Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning,ACS Nano

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Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning
ACS Nano ( IF 15.8 ) Pub Date : 2022-06-21 , DOI: 10.1021/acsnano.2c00168
Eunju Cha ₁ , Hyungjin Chung ₂ , Jaeduck Jang ₁ , Junho Lee ₁ , Eunha Lee ₁ , Jong Chul Ye ₃

Affiliation

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.

中文翻译：

使用无监督深度学习的纳米晶体低剂量稀疏视图 HAADF-STEM-EDX 层析成像

高角度环形暗场 (HAADF) 扫描透射电子显微镜 (STEM) 可以与能量色散 X 射线 (EDX) 光谱学一起获得，以提供有关正在成像的纳米粒子的补充信息。最近的深度学习方法显示了这些应用的精确 3D 断层扫描重建的潜力，但监督训练通常需要大量高质量的电子显微照片，由于电子束对粒子的损坏，这些照片可能难以收集。为了克服这些限制并即使在低剂量稀疏视图条件下也能进行断层扫描重建，我们在这里提出了一种用于 HAADF-STEM-EDX 断层扫描的无监督深度学习方法。具体来说，为了提高低剂量条件下的 EDX 图像质量，提出了一种 HAADF 约束的无监督去噪方法。此外，为了实现极端稀疏视图层析成像重建，在投影域中提出了一种无监督视图丰富方案。对不同类型量子点的大量实验表明，所提出的方法即使仅使用在低剂量条件下记录的三个投影视图。

更新日期：2022-06-21

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