A deep dictionary network-based foundation model for ultra-low-dose CT denoising
Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality. Existing deep learning-based denoising methods are typically trained in an organ-specific fashion, resulting in limited generalization across heterogeneous multi?organ imaging scenarios. Foundation models present a promising all-in-one paradigm for unified multi-organ denoising. However, their architectures suffer from poor interpretability and rely on heuristic training strategies. To address these limitations, we propose an architecture?interpretable foundation model based on the deep dictionary network (DDN) for unified multi-organ ULDCT denoising....
| Authors | Baoshun Shi, Shuangyi Yang, Ke Jiang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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