# Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction

arXiv: https://arxiv.org/abs/2504.07753 · HTML: https://mlsanity.com/paper/2504.07753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 14 of 14)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction](https://mlsanity.com/paper/2211.13926/) (2022)
- [Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis](https://mlsanity.com/paper/1611.06391/) (2016)
- [Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2308.15942/) (2023)
- [Differentiable Forward Projector for X-ray Computed Tomography](https://mlsanity.com/paper/2307.05801/) (2023)
- [Tensor-Based Classifiers for Hyperspectral Data Analysis](https://mlsanity.com/paper/1709.08164/) (2017)
- [Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation](https://mlsanity.com/paper/2103.05255/) (2021)
- [Convergent Data-driven Regularizations for CT Reconstruction](https://mlsanity.com/paper/2212.07786/) (2022)
- [A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models](https://mlsanity.com/paper/2504.03821/) (2025)
- [RED: Residual Estimation Diffusion for Low-Dose PET Sinogram Reconstruction](https://mlsanity.com/paper/2411.05354/) (2024)
- [Sparse-View Spectral CT Reconstruction Using Deep Learning](https://mlsanity.com/paper/2011.14842/) (2020)
- [Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion](https://mlsanity.com/paper/2501.09935/) (2025)

## Built on by (most cited first, 1 of 1)

- [Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2602.07980/) (2026)

## Abstract (arXiv)

Sparse-view sampling in dual-energy computed tomography (DECT) significantly reduces radiation dose and increases imaging speed, yet is highly prone to artifacts. Although diffusion models have demonstrated potential in effectively handling incomplete data, most existing methods in this field focus on the image do-main and lack global constraints, which consequently leads to insufficient reconstruction quality. In this study, we propose a dual-domain virtual-mask in-formed diffusion model for sparse-view reconstruction by leveraging the high inter-channel correlation in DECT. Specifically, the study designs a virtual mask and applies it to the high-energy and low-energy data to perform perturbation operations, thus constructing high-dimensional tensors that serve as the prior information of the diffusion model. In addition, a dual-domain collaboration strategy is adopted to integrate the information of the randomly selected high-frequency components in the wavelet domain with the information in the projection domain, for the purpose of optimizing the global struc-tures and local details. Experimental results indicated that the present method exhibits excellent performance across multiple datasets.