ReviewJournal of imaging informatics in medicine2025
A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images.
Review in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
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Who cites it
14 citing papers in PubMed.
- OpenQPAIData: Measurements of test objects with reference labels for quantitative photoacoustic imaging research.Scientific data · 2026Article
- Nonstop gated cone-beam computed tomography for respiratory gating pancreatic cancer radiotherapy using dual-domain deep learning reconstruction.Physics and imaging in radiation oncology · 2026Article
- On optimisation of Paganin's method for propagation-based X-ray phase-contrast imaging and tomography.Journal of microscopy · 2026Article
- PhotIQA: A photoacoustic image data set with image quality ratings.Scientific data · 2026Article
- Deep learning-driven super-resolution for cone-beam computed tomography: AnImaging science in dentistry · 2026Article
- Assessing image quality in photoacoustic imaging: A metric-based and deep learning-based evaluation.Photoacoustics · 2026Article
- Generative deep learning for foundational video translation in ultrasound.Scientific reports · 2026Article
- Deep Learning Empowered Microstructure Codebook: New Paradigm for Multi-Parameter Tissue Characterization Estimation.Human brain mapping · 2026Article
- Learned denoising with simulated and experimental low-dose CT data.Scientific reports · 2025Article
- Agreement of image quality metrics with radiological evaluation in the presence of motion artifacts.Magma (New York, N.Y.) · 2025Article
- Multi-wavelength graph convolutional network for high-performance sparse multispectral optoacoustic tomography.Photoacoustics · 2025Article
- ROQUS: a retinal OCT quality and usability score.Biomedical optics express · 2025Article
- Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts.ArXiv · 2025Article
- Diagnostic accuracy of artificial intelligence in the diagnosis of pemphigus and pemphigoid groups of disorders based on clinical images: A systematic review and meta-analysis.Journal of oral biology and craniofacial researchReview
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on medical images. The popular full reference (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 reported in the literature, highlighting the gap between development and actual clinical application. Such inconsistencies are not surprising, as medical images have very different properties than natural images, and PSNR and SSIM have neither been targeted nor properly tested for medical images. This may cause unforeseen problems in clinical applications due to wrong judgement of novel methods. This paper provides a structured and comprehensive overview of examples where PSNR and SSIM 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. Therefore, improvement is urgently needed in particular in this era of AI to increase reliability and explainability in machine learning for medical imaging and beyond. Lastly, we will provide ideas for future research as well as suggest guidelines for the usage of FR-IQA measures applied to medical images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.