Green MRI for neuroimaging: monitoring brain tumors using non-contrast sequences.
TL;DR
The clinical and environmental rationales for minimizing the use of GBCAs, particularly in the follow-up of extra-axial tumors are discussed, and how advanced sequences and deep learning provide viable, contrast-free alternatives for future radiological practice is explored.
Abstract
The emerging concept of "Green Radiology" aims to mitigate the environmental impact of medical imaging while maintaining high standards of patient care. Among various modalities, MRI is particularly resource-intensive. This review focuses on the clinical feasibility and significance of minimizing gadolinium-based contrast agent (GBCA) administration in neuroimaging, specifically for the longitudinal follow-up of brain tumors. Key points of this review are as follows: (1) Rationales for "Green" MRI: Beyond patient safety concerns such as adverse reactions and gadolinium retention in the brain and bone, GBCAs pose significant environmental risks due to their persistence in global water systems and potential eco-toxicity. (2) Feasibility in extra-axial tumors: For many extra-axial lesions, including meningiomas, schwannomas, and macro-PitNETs (≥ 10 mm), non-contrast MRI sequences-leveraging the inherent contrast of cerebrospinal fluid-provide sufficient diagnostic information for routine surveillance. (3) Strategies for intra-axial tumors: While GBCAs remain the gold standard for high-grade gliomas and metastases, a "non-contrast-first" strategy may be viable for tumors with indolent progression. In these cases, GBCAs can be reserved for instances where interval changes are first identified on unenhanced sequences. (4) Advanced contrast-free alternatives: Non-invasive techniques such as arterial spin labeling (ASL), chemical exchange saturation transfer (CEST) imaging, and advanced diffusion-weighted imaging (DWI) offer powerful metabolic and microstructural insights without the need for chemical agents. (5) Future perspectives: Deep learning-based technologies, including "virtual contrast" synthesis and dosage reduction algorithms, hold immense potential to harmonize diagnostic excellence with environmental sustainability. Transitioning toward "Green Neuroradiology" through the judicious use of GBCAs and the adoption of advanced non-contrast sequences is a practical and necessary step for sustainable radiological practice. This review highlights the concept of "Green MRI" for sustainable neuroimaging. We discuss the clinical and environmental rationales for minimizing the use of GBCAs, particularly in the follow-up of extra-axial tumors. Furthermore, we explore how advanced sequences and deep learning provide viable, contrast-free alternatives for future radiological practice.