# Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splitting

arXiv: https://arxiv.org/abs/1909.09773 · HTML: https://mlsanity.com/paper/1909.09773/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning](https://mlsanity.com/paper/2206.07050/) (2022)
- [Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation](https://mlsanity.com/paper/2103.05255/) (2021)
- [LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction](https://mlsanity.com/paper/2208.00207/) (2022)
- [Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction](https://mlsanity.com/paper/2405.17167/) (2024)
- [A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction](https://mlsanity.com/paper/2205.00463/) (2022)
- [Efficient Unrolled Networks for Large-Scale 3D Inverse Problems](https://mlsanity.com/paper/2601.02141/) (2026)
- [PLOT-CT: Pre-log Voronoi Decomposition Assisted Generation for Low-dose CT Reconstruction](https://mlsanity.com/paper/2602.11625/) (2026)
- [Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization](https://mlsanity.com/paper/2605.25348/) (2026)
- [Deep-layer limit and stability analysis of the basic forward-backward-splitting induced network (II): learning problems](https://mlsanity.com/paper/2605.27133/) (2026)

## Abstract (arXiv)

Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on unrolling of proximal forward-backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast with PFBS-IR that utilizes standard data fidelity updates via iterative reconstruction (IR) method, PFBS-AIR involves preconditioned data fidelity updates that fuse analytical reconstruction (AR) method and IR in a synergistic way, I.e. fused analytical and iterative reconstruction (AIR). The results suggest that DL-regularized methods (PFBS-IR and PFBS-AIR) provided better reconstruction quality from conventional wisdoms (AR or IR), and DL-based postprocessing method (FBPConvNet). In addition, owing to AIR, PFBS-AIR noticeably outperformed PFBS-IR.