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

2026-02-25

AI Summary

This work addresses the challenge of denoising low-dose cone-beam computed tomography (CBCT) images while preserving edges and fine anatomical structures, which is hindered by severe noise. To this end, the authors propose HARU-Net, a novel architecture integrating a hybrid attention Transformer, residual learning, and a U-Net backbone. Key innovations include hybrid attention Transformer blocks within skip connections, a residual hybrid attention Transformer group at the bottleneck, and residual convolutional blocks, collectively enabling effective modeling of both local details and global contextual information. Trained on a high-resolution mandibular CBCT dataset, HARU-Net outperforms existing methods in terms of PSNR (37.52 dB), SSIM (0.9557), and GMSD (0.1084), while exhibiting lower computational overhead, thereby significantly enhancing diagnostic image quality in low-dose CBCT.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Low Level & Physics-based Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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-based approaches offer high-fidelity restoration, their use in CBCT denoising is limited by the scarcity of high-resolution CBCT data for supervised training. To address this research gap, 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 (HAB) embedded within each skip connection to selectively emphasize salient anatomical features, (ii) a residual hybrid attention transformer group (RHAG) 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. HARU-Net consistently outperforms state-of-the-art (SOTA) methods including SwinIR and Uformer, achieving the highest PSNR (37.52 dB), highest SSIM (0.9557), and lowest GMSD (0.1084). This effective and clinically reliable CBCT denoising is achieved at a computational cost significantly lower than that of the SOTA methods, offering a practical advancement toward improving diagnostic quality in low-dose CBCT imaging.

Problem

• Cone-beam computed tomography
• low-dose imaging
• edge-preserving denoising
• spatially varying noise
• anatomical structure preservation

Innovation

• Hybrid Attention
• Residual U-Net
• CBCT Denoising
• Transformer Block
• Edge-Preserving