Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
J Appl Clin Med Phys. 2023 Sep;24(9):e14113. doi: 10.1002/acm2.14113. Epub 2023 Aug 11.
Deep learning has been successfully applied to low-dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per-pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image.
In this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high-quality diagnostic value using LDCT images, providing strong support for condition diagnosis.
We propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high-frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high-frequency information of the image, we use HFLoss to calculate the difference between CT images in the high-frequency information part.
Our hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details.
We propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high-quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.
深度学习已成功应用于低剂量 CT（LDCT）去噪。但模型的训练非常依赖于适当的损失函数。现有的去噪模型通常使用逐像素损失，包括平均绝对误差（MAE）和均方误差（MSE）。这忽略了 CT 图像不同区域去噪难度的差异，导致生成图像中较大纹理信息的丢失。
在本文中，我们提出了一种新的混合损失函数，该函数适应 CT 图像不同区域的噪声，以平衡去噪难度并保留纹理细节，从而使用 LDCT 图像获得具有高质量诊断价值的 CT 图像，为病情诊断提供有力支持。
我们提出了一种由加权补丁损失（WPLoss）和高频信息损失（HFLoss）组成的混合损失函数。为了增强模型对难以去噪的局部区域的去噪能力，我们改进 MAE 得到 WPLoss。在将生成图像和目标图像划分为多个补丁后，根据每个补丁的损失比自适应地和动态地调整每个补丁的损失权重。此外，考虑到纹理细节包含在图像的高频信息中，我们使用 HFLoss 来计算高频信息部分中 CT 图像之间的差异。
我们的混合损失函数在实验中提高了几个模型的去噪性能，获得了更高的峰值信噪比（PSNR）和结构相似性指数（SSIM）。此外，通过对比较实验生成结果的直观检查，所提出的混合函数可以有效地抑制噪声并保留图像细节。
我们提出了一种用于 LDCT 图像去噪的混合损失函数，它具有良好的可解释性，可以提高现有模型的去噪性能。并且，使用不同数据集的多个模型的验证结果表明，它具有良好的泛化能力。通过使用此损失函数，可以获得具有低辐射的高质量 CT 图像，从而避免辐射带来的危害，并确保为患者进行疾病诊断。
