Public articles linked to the same research event.
Biomedical physics & engineering express This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.
This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.