HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography

Researchers developed HARU-Net, a novel AI architecture for denoising cone-beam computed tomography (CBCT) medical images that outperforms existing state-of-the-art methods while using less computational resources. The system addresses critical noise issues in low-dose dental and maxillofacial imaging by combining hybrid attention mechanisms with residual U-Net architecture.