Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning.
ACS Nano
; 16(7): 10314-10326, 2022 07 26.
Article
em En
| MEDLINE
| ID: mdl-35729795
ABSTRACT
High-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) can be acquired together with energy dispersive X-ray (EDX) spectroscopy to give complementary information on the nanoparticles being imaged. Recent deep learning approaches show potential for accurate 3D tomographic reconstruction for these applications, but a large number of high-quality electron micrographs are usually required for supervised training, which may be difficult to collect due to the damage on the particles from the electron beam. To overcome these limitations and enable tomographic reconstruction even in low-dose sparse-view conditions, here we present an unsupervised deep learning method for HAADF-STEM-EDX tomography. Specifically, to improve the EDX image quality from low-dose condition, a HAADF-constrained unsupervised denoising approach is proposed. Additionally, to enable extreme sparse-view tomographic reconstruction, an unsupervised view enrichment scheme is proposed in the projection domain. Extensive experiments with different types of quantum dots show that the proposed method offers a high-quality reconstruction even with only three projection views recorded under low-dose conditions.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Nanopartículas
/
Aprendizado Profundo
Idioma:
En
Revista:
ACS Nano
Ano de publicação:
2022
Tipo de documento:
Article