Multi-scale perceptual modulation network for low-dose computed tomography denoising.

ABSTRACT:

Background

Low-dose computed tomography (LDCT) reduces radiation exposure, but the introduced noise and artifacts impair its diagnostic accuracy. Convolutional neural networks (CNNs) are widely used for LDCT denoising, but they suffer from a limited receptive field. The use of a larger kernel size can enlarge the receptive field and boost model performance; however, the computational cost of the model greatly increases. We aimed to develop a LDCT denoising CNN with a large receptive field and lower computational complexity.

Methods

We developed a multi-scale perceptual modulation network (MSPMnet) incorporating a powerful multi-head decomposable convolution (MHDC). To address the high computational complexity of large kernel convolutions, we developed a novel MHDC module tha

SUBMITTER: Huang J 

PROVIDER: S-EPMC11652037 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature