Benchmarking deep learning‐based low‐dose CT image denoising algorithms
Medical physics, 2024•Wiley Online Library
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 …
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 …
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 overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state‐of‐the‐art methods using this standardized setup.
Results
Our evaluation reveals that most deep learning‐based methods show statistically similar performance, and improvements over the past years have been marginal at best.
Conclusions
This study highlights the need for a more rigorous and fair evaluation of novel deep learning‐based methods for low‐dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate their algorithms.