DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling
Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critical challenge. Low-dose CT (LDCT) protocols reduce radiation risks but inevitably suffer from severe noise and artifacts that compromise diagnostic accuracy. While existing deep learning methods have achieved promising results, there remains a continuous quest for generative paradigms that intrinsically capture global-to-local structural dependencies to better preserve fine anatomical details. To this end, we propose DeVAR, a novel generative framework that applies visual autoregressive modeling (VAR) to LDCT denoising for the first time. Con...
| Authors | Xizhuo Zhang, Yannian Gu, Zhongzhen Huang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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