Eulig Elias, Ommer Björn, Kachelrieß Marc
Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
Med Phys. 2024 Dec;51(12):8776-8788. doi: 10.1002/mp.17379. Epub 2024 Sep 17.
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.
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.
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.
Our evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal at best.
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.
长期以来，人们一直在努力降低辐射剂量，从而降低计算机断层扫描（CT）采集过程中对患者的潜在辐射风险，同时又不会严重降低图像质量。为此，多年来人们采用了各种技术，包括迭代重建方法和降噪算法。
最近，基于深度学习的降噪方法越来越受欢迎，许多论文声称其在定量和定性方面的性能都在不断提高。然而，缺乏标准化的基准设置以及各研究之间实验设计的不一致性，阻碍了所报告结果的可验证性和可重复性。
在本研究中，我们提出了一种基准设置，以克服这些缺陷，并提高该领域实验结果的可重复性和可验证性。我们使用这种标准化设置对几种最先进的方法进行了全面且公平的评估。
我们的评估表明，大多数基于深度学习的方法在统计上表现出相似的性能，而且过去几年的改进充其量只是微不足道的。
本研究强调了对基于深度学习的新型低剂量CT图像去噪方法进行更严格、公平评估的必要性。我们的基准设置是朝着这个方向迈出的重要第一步，未来的研究人员可以使用它来评估他们的算法。
