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<p>Automated and reliable image quality assessment (IQA) is essential for safe use of medical image synthesis in critical applications like adaptive radiotherapy, treatment planning, or missing-modality reconstruction, where unnoticed generative artifacts may adversely affect outcomes. We evaluated image-to-image translation quality by coupling large-scale visual quality assessment with explainable automated IQA modeling. Adversarial diffusion-based framework, SynDiff, was applied to four cross-modality synthesis tasks, including three inter-MR and a CBCT-to-CT translation. Using four-fold cross-validation, ten reference-based and eight no-reference IQA metrics were computed for all synthesized images. Visual IQA ratings were independently collected from thirteen raters using predetermined protocol and specialized image viewer enabling blinded, randomized six-point Likert scoring. Auto-Sklearn was employed to learn ensemble regression models mapping IQA metrics to visual consensus ratings, with separate models trained on reference-based and no-reference metrics. The models closely reproduced distribution and ordering of ratings, typically within ±0.5 Likert points. Reference-based models achieved higher agreement with visual ratings than no-reference models (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(R^2\)</EquationSource></InlineEquation> 0.75 vs. 0.59, resp.), although the latter remained unbiased and informative. Explainability analyses indicated that metrics quantifying structural similarity or fidelity (e.g., anatomical boundary preservation) and intensity-based contrast relationships between tissues were the strongest predictors. Overall, the results demonstrate that ensemble regression models can provide transparent, scalable, and clinically meaningful quality control for generative medical imaging.</p>

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Visual fidelity–driven quality assessment of medical image translation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月18日

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  • Žiga Bizjak,
  • Jan Žagar,
  • Žiga Špiclin

摘要

Automated and reliable image quality assessment (IQA) is essential for safe use of medical image synthesis in critical applications like adaptive radiotherapy, treatment planning, or missing-modality reconstruction, where unnoticed generative artifacts may adversely affect outcomes. We evaluated image-to-image translation quality by coupling large-scale visual quality assessment with explainable automated IQA modeling. Adversarial diffusion-based framework, SynDiff, was applied to four cross-modality synthesis tasks, including three inter-MR and a CBCT-to-CT translation. Using four-fold cross-validation, ten reference-based and eight no-reference IQA metrics were computed for all synthesized images. Visual IQA ratings were independently collected from thirteen raters using predetermined protocol and specialized image viewer enabling blinded, randomized six-point Likert scoring. Auto-Sklearn was employed to learn ensemble regression models mapping IQA metrics to visual consensus ratings, with separate models trained on reference-based and no-reference metrics. The models closely reproduced distribution and ordering of ratings, typically within ±0.5 Likert points. Reference-based models achieved higher agreement with visual ratings than no-reference models (\(R^2\) 0.75 vs. 0.59, resp.), although the latter remained unbiased and informative. Explainability analyses indicated that metrics quantifying structural similarity or fidelity (e.g., anatomical boundary preservation) and intensity-based contrast relationships between tissues were the strongest predictors. Overall, the results demonstrate that ensemble regression models can provide transparent, scalable, and clinically meaningful quality control for generative medical imaging.