Image quality assessment (IQA) is not just indispensable in clinical practice
to ensure high standards, but also in the development stage of novel algorithms
that operate on medical images with reference data. This paper provides a
structured and comprehensive collection of examples where the two most common
full reference (FR) image quality measures prove to be unsuitable for the
assessment of novel algorithms using different kinds of medical images,
including real-world MRI, CT, OCT, X-Ray, digital pathology and photoacoustic
imaging data. In particular, the FR-IQA measures PSNR and SSIM are known and
tested for working successfully in many natural imaging tasks, but
discrepancies in medical scenarios have been noted in the literature.
Inconsistencies arising in medical images are not surprising, as they have very
different properties than natural images which have not been targeted nor
tested in the development of the mentioned measures, and therefore might imply
wrong judgement of novel methods for medical images. Therefore, improvement is
urgently needed in particular in this era of AI to increase explainability,
reproducibility and generalizability in machine learning for medical imaging
and beyond. On top of the pitfalls we will provide ideas for future research as
well as suggesting guidelines for the usage of FR-IQA measures applied to
medical images.

图像质量评估 (IQA) 不仅在临床实践中确保高标准的不可或缺，而且在基于医学图像的新算法开发阶段也是必须的。本文提供了一个有结构且全面的示例集，其中两种最常见的全参考 (FR) 图像质量评估方法无法用于评估使用不同类型医学图像 (包括真实世界的 MRI、CT、OCT、X 射线、数字病理学和光声成像数据) 的新算法。需要紧急改进，以增加医学图像及其它领域机器学习的可解释性、可重复性和泛化性。除了这些障碍，我们还将提供未来研究的思路，并建议在医学图像中使用 FR-IQA 方法的指导方针。