ArticleScientific reports2025
Similarity and quality metrics for MR image-to-image translation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Evaluation of generative adversarial network-based postprocessing super-resolution for lumbar spine magnetic resonance imaging.Physical and engineering sciences in medicine · 2026Article
- Deep learning-driven super-resolution for cone-beam computed tomography: AnImaging science in dentistry · 2026Article
- Quantification of edge-enhancing effects using accelerated deep learning reconstructed orbital MRI sequences.European journal of radiology open · 2026Article
- Deep learning-based thermal mapping for enhanced urban heat management and cooling energy reduction in Arid Saudi Arabian environments.Scientific reports · 2026Article
- Generative deep learning for foundational video translation in ultrasound.Scientific reports · 2026Article
- Clinical Potential of Artificial Bone Scintigraphy from Early-Phase Bone Scintigraphy Using Unpaired Image-to-Image Translation in Patients with Breast Cancer: A Single-Center Prospective Study.Tomography (Ann Arbor, Mich.) · 2026Article
- Decrypting chaotic visual ciphers via quasi quantum neural networks (Q²NNs).Scientific reports · 2026Article
- Potential of Image2Image translation in reducing AI bias attributed to differences in CT reconstruction methods: proof-of-concept study on a paired dataset.Frontiers in radiology · 2026Article
- PSO-based parameter optimization of intuitionistic fuzzy generator for low-light image enhancement.Frontiers in artificial intelligence · 2026Article
- Probabilistic brain MR image transformation using generative models.Scientific reports · 2025Article
- Intraoperative 3D reconstruction from sparse arbitrarily posed real X-rays.Scientific reports · 2025Article
- Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy.Scientific reports · 2025Article
- Implementation of a Conditional Latent Diffusion-Based Generative Model to Synthetically Create Unlabeled Histopathological Images.Bioengineering (Basel, Switzerland) · 2025Article
- Prostate MRI Using Deep Learning Reconstruction in Response to Cancer Screening Demands-A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps.The Journal of international medical research · 2025Article
- RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using autoencoders.Imaging neuroscience (Cambridge, Mass.)Article
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5 authors.
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No grant is acknowledged in the PubMed record.
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.
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