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

ABSTRACT:

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 p

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10476999 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature