Slide for Cha et al. "Low-dose sparse-view HAADF-STEM-EDX tomography of nanocrystals using unsupervised deep learning"

1. Low-dose sparse-view HAADF-STEM-EDX
tomography of nanocrystals using
unsupervised deep learning
Eunju Cha*, Hyungjin Chung*, Jaeduck Jang, Junho Lee, Eunha Lee, Jong Chul Ye
*: Equal contribution
Hyungjin Chung

2. Introduction

3. HAADF-STEM-EDX tomography
• High-angle annular dark-field (HAADF)
• Energy dispersive X-ray (EDX)
• Both signals acquired through scanning
transmission electron microscopy (STEM)
• Signals are…
• Sparse view: 2~13 views
• Low dose: very low SNR

4. Method

5. Unsupervised method for 3D tomographic reconstruction
CycleGAN-based kernel regression
• Physics informed
cycleGAN for
unsupervised kernel
regression network
• Exploit the correlation btw.
EDX / z-contrast images
• Dramatically increase SNR

6. Unsupervised method for 3D tomographic reconstruction
ProjectionGAN for 3D recon.
• CycleGAN boosts quality of unmeasured-view projections - ProjectionGAN

7. Results

8. Kernel regression results
proposed

9. Phantom 3D reconstruction

10. Quantom dot (QD) 3D reconstruction (3-view)