Similarity and quality metrics for MR image-to-image translation
Abstract Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower noise levels. To ensure patient safety, these methods should be validated by human readers, which requires a con...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87358-0 |
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| author | Melanie Dohmen Mark A. Klemens Ivo M. Baltruschat Tuan Truong Matthias Lenga |
| author_facet | Melanie Dohmen Mark A. Klemens Ivo M. Baltruschat Tuan Truong Matthias Lenga |
| author_sort | Melanie Dohmen |
| collection | DOAJ |
| description | Abstract Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower noise levels. To ensure patient safety, these methods should be validated by human readers, which requires a considerable amount of time and costs. Quantitative metrics can effectively complement such studies and provide reproducible and objective assessment of synthetic images. If a reference is available, the similarity of MR images is frequently evaluated by SSIM and PSNR metrics, even though these metrics are not or too sensitive regarding specific distortions. When reference images to compare with are not available, non-reference quality metrics can reliably detect specific distortions, such as blurriness. To provide an overview on distortion sensitivity, we quantitatively analyze 11 similarity (reference) and 12 quality (non-reference) metrics for assessing synthetic images. We additionally include a metric on a downstream segmentation task. We investigate the sensitivity regarding 11 kinds of distortions and typical MR artifacts, and analyze the influence of different normalization methods on each metric and distortion. Finally, we derive recommendations for effective usage of the analyzed similarity and quality metrics for evaluation of image-to-image translation models. |
| format | Article |
| id | doaj-art-8a81988bb63f43529ad1500492ff7000 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8a81988bb63f43529ad1500492ff70002025-02-02T12:21:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-87358-0Similarity and quality metrics for MR image-to-image translationMelanie Dohmen0Mark A. Klemens1Ivo M. Baltruschat2Tuan Truong3Matthias Lenga4Bayer AG, RadiologyBayer AG, RadiologyBayer AG, RadiologyBayer AG, RadiologyBayer AG, RadiologyAbstract Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower noise levels. To ensure patient safety, these methods should be validated by human readers, which requires a considerable amount of time and costs. Quantitative metrics can effectively complement such studies and provide reproducible and objective assessment of synthetic images. If a reference is available, the similarity of MR images is frequently evaluated by SSIM and PSNR metrics, even though these metrics are not or too sensitive regarding specific distortions. When reference images to compare with are not available, non-reference quality metrics can reliably detect specific distortions, such as blurriness. To provide an overview on distortion sensitivity, we quantitatively analyze 11 similarity (reference) and 12 quality (non-reference) metrics for assessing synthetic images. We additionally include a metric on a downstream segmentation task. We investigate the sensitivity regarding 11 kinds of distortions and typical MR artifacts, and analyze the influence of different normalization methods on each metric and distortion. Finally, we derive recommendations for effective usage of the analyzed similarity and quality metrics for evaluation of image-to-image translation models.https://doi.org/10.1038/s41598-025-87358-0MetricsImage synthesisMRISimilarityImage quality |
| spellingShingle | Melanie Dohmen Mark A. Klemens Ivo M. Baltruschat Tuan Truong Matthias Lenga Similarity and quality metrics for MR image-to-image translation Scientific Reports Metrics Image synthesis MRI Similarity Image quality |
| title | Similarity and quality metrics for MR image-to-image translation |
| title_full | Similarity and quality metrics for MR image-to-image translation |
| title_fullStr | Similarity and quality metrics for MR image-to-image translation |
| title_full_unstemmed | Similarity and quality metrics for MR image-to-image translation |
| title_short | Similarity and quality metrics for MR image-to-image translation |
| title_sort | similarity and quality metrics for mr image to image translation |
| topic | Metrics Image synthesis MRI Similarity Image quality |
| url | https://doi.org/10.1038/s41598-025-87358-0 |
| work_keys_str_mv | AT melaniedohmen similarityandqualitymetricsformrimagetoimagetranslation AT markaklemens similarityandqualitymetricsformrimagetoimagetranslation AT ivombaltruschat similarityandqualitymetricsformrimagetoimagetranslation AT tuantruong similarityandqualitymetricsformrimagetoimagetranslation AT matthiaslenga similarityandqualitymetricsformrimagetoimagetranslation |