GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization
GenDiff is a novel diffusion-based framework for low-dose CT reconstruction that jointly models continuous radiation dose and anatomical information. It integrates a Dose-Anatomy Encoder, cold diffusion backbone, physics-consistency update, and Structural Prior Refinement Module, outperforming existing methods on multi-anatomy clinical datasets with strong robustness and generalization.
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[Submitted on 11 Jul 2026]
Title:GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization
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Abstract:Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality. Existing deep learning-based low-dose CT (LDCT) reconstruction methods are typically optimized for fixed dose levels or specific anatomical regions, limiting their robustness and generalization in realistic clinical settings. We propose GenDiff, a generalizable diffusion-based framework for LDCT reconstruction that jointly models continuous radiation dose and anatomical information within a unified reconstruction network. The proposed framework integrates a Dose-Anatomy Encoder to learn acquisition-aware embeddings, a dose- and anatomy-conditioned cold diffusion backbone for iterative refinement, a physics-consistency update to enforce fidelity to the CT forward model, and a Structural Prior Refinement Module (SPRM) that preserves anatomical structures while suppressing dose-dependent artifacts. Extensive experiments on multi-anatomy clinical datasets, including unseen ultra-low-dose conditions as well as out-of-distribution phantom and animal datasets, demonstrate that GenDiff consistently outperforms state-of-the-art convolutional neural network and diffusion-based reconstruction methods. The proposed approach achieves superior reconstruction quality while maintaining strong robustness across different dose levels, anatomical regions, and acquisition domains, making it a promising solution for practical low-dose CT imaging.
Comments: 20 pages, 8 figures, 4 tables. Under review at PeerJ Computer Science
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.11941 [cs.CV]
(or arXiv:2607.11941v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.11941
arXiv-issued DOI via DataCite
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From: Md Imam Ahasan [view email] [v1] Sat, 11 Jul 2026 09:05:51 UTC (19,902 KB)
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