# Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

arXiv: https://arxiv.org/abs/1801.01452 · HTML: https://mlsanity.com/paper/1801.01452/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

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

- [DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction](https://mlsanity.com/paper/2404.17890/) (2024)
- [Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction](https://mlsanity.com/paper/2111.14831/) (2021)
- [Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT](https://mlsanity.com/paper/2310.06949/) (2023)
- [Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference](https://mlsanity.com/paper/2210.01125/) (2022)
- [Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems](https://mlsanity.com/paper/2308.08511/) (2023)
- [Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation](https://mlsanity.com/paper/2102.12056/) (2021)
- [Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine](https://mlsanity.com/paper/2506.02149/) (2025)
- [Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction](https://mlsanity.com/paper/2203.05968/) (2022)
- [Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction](https://mlsanity.com/paper/2404.08748/) (2024)

## Abstract (arXiv)

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the L0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.