# MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images

arXiv: https://arxiv.org/abs/2306.14274 · HTML: https://mlsanity.com/paper/2306.14274/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Learning Steerable Filters for Rotation Equivariant CNNs](https://mlsanity.com/paper/1711.07289/) (2017)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction](https://mlsanity.com/paper/2108.10991/) (2021)
- [Scale-Equivariant Steerable Networks](https://mlsanity.com/paper/1910.11093/) (2019)
- [Equivariant Imaging: Learning Beyond the Range Space](https://mlsanity.com/paper/2103.14756/) (2021)
- [DuDoNet: Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/1907.00273/) (2019)
- [Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction](https://mlsanity.com/paper/1803.00694/) (2018)
- [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](https://mlsanity.com/paper/2012.08721/) (2020)
- [Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database](https://mlsanity.com/paper/1711.10535/) (2017)
- [Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2009.07469/) (2020)
- [TransCT: Dual-path Transformer for Low Dose Computed Tomography](https://mlsanity.com/paper/2103.00634/) (2021)
- [PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions](https://mlsanity.com/paper/2007.10408/) (2020)
- [ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/1908.01104/) (2019)
- [Equivariant neural networks for inverse problems](https://mlsanity.com/paper/2102.11504/) (2021)
- [ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI](https://mlsanity.com/paper/1705.06869/) (2017)
- [Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions](https://mlsanity.com/paper/2107.14519/) (2021)
- [MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction](https://mlsanity.com/paper/2006.00171/) (2020)
- [InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2109.05298/) (2021)
- [DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction](https://mlsanity.com/paper/2111.10790/) (2021)
- [Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction](https://mlsanity.com/paper/2204.10436/) (2022)

## Built on by (most cited first, 6 of 6)

- [3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation](https://mlsanity.com/paper/2312.15676/) (2023)
- [Towards a Medical AI Scientist](https://mlsanity.com/paper/2603.28589/) (2026)
- [Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstruction](https://mlsanity.com/paper/2511.20296/) (2025)
- [PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement](https://mlsanity.com/paper/2408.03657/) (2024)
- [Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data](https://mlsanity.com/paper/2503.21259/) (2025)
- [Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.06622/) (2026)

## Abstract (arXiv)

Sparse-view computed tomography (CT) has been adopted as an important technique for speeding up data acquisition and decreasing radiation dose. However, due to the lack of sufficient projection data, the reconstructed CT images often present severe artifacts, which will be further amplified when patients carry metallic implants. For this joint sparse-view reconstruction and metal artifact reduction task, most of the existing methods are generally confronted with two main limitations: 1) They are almost built based on common network modules without fully embedding the physical imaging geometry constraint of this specific task into the dual-domain learning; 2) Some important prior knowledge is not deeply explored and sufficiently utilized. Against these issues, we specifically construct a dual-domain reconstruction model and propose a model-driven equivariant proximal network, called MEPNet. The main characteristics of MEPNet are: 1) It is optimization-inspired and has a clear working mechanism; 2) The involved proximal operator is modeled via a rotation equivariant convolutional neural network, which finely represents the inherent rotational prior underlying the CT scanning that the same organ can be imaged at different angles. Extensive experiments conducted on several datasets comprehensively substantiate that compared with the conventional convolution-based proximal network, such a rotation equivariance mechanism enables our proposed method to achieve better reconstruction performance with fewer network parameters. We will release the code at urlhttps://github.com/hongwang01/MEPNet.