Objectives Deep learning-based synthetic contrast imaging has been proposed as an alternative to iodinated and gadolinium-based contrast agents in CT and MRI. This scoping review aimed to provide a cross-modality overview of current evidence, including study characteristics and validation strategies. Materials and methods Following PRISMA-ScR guidelines, PubMed, Embase, Scopus, and Web of Science were searched from inception to September 2025. Eligible studies applied deep learning to synthesize contrast-enhanced CT/MRI from non-contrast or modified-contrast inputs and reported reference-based validation. Extracted variables included modality, anatomy, input type, model type, dataset scale, validation category, and four predefined evaluation metrics. Results Fifty-six studies met the inclusion criteria (25 CT, 31 MRI). The brain was the most frequent target, followed by head and neck, breast, and liver applications. Non-contrast inputs were used in 71% of studies, with the remainder using modified-contrast strategies. Generative adversarial networks were the predominant model class, while diffusion and transformer models appeared after 2023. Dataset sizes ranged from 10 to 7306 (median, 218), and 57% of studies were single-center. Quantitative fidelity was evaluated in 88% of studies, reporting structural similarity index values of 0.73–0.99 and peak signal-to-noise ratios of 22–51 dB. Task-based performance was assessed in 39% of studies, radiologist-rated image quality in 54%, and diagnostic performance in 30%, with sensitivities of 72–92% and specificities of 59–95%. Conclusion Deep learning-based synthetic contrast imaging shows high quantitative and perceptual fidelity, but evidence supporting diagnostic interchangeability and routine clinical use remains limited. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Reducing contrast media use in CT and MRI is a clinical priority, and deep learning-based synthetic imaging is emerging as a potential alternative. <Emphasis Type="BoldItalic">Findings</Emphasis> Deep learning generated synthetic contrast-enhanced images with high quantitative fidelity and acceptable image quality, but diagnostic performance was evaluated in only limited studies. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> This review outlines the capabilities and limitations of deep learning-based synthetic contrast imaging, informing the development of contrast-minimizing strategies for safer CT and MRI practice. Graphical Abstract

Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review

期刊论文
发表日期：

2026年4月23日

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Gyu-Dong Jo,
Yoon Joo Choi,
Chena Lee,
Kug Jin Jeon,
Sang-Sun Han

摘要

Objectives

Deep learning-based synthetic contrast imaging has been proposed as an alternative to iodinated and gadolinium-based contrast agents in CT and MRI. This scoping review aimed to provide a cross-modality overview of current evidence, including study characteristics and validation strategies.

Materials and methods

Following PRISMA-ScR guidelines, PubMed, Embase, Scopus, and Web of Science were searched from inception to September 2025. Eligible studies applied deep learning to synthesize contrast-enhanced CT/MRI from non-contrast or modified-contrast inputs and reported reference-based validation. Extracted variables included modality, anatomy, input type, model type, dataset scale, validation category, and four predefined evaluation metrics.

Results

Fifty-six studies met the inclusion criteria (25 CT, 31 MRI). The brain was the most frequent target, followed by head and neck, breast, and liver applications. Non-contrast inputs were used in 71% of studies, with the remainder using modified-contrast strategies. Generative adversarial networks were the predominant model class, while diffusion and transformer models appeared after 2023. Dataset sizes ranged from 10 to 7306 (median, 218), and 57% of studies were single-center. Quantitative fidelity was evaluated in 88% of studies, reporting structural similarity index values of 0.73–0.99 and peak signal-to-noise ratios of 22–51 dB. Task-based performance was assessed in 39% of studies, radiologist-rated image quality in 54%, and diagnostic performance in 30%, with sensitivities of 72–92% and specificities of 59–95%.

Conclusion

Deep learning-based synthetic contrast imaging shows high quantitative and perceptual fidelity, but evidence supporting diagnostic interchangeability and routine clinical use remains limited.

Key Points

Question Reducing contrast media use in CT and MRI is a clinical priority, and deep learning-based synthetic imaging is emerging as a potential alternative.

Findings Deep learning generated synthetic contrast-enhanced images with high quantitative fidelity and acceptable image quality, but diagnostic performance was evaluated in only limited studies.

Clinical relevance This review outlines the capabilities and limitations of deep learning-based synthetic contrast imaging, informing the development of contrast-minimizing strategies for safer CT and MRI practice.

Graphical Abstract