# Systematic Review on Learning-based Spectral CT

arXiv: https://arxiv.org/abs/2304.07588 · HTML: https://mlsanity.com/paper/2304.07588/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2023.

## Built on by (most cited first, 7 of 7)

- [PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models](https://mlsanity.com/paper/2312.09754/) (2023)
- [Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review](https://mlsanity.com/paper/2402.04301/) (2024)
- [Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model](https://mlsanity.com/paper/2507.18012/) (2025)
- [SpectralCTGaussians: Projection-Domain Reconstruction and Basis Material Decomposition for Spectral CT using 3D Gaussian Splatting](https://mlsanity.com/paper/2609.29638/) (2026)
- [Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction](https://mlsanity.com/paper/2404.08748/) (2024)
- [Shared-Structure 4D Spectral Gaussian Representation for Sparse-View Spectral CT Reconstruction](https://mlsanity.com/paper/2608.16463/) (2026)
- [Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion](https://mlsanity.com/paper/2610.06094/) (2026)

## Abstract (arXiv)

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.