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Compressed Sensing for Dose Reduction in STEM Tomography

L. Donati, M. Nilchian, M. Unser, S. Trépout, C. Messaoudi, S. Marco

Proceedings of the Fourteenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'17), Melbourne, Commonwealth of Australia, April 18-21, 2017, pp. 23-27.



We designed a complete acquisition-reconstruction framework to reduce the radiation dosage in 3D scanning transmission electron microscopy (STEM). Projection measurements are acquired by randomly scanning a subset of pixels at every tilt-view (i.e., random-beam STEM or RB-STEM ). High-quality images are then recovered from the randomly downsampled measurements through a regularized tomographic reconstruction framework. By fulfilling the compressed sensing requirements, the proposed approach improves the reconstruction of heavily-downsampled RB-STEM measurements over the current state-of-the-art technique. This development opens new perspectives in the search for methods permitting lower-dose 3D STEM imaging of electron-sensitive samples without degrading the quality of the reconstructed volume. A Matlab code implementing the proposed reconstruction algorithm has been made available online.


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