Dictionary-Learning-Based Reconstruction Method for Electron Tomography
This work addresses reconstruction challenges in electron tomography for materials science or biology, but it is incremental as it applies existing methods to a specific context.
The paper tackled the problem of missing wedge artifacts in electron tomography by evaluating and comparing three reconstruction methods (EST, ADSIR, OS-SART) and two data acquisition modes (ES and EA). The results showed that ADSIR outperformed EST and OS-SART, and the equally sloped mode offered no advantage over the conventional equally angled mode.
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 the context.