Benchmarking deep learning-based low-dose CT image denoising algorithms.

ABSTRACT:

Background

Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms.

Purpose

Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reported results.

Methods

In this study, we propose a benchmark setup to

SUBMITTER: Eulig E 

PROVIDER: S-EPMC11656299 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature