HARU-Net: hybrid attention residual U-Net for edge-preserving denoising in cone-beam computed tomography
Year: 2026
Type: article
Abstract: Objectives. Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging, but low-dose acquisition introduces strong, spatially varying noise that degrades soft-tissue visibility and obscures fine anatomical structures. Classical denoising methods struggle to suppress noise in CBCT while preserving edges. Although deep learning (DL)-based approaches offer high-fidelity restoration, their use in CBCT denoising is limited by the scarcity of high-resolution CBCT data for supervised training. This study aims to develop an efficient DL framework for high-quality CBCT denoising that effectively suppresses noise while preserving fine anatomical structures and maintaining computational efficiency for practical clinical deployment. Approach. To achieve this objective, we propose a novel Hybrid Attention Residual U-Net (HARU-Net) for high-quality denoising of CBCT data, trained on a cadaver dataset of human hemimandibles acquired using a high-resolution protocol of the 3D Accuitomo 170 (J. Morita, Kyoto, Japan) CBCT system. The novel contribution of this approach is the integration of three complementary architectural components: (i) a hybrid attention transformer block embedded within each skip connection to selectively emphasize salient anatomical features, (ii) a residual hybrid attention transformer group at the bottleneck to strengthen global contextual modeling and long-range feature interactions, and (iii) residual learning convolutional blocks to facilitate deeper, more stable feature extraction throughout the network. Results. HARU-Net 1 1 https://github.com/DrKay87/HARU-Net–Hybrid-Attention-Residual-U-Net-for-Denoising-in-Cone-Beam-Computed-Tomography . consistently outperforms state-of-the-art methods achieving the highest peak signal-to-noise ratio (37.52 dB), the second-highest SSIM (0.9557), and the lowest GMSD (0.1084). Compared with transformer-based methods, the proposed network achieves superior denoising performance while maintaining substantially lower computational complexity. Conclusion. The proposed HARU-Net provides an effective balance between noise suppression, anatomical structure preservation, and computational efficiency. These characteristics make it a promising and practical solution for improving image quality and supporting reliable diagnosis in low-dose CBCT imaging.
Authors: Khuram Naveed, Ruben Pauwels
Institution: Aarhus University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,400
OpenAlex ID: W7131907166
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