# Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain

arXiv: https://arxiv.org/abs/2103.09022 · HTML: https://mlsanity.com/paper/2103.09022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [TorchRadon: Fast Differentiable Routines for Computed Tomography](https://mlsanity.com/paper/2009.14788/) (2020)
- [Diffraction tomography with a deep image prior](https://mlsanity.com/paper/1912.05330/) (2019)
- [Quantitative Phase Imaging and Artificial Intelligence: A Review](https://mlsanity.com/paper/1806.03982/) (2018)
- [CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry](https://mlsanity.com/paper/1908.09414/) (2019)
- [DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging using Deep Learning](https://mlsanity.com/paper/2009.13777/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-based Diffusion Models for Electrical Impedance Tomography](https://mlsanity.com/paper/2310.15831/) (2023)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)
- [Pose-Free 3D Quantitative Phase Imaging of Flowing Cellular Populations](https://mlsanity.com/paper/2509.04848/) (2025)

## Abstract (arXiv)

Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles. Although the distribution of RI index is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along axial direction compared to the horizontal imaging plane. To solve this issue, here we present a novel unsupervised deep learning framework, which learns the probability distribution of missing projection views through optimal transport driven cycleGAN. Experimental results show that missing cone artifact in ODT can be significantly resolved by the proposed method.