Investigation of limitations of optical diffraction tomography

2007 ◽  
Vol 15 (2) ◽  
Author(s):  
T. Kozacki ◽  
M. Kujawińska ◽  
P. Kniażewski
Keyword(s):  
Refractive Index ◽  
Dynamic Range ◽  
Tomographic Reconstruction ◽  
Back Propagation ◽  
Reconstruction Algorithm ◽  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Refractive Index Distribution ◽  
Optical Diffraction Tomography ◽  
Index Distribution

AbstractOptical diffraction tomography (ODT) applied to measurement of optical microelements is limited by low dynamic range, i.e., only objects with small deviations of refractive-index distribution can be measured. Therefore in this paper the limitations and errors of ODT are investigated throughout extensive numerical experiments. It is shown that these errors can be reduced by introduction of additional numerical focusing in the tomographic reconstruction algorithm. Additionally, new tomographic reconstruction algorithm using back propagation in reference medium for optical microelements measurement with known design is proposed. This hybrid reconstruction algorithm allows significant extension of ODT applicability in measurement of elements having large deviations of refractive-index distribution.

Enhancement of optical resolution in three-dimensional refractive-index tomograms of biological samples by employing micromirror-embedded coverslips

Lab on a Chip ◽  
2018 ◽  
Vol 18 (22) ◽  
pp. 3484-3491 ◽  
Author(s):  
Seungwoo Shin ◽  
Jihye Kim ◽  
Je-Ryung Lee ◽  
Eun-chae Jeon ◽  
Tae-Jin Je ◽  
...  
Keyword(s):  
Refractive Index ◽  
Biological Samples ◽  
Three Dimensional ◽  
Optical Resolution ◽  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Optical Diffraction Tomography

Resolution-enhanced optical diffraction tomography using a micromirror-embedded coverslips.

scholarly journals Three-dimensional refractive index distributions of individual angiosperm pollen grains

2018 ◽  
Author(s):  
Chansuk Park ◽  
SangYun Lee ◽  
Geon Kim ◽  
SeungJun Lee ◽  
Jaehoon Lee ◽  
...  
Keyword(s):  
Refractive Index ◽  
Three Dimensional ◽  
Pollen Grains ◽  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Optical Diffraction Tomography ◽  
Internal Structures ◽  
Quantitative Analyses ◽  
Angiosperm Pollen ◽  
Preparation Techniques

Three-dimensional (3D) refractive index (RI) imaging and quantitative analyses of angiosperm pollen grains are presented. Using optical diffraction tomography, the 3D RI structures of individual angiosperm pollen grains were measured without using labeling or other preparation techniques. Various physical quantities, including volume, surface area, exine volume, and sphericity, were determined from the measured RI tomograms of pollen grains. Exine skeletons, the distinct internal structures of angiosperm pollen grains, were identified and systematically analyzed.

scholarly journals Optical Fiber Refractive Index Profiling by Iterative Optical Diffraction Tomography

2018 ◽  
Vol 36 (24) ◽  
pp. 5754-5763 ◽  
Author(s):  
Shengli Fan ◽  
Seth Smith-Dryden ◽  
Jian Zhao ◽  
Stefan Gausmann ◽  
Axel Schulzgen ◽  
...  
Keyword(s):  
Refractive Index ◽  
Optical Fiber ◽  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Optical Diffraction Tomography

scholarly journals Deep learning-based optical field screening for robust optical diffraction tomography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
DongHun Ryu ◽  
YoungJu Jo ◽  
Jihyeong Yoo ◽  
Taean Chang ◽  
Daewoong Ahn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tomographic Reconstruction ◽  
Saliency Map ◽  
Optical Field ◽  
Optical Diffraction ◽  
Training Dataset ◽  
Test Accuracy ◽  
Diffraction Tomography ◽  
Rule Based ◽  
Optical Diffraction Tomography

Abstract In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distil the knowledge of an expert into a deep convolutional neural network. We built an extensive database of optical field images with clean/noisy annotations, and then trained a binary-classification network based upon the data. The trained network outperformed visual inspection by non-expert users and a widely used rule-based algorithm, with >90% test accuracy. Subsequently, we confirmed that the superior screening performance significantly improved the tomogram quality. To further confirm the trained model’s performance and generalisability, we evaluated it on unseen biological cell data obtained with a setup that was not used to generate the training dataset. Lastly, we interpreted the trained model using various visualisation techniques that provided the saliency map underlying each model inference. We envision the proposed network would a powerful lightweight module in the tomographic reconstruction pipeline.

Sign in / Sign up

Export Citation Format

Share Document