1.
Missing Cone Artifact Removal in ODT Using
Unsupervised Deep Learning in the Projection
Domain
BISPL - BioImaging, Signal Processing,
and Learning lab.
KAIST, Korea

2.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Optical Diffraction Tomography
 Measures 3D refractive index with optical illumination
 Reconstruction through field-retrieval (Fourier diffraction theorem)
 GP used to enhance resolution
2

3.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Missing cone prblem
3
… …
𝑘𝑚, 𝑘𝑛
𝑘𝑝
Measurement
(hologram)
 Problem arising in diffraction tomography
• Low axial resolution, elongation in the optical axes
Fourier
Diffraction
theorem

4.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Missing cone prblem
4
… …
𝑘𝑚, 𝑘𝑛
𝑘𝑝
Measurement
(hologram)
 Problem arising in diffraction tomography
• Low axial resolution, elongation in the optical axes
Fourier
Diffraction
theorem

5.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Missing cone prblem
5
… …
𝑘𝑚, 𝑘𝑛
𝑘𝑝
Measurement
(hologram)
 Problem arising in diffraction tomography
• Low axial resolution, elongation in the optical axes
Fourier
Diffraction
theorem

6.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Missing cone prblem
6
… …
𝑘𝑚, 𝑘𝑛
𝑘𝑝
Measurement
(hologram)
Missing cone
Missing cone problem in 3D
 Problem arising in diffraction tomography
• Low axial resolution, elongation in the optical axes

7.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Missing cone prblem
7
Low resolution
 Problem arising in diffraction tomography
• Low axial resolution, elongation in the optical axes

8.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Research Motivation
8
Parallel-ray
projections
gp-reconstruction  Close relationship btw. non-diffraction / diffraction tomography
Approximation possible!

9.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Research Motivation
9
Parallel-ray
projections
gp-reconstruction  Close relationship btw. non-diffraction / diffraction tomography
• Projections aligned w. measurement angle: high resolution
• Projections un-aligned w. measurement angle: low resolution
𝒴Ω

10.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Research Motivation
10
Parallel-ray
projections
gp-reconstruction  Close relationship btw. non-diffraction / diffraction tomography
• Projections aligned w. measurement angle: high resolution
• Projections un-aligned w. measurement angle: low resolution
𝒴Ω 𝒴Ωc

11.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Research Motivation
11
Parallel-ray
projections
gp-reconstruction  Close relationship btw. non-diffraction / diffraction tomography
• Projections aligned w. measurement angle: high resolution
• Projections un-aligned w. measurement angle: low resolution
𝒴Ω 𝒴Ωc
projectionGAN
0° 20° 40° 80° 100°
60°

12.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
projectionGAN
Field-retrieval
GP-reconstruction
… …
1st 23th 49th
Cell
Source
Incident Wave
Diffracted
Wave
Measured
Hologram
1. Reconstruction
12

13.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
projectionGAN
Field-retrieval
GP-reconstruction
… …
1st 23th 49th
Cell
Source
Incident Wave
Diffracted
Wave
TomoGAN
Final reconstruction
X-ray transform
𝜔1 𝜔2 𝜔3 𝜔4 𝜔5
𝜔1 𝜔2 𝜔3 𝜔4 𝜔5
FBP
Measured
Hologram
13
2. projectionGAN
1. Reconstruction

14.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Results: numerical simulation
14
Elongation
False signal
projectionGAN

15.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Results: microbead
15
Missing cone
Inhomogeneous
shape
RI (True: 1.46)
projectionGAN

16.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Results: biological cells
16

17.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Results: biological cells
17

18.
Part I: Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain
Results: biological cells
18