Self‐adaption and texture generation: A hybrid loss function for low‐dose CT denoising

作者：Zhenchuan Wang, Minghui Liu, Minghui Liu, Xuan Cheng, Jinqi Zhu, Xiaomin Wang, Haigang Gong, Ming Liu, Ming Liu, Lifeng Xu · 发表于：Journal of Applied Clinical Medical Physics · 年份：2023 · DOI：10.1002/acm2.14113 · 被引用次数：9 · 研究领域：Medical Imaging Techniques and Applications、Advanced X-ray and CT Imaging、Cardiac Imaging and Diagnostics

BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: Our hybrid loss function improves the denoising performance of several mode...