Compressed sensing for dose reduction in STEM tomography

L. Donati; M. Nilchian; M. Unser; S. Trepout; C. Messaoudi; S. Marcoy

doi:10.1109/ISBI.2017.7950459

Compressed sensing for dose reduction in STEM tomography

L. Donati, M. Nilchian, M. Unser, S. Trepout, C. Messaoudi, S. Marcoy

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

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.

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
Editors	Simon Warfield, Arrete Munoz-Barrutia
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	23-27
Number of pages	5
ISBN (Electronic)	9781509011711, 9781509011728
ISBN (Print)	9781509011735
DOIs	https://doi.org/10.1109/ISBI.2017.7950459
Publication status	Published - 15 Jun 2017
Externally published	Yes
Event	IEEE International Symposium on Biomedical Imaging (ISBI) 2017 - Melbourne Convention and Exhibition Centre, Melbourne, Australia Duration: 18 Apr 2017 → 21 Apr 2017 Conference number: 14th https://ieeexplore.ieee.org/xpl/conhome/7944115/proceeding (IEEE proceedings) http://biomedicalimaging.org/2017/

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	IEEE International Symposium on Biomedical Imaging (ISBI) 2017
Abbreviated title	ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	18/04/17 → 21/04/17
Other	ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). The 2017 meeting will include tutorials, and a scientific program composed of plenary talks, invited special sessions, challenges, as well as oral and poster presentations of peer-reviewed papers.
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7944115/proceeding (IEEE proceedings) http://biomedicalimaging.org/2017/

Keywords

Compressed sensing
Dose reduction
Random-beam scanning
Regularized reconstruction
STEM tomography

Access to Document

10.1109/ISBI.2017.7950459

Cite this

Donati, L., Nilchian, M., Unser, M., Trepout, S., Messaoudi, C., & Marcoy, S. (2017). Compressed sensing for dose reduction in STEM tomography. In S. Warfield, & A. Munoz-Barrutia (Eds.), 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 23-27). (Proceedings - International Symposium on Biomedical Imaging). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISBI.2017.7950459

Donati, L. ; Nilchian, M. ; Unser, M. et al. / Compressed sensing for dose reduction in STEM tomography. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). editor / Simon Warfield ; Arrete Munoz-Barrutia. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 23-27 (Proceedings - International Symposium on Biomedical Imaging).

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abstract = "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.",

keywords = "Compressed sensing, Dose reduction, Random-beam scanning, Regularized reconstruction, STEM tomography",

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Donati, L, Nilchian, M, Unser, M, Trepout, S, Messaoudi, C & Marcoy, S 2017, Compressed sensing for dose reduction in STEM tomography. in S Warfield & A Munoz-Barrutia (eds), 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Proceedings - International Symposium on Biomedical Imaging, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 23-27, IEEE International Symposium on Biomedical Imaging (ISBI) 2017, Melbourne, Victoria, Australia, 18/04/17. https://doi.org/10.1109/ISBI.2017.7950459

Compressed sensing for dose reduction in STEM tomography. / Donati, L.; Nilchian, M.; Unser, M. et al.
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). ed. / Simon Warfield; Arrete Munoz-Barrutia. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 23-27 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Nilchian, M.

AU - Unser, M.

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AU - Messaoudi, C.

AU - Marcoy, S.

N1 - Conference code: 14th

PY - 2017/6/15

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N2 - 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.

AB - 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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ER -

Donati L, Nilchian M, Unser M, Trepout S, Messaoudi C, Marcoy S. Compressed sensing for dose reduction in STEM tomography. In Warfield S, Munoz-Barrutia A, editors, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 23-27. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2017.7950459

Compressed sensing for dose reduction in STEM tomography

Abstract

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Keywords

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