HARU-Net 用混合注意力机制提升低剂量CBCT图像去噪，保留细节且速度快。
HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
- 融合混合注意力块与残差结构，增强特征提取与边缘保持能力
- 在37.52 dB PSNR、0.9557 SSIM下实现最佳去噪效果
- 计算成本更低，适合临床部署，提升低剂量CBCT诊断质量
锥形束计算机断层扫描（CBCT）广泛用于牙科与颌面影像，但低剂量采集引入强而空间变化的噪声，降低软组织可见性并模糊细微解剖结构。传统去噪方法难以在抑制噪声的同时保留边缘。尽管深度学习方法可实现高保真重建，但在CBCT去噪中的应用受限于高质量标注数据稀缺。为此，本文提出新型混合注意力残差U-Net（HARU-Net），基于3D Accuitomo 170系统采集的人类半下颌标本数据集训练。其创新点包括：(i) 在跳跃连接中嵌入混合注意力变压器块（HAB），选择性强调关键解剖特征；(ii) 在瓶颈层引入残差混合注意力组（RHAG），强化全局上下文建模与长程特征交互；(iii) 采用残差卷积块，促进深层稳定特征提取。HARU-Net显著优于SOTA方法（如SwinIR和Uformer），达到最高PSNR（37.52 dB）、最高SSIM（0.9557）和最低GMSD（0.1084）。该方法在计算开销远低于SOTA的前提下，实现高效可靠的去噪，为低剂量CBCT图像质量提升提供了实用方案。
原文摘要 · Abstract (English)
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.
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