# Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging

arXiv: https://arxiv.org/abs/1907.01262 · HTML: https://mlsanity.com/paper/1907.01262/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Robust and Efficient Medical Imaging with Self-Supervision](https://mlsanity.com/paper/2205.09723/) (2022)
- [Supervised Transfer Learning at Scale for Medical Imaging](https://mlsanity.com/paper/2101.05913/) (2021)
- [Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning](https://mlsanity.com/paper/2211.08559/) (2022)
- [Clustering of illustrations by atmosphere using a combination of supervised and unsupervised learning](https://mlsanity.com/paper/2307.15099/) (2023)

## Abstract (arXiv)

X-ray computed tomography (CT) reconstructs cross-sectional images from projection data. However, ionizing X-ray radiation associated with CT scanning might induce cancer and genetic damage. Therefore, the reduction of radiation dose has attracted major attention. Few-view CT image reconstruction is an important topic to reduce the radiation dose. Recently, data-driven algorithms have shown great potential to solve the few-view CT problem. In this paper, we develop a dual network architecture (DNA) for reconstructing images directly from sinograms. In the proposed DNA method, a point-based fully-connected layer learns the backprojection process requesting significantly less memory than the prior arts do. Proposed method uses O(C*N*N_c) parameters where N and N_c denote the dimension of reconstructed images and number of projections respectively. C is an adjustable parameter that can be set as low as 1. Our experimental results demonstrate that DNA produces a competitive performance over the other state-of-the-art methods. Interestingly, natural images can be used to pre-train DNA to avoid overfitting when the amount of real patient images is limited.