that the proposed reconstruction methods outperform the conventional reconstruction methods. These statistical approaches provided better image accuracy and higher resolution compared with the conventional algebraic and transfer domain based reconstruction methods. The methods provided in this study contribute to enhance our understanding of cellular and molecular structures by providing 3D images of those with improved accuracy and resolution.