# MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction

arXiv: https://arxiv.org/abs/2006.00171 · HTML: https://mlsanity.com/paper/2006.00171/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 23 of 23)

- [DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction](https://mlsanity.com/paper/2404.17890/) (2024)
- [Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey](https://mlsanity.com/paper/2203.15725/) (2022)
- [Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation](https://mlsanity.com/paper/2103.05255/) (2021)
- [Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction](https://mlsanity.com/paper/2412.09331/) (2024)
- [TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D DSA Rendering](https://mlsanity.com/paper/2403.19586/) (2024)
- [MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2306.14274/) (2023)
- [Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning](https://mlsanity.com/paper/2203.14794/) (2022)
- [Active CT Reconstruction with a Learned Sampling Policy](https://mlsanity.com/paper/2211.01670/) (2022)
- [Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views](https://mlsanity.com/paper/2305.19767/) (2023)
- [Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator](https://mlsanity.com/paper/2310.03118/) (2023)
- [Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstruction](https://mlsanity.com/paper/2511.20296/) (2025)
- [MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction](https://mlsanity.com/paper/2405.17141/) (2024)
- [CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates](https://mlsanity.com/paper/2409.01571/) (2024)
- [Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction](https://mlsanity.com/paper/2601.15098/) (2026)
- [AI-Enabled Ultra-Low-Dose CT Reconstruction](https://mlsanity.com/paper/2106.09834/) (2021)
- [A Note on Machine Learning Approach for Computational Imaging](https://mlsanity.com/paper/2202.11883/) (2022)
- [A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems](https://mlsanity.com/paper/2303.03678/) (2023)
- [APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging](https://mlsanity.com/paper/2307.05270/) (2023)
- [CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging](https://mlsanity.com/paper/2406.14976/) (2024)
- [Deep Inertia Lp Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction](https://mlsanity.com/paper/2408.06600/) (2024)
- [A Warm-basis Method for Bridging Learning and Iteration: a Case Study in Fluorescence Molecular Tomography](https://mlsanity.com/paper/2510.05926/) (2025)
- [HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis](https://mlsanity.com/paper/2604.03224/) (2026)
- [LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2606.16212/) (2026)

## Abstract (arXiv)

X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm. However, unlike the existing strategy to include as many data-adaptive components in the unrolled dynamics model as possible, we find that it is enough to only learn the parts where traditional designs mostly rely on intuitions and experience. More specifically, we propose to learn an initializer for the conjugate gradient (CG) algorithm that involved in one of the subproblems of the backbone model. Other components, such as image priors and hyperparameters, are kept as the original design. Since a hypernetwork is introduced to inference on the initialization of the CG module, it makes the proposed model a certain meta-learning model. Therefore, we shall call the proposed model the meta-inversion network (MetaInv-Net). The proposed MetaInv-Net can be designed with much less trainable parameters while still preserves its superior image reconstruction performance than some state-of-the-art deep models in CT imaging. In simulated and real data experiments, MetaInv-Net performs very well and can be generalized beyond the training setting, i.e., to other scanning settings, noise levels, and data sets.