title: Dictionary-learning-based reconstruction method for electron tomography.
abstract: Electron tomography usually suffers from so-called “missing wedge” artifacts caused by limited tilt angle range. An equally sloped tomography (EST) acquisition scheme (which should be called the linogram sampling scheme) was recently applied to achieve 2.4-angstrom resolution. On the other hand, a compressive sensing inspired reconstruction algorithm, known as adaptive dictionary based statistical iterative reconstruction (ADSIR), has been reported for X-ray computed tomography. In this paper, we evaluate the EST, ADSIR, and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes. Our results show that OS-SART is comparable to EST, and the ADSIR outperforms EST and OS-SART. Furthermore, the equally sloped projection data acquisition mode has no advantage over the conventional equally angled mode in this context.
authors.forename: Baodong
authors.initials: B
authors.lastname: Liu
authors.forename: Hengyong
authors.initials: H
authors.lastname: Yu
authors.forename: Scott S
authors.initials: SS
authors.lastname: Verbridge
authors.forename: Lizhi
authors.initials: L
authors.lastname: Sun
authors.forename: Ge
authors.initials: G
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055032:Electron Microscope Tomography / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*
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