To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with convolutional neural networks (CNNs) have been proposed for natural image denoising; however, these approaches might introduce image blurring or loss of original gradients. The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. An abdominal CT of 100 images obtained from a public database was adopted, and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. To evaluate the image quality, image similarities determined by the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were calculated for the denoised images. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. Moreover, the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10% from that of the original method. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.

中文翻译：

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基于深度卷积神经网络的低剂量计算机断层扫描图像去噪的定量评估

为了最大限度地降低辐射风险，减少剂量在计算机断层扫描 (CT) 的诊断和治疗应用中很重要。然而，由于 X 射线剂量的减少和可能不可接受的降低的诊断性能，图像噪声会降低图像质量。已经提出了使用卷积神经网络 (CNN) 的深度学习方法来进行自然图像去噪；然而，这些方法可能会导致图像模糊或原始梯度丢失。本研究的目的是比较基于 CNN 的低剂量 CT 降噪方法与其他降噪方法在独特 CT 噪声模拟图像上的剂量相关特性。为了模拟低剂量 CT 图像，将泊松噪声分布引入正常剂量图像，同时对 CT 单元特定的调制传递函数进行卷积。采用从公共数据库中获得的 100 张腹部 CT 图像，从原始剂量以 10 步剂量减少间隔创建模拟剂量减少图像，最终剂量为 1/100。这些图像使用 CNN (DnCNN) 的去噪网络结构作为通用 CNN 模型和用于迁移学习进行去噪。为了评估图像质量，计算了去噪图像的结构相似性指数（SSIM）和峰值信噪比（PSNR）确定的图像相似性。在 SSIM 和 PSNR 方面，DnCNN 比其他图像去噪方法实现了明显更好的去噪，尤其是在用于生成 10% 和 5% 剂量等效图像的​​超低剂量水平下。而且，开发的 CNN 模型可以在这些剂量水平下消除噪声并保持图像清晰度，并将 SSIM 比原始方法提高约 10%。相比之下，在小剂量减少条件下，该模型也会导致图像过度平滑。在定量评估中，CNN去噪方法通过剪裁CNN模型改进了低剂量CT并防止了过度平滑。