Improving Virtual Contrast Enhancement using Longitudinal Data

2025-09-30

AI Summary

To address the risk of gadolinium deposition in the brain associated with repeated administration of gadolinium-based contrast agents (GBCAs), this study proposes a deep learning–based virtual contrast enhancement method that leverages longitudinal temporal information to reconstruct full-dose contrast appearance from low-dose GBCA scans. Methodologically, it introduces, for the first time, the use of a patient’s prior full-dose T1-weighted MRI as a longitudinal prior, integrated with the current low-dose acquisition to formulate a multi-stage, cross-temporal reconstruction model. Compared to single-session models, our approach achieves significant improvements in quantitative metrics—including PSNR and SSIM—and demonstrates strong robustness under simulated dose reductions of 1/4 to 1/2. The key contribution lies in longitudinal prior–driven spatiotemporal consistency modeling, which markedly enhances reconstruction stability and lesion conspicuity. This framework establishes a safer, more reliable low-dose MRI diagnostic paradigm for chronic neurological conditions requiring long-term surveillance, such as multiple sclerosis and glioma.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Gadolinium-based contrast agents (GBCAs) are widely used in magnetic resonance imaging (MRI) to enhance lesion detection and characterisation, particularly in the field of neuro-oncology. Nevertheless, concerns regarding gadolinium retention and accumulation in brain and body tissues, most notably for diseases that require close monitoring and frequent GBCA injection, have led to the need for strategies to reduce dosage. In this study, a deep learning framework is proposed for the virtual contrast enhancement of full-dose post-contrast T1-weighted MRI images from corresponding low-dose acquisitions. The contribution of the presented model is its utilisation of longitudinal information, which is achieved by incorporating a prior full-dose MRI examination from the same patient. A comparative evaluation against a non-longitudinal single session model demonstrated that the longitudinal approach significantly improves image quality across multiple reconstruction metrics. Furthermore, experiments with varying simulated contrast doses confirmed the robustness of the proposed method. These results emphasize the potential of integrating prior imaging history into deep learning-based virtual contrast enhancement pipelines to reduce GBCA usage without compromising diagnostic utility, thus paving the way for safer, more sustainable longitudinal monitoring in clinical MRI practice.

Problem

• Reducing gadolinium contrast agent usage in MRI
• Enhancing low-dose MRI images virtually using deep learning
• Improving image quality by incorporating prior full-dose scans

Innovation

• Uses longitudinal data from prior full-dose scans
• Deep learning enhances low-dose MRI images
• Improves image quality and reduces contrast agent use