# Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model

arXiv: https://arxiv.org/abs/2009.12760 · HTML: https://mlsanity.com/paper/2009.12760/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction](https://mlsanity.com/paper/2211.13926/) (2022)
- [Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction](https://mlsanity.com/paper/2405.17167/) (2024)
- [A Low-dose CT Reconstruction Network Based on TV-regularized OSEM Algorithm](https://mlsanity.com/paper/2408.13832/) (2024)
- [Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows](https://mlsanity.com/paper/2410.17543/) (2024)

## Abstract (arXiv)

Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compensate for the increased noise due to reduction of photon flux. Rather than most existing prior-driven algorithms that benefit from manually designed prior functions or supervised learning schemes, in this work we integrate the data-consistency as a conditional term into the iterative generative model for low-dose CT. At the stage of prior learning, the gradient of data density is directly learned from normal-dose CT images as a prior. Then at the iterative reconstruction stage, the stochastic gradient descent is employed to update the trained prior with annealed and conditional schemes. The distance between the reconstructed image and the manifold is minimized along with data fidelity during reconstruction. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method.