.png){kind=icon}
.png){kind=icon}
Return
Tomographic Foundation Model—FORCE: Flow-Oriented Reconstruction Conditioning Engine
.png){kind=icon}
PRE
AI
DOI：10.1109/TMI.2026.3669463.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe noise and artifacts in reconstructed images, requiring improved reconstruction techniques. The introduction of deep learning has significantly advanced CT image reconstruction. However, obtaining paired training data remains rather challenging due to patient motion and other constraints. Although deep learning methods can still perform well with approximately paired data, they inherently carry the risk of hallucination due to data inconsistencies and model instability. In this paper, we integrate the data fidelity with the state-of-the-art generative AI model, referred to as the Poisson flow generative model (PFGM) with a generalized version PFGM++, and propose a novel CT framework: Flow-Oriented Reconstruction Conditioning Engine (FORCE). In our experiments, the proposed method shows superior performance in various CT imaging tasks, outperforming existing unsupervised reconstruction approaches.
Keywords:
CT reconstruction
poisson flow generative model
unsupervised learning
iterative reconstruction
Journal
IF:
9.8
Papers:
6.2K
Citations:
3.7W