Jul 2026· Japanese Journal of Radiology· 0 citations· 35 references
Medicine
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.
The recent developments in ultra-low-field brain MRI are reviewed, which enable imaging in open environments and demonstrate initial clinical applicability in point-of-care settings, and future developments are envisioned to address the current limitations of image quality and contrast in ultra-low-field brain MRI systems.
Ed X. Wu, Yujiao Zhao, Yilong Liu et al.· Stroke· 1 citation
Abstract. Brain tumors remain a major cause of morbidity and mortality worldwide. High-field MRI is considered the gold standard for neuro-oncological diagnostics; however, its availability is limited in many regions. This study aimed to evaluate the diagnostic capabilities of a low-field 0.2 T MRI system in detecting and characterizing intracranial tumors and to compare the findings with CT data. A retrospective analysis of 14,570 brain examinations performed between 2000 and 2009 identified 678 space-occupying lesions (4.7%). Gliomas, metastases, and meningiomas were the most common tumors. Low-field MRI demonstrated high sensitivity in detecting tumor heterogeneity, cystic degeneration, necrosis, hemorrhage, and contrast enhancement patterns, which were critical for tumor grading. Dynamic contrast-enhanced MRI improved differentiation between benign and malignant tumors. The results indicate that low-field MRI is a valuable, cost-effective tool for neuro-oncological diagnostics, particularly in resource-limited settings.
Perfusion MRI plays an important role in brain tumor assessment, especially for tumor grading and differentiation of tumor progression from pseudoprogression. Arterial spin labeling (ASL) MRI is a non-invasive method for measuring cerebral blood flow (CBF) using blood water as an endogenous tracer, which has great clinical potential for brain tumor imaging and might help to lower the use of gadolinium-based contrast agents. In this review, we discuss recent advances of the ASL method with a special focus on brain tumors, including multi-time point ASL which not only allows more accurate CBF quantification, but also yields arterial transit time (ATT) maps. Another method to reduce dependency on ATT is velocity-selective ASL, for which labeling is directly performed in the imaging volume. By performing a physiological challenge, like a hypercapnia breathing challenge, the reactivity of the tumor's neovasculature can be determined. More recent advances in ASL allow probing the blood-brain barrier (BBB) integrity by measuring the rate of water transport across the BBB (BBB-ASL). By labeling only a single artery, selective ASL sequences can help to identify what part of the tumor is fed by which artery. In conclusion, we see the future potential of ASL MRI not only in replacing contrast agents for perfusion assessment but also in providing additional information to the MRI exam, such as BBB integrity or the architecture of the vascular feeders.
Gabriel Hoffmann, M. V. van Osch· MAGMA· 0 citations
Gadolinium-based contrast agents (GBCAs) are essential for the evaluation of central nervous system (CNS) disorders, enhancing lesion detection, characterization, and therapeutic monitoring. Despite their widespread use and overall safety, their application in daily practice may be, in some cases, suboptimal due to technical or interpretative issues, including incorrect dosing, inadequate timing of acquisition, and sequence parameter adjustments. Although multiple GBCAs demonstrate comparable diagnostic performance, safety concerns such as hypersensitivity reactions, nephrogenic systemic fibrosis, and gadolinium retention have led to increasing emphasis on optimized and justified use. Regulatory differences between agencies, alongside growing preference for macrocyclic agents, reflect efforts to balance diagnostic benefit and safety. From a physicochemical standpoint, relaxivity, molecular structure, and pharmacokinetics critically influence imaging performance, particularly through T1 signal enhancement. Recent advances support dose reduction strategies, especially with high-relaxivity agents, without compromising diagnostic accuracy. Technical factors, including magnetic field strength, acquisition timing, and sequence selection, further modulate enhancement quality. Beyond conventional imaging, advanced techniques such as perfusion MRI (Dynamic Susceptibility Contrast and Dynamic Contrast Enhanced), Dixon imaging, black-blood sequences, and susceptibility-weighted imaging provide complementary physiological and structural information, enhancing diagnostic precision. These approaches enable more efficient use of GBCAs and increase diagnostic accuracy. Moreover, emerging artificial intelligence applications may further transform contrast utilization by enabling high-quality imaging at lower doses. In conclusion, a comprehensive, technically optimized, and physiologically informed approach to GBCA use is crucial to maximizing diagnostic yield while minimizing potential risks in CNS MRI.
T. Martín-Noguerol, M. V. Feldman-Garay, Sara Cánovas-Delgado et al.· Magnetic Resonance Imaging· 0 citations
Purpose: To develop a method for statistically significant tumor delineation. Glioblastoma (GBM) remains resistant to current therapeutic strategies and is unequivocally associated with a dismal prognosis. Hyperpolarized 13C MRI (hpMRI) provides unique insights into tissue metabolism, enabling outlining of tumor boundaries. Statistical confidence of this outline may help guide surgical resection, inform therapeutic decisions, and evaluate treatment efficacy. Materials and Methods: The new method was applied to time-resolved hyperpolarized [1-13C]pyruvate data from a previous study in a rat glioma model as well as from a new clinical acquisition from a patient with brain cancer. MATLAB R2021a was used for the comprehensive extraction and analysis of metabolite profiles. Time-points corresponding to high pyruvate and lactate signal intensities were combined, smoothed using a two-dimensional (2D) moving average, and analyzed with a sliding window to identify regions statistically distinct from manually segmented normal tissue. The negative predictive value (NPV) and the positive predictive value (PPV) of the detection method were evaluated by comparison with cancer regions delineated using standard radiological criteria. Results: The new approach enabled glioblastoma delineation with the NPV of 99% and the PPV of 59% in small animals. While in the larger brain of the clinical patient, with bicarbonate taken into account, NPV and PPV obtained 95% and 92% respectively. Conclusions: Combining time-points of high pyruvate and lactate signal intensities increases the statistical power of two-dimensional testing. This promising technique requires further evaluation in a larger patient cohort.
Abdallah Salemdawood, Dirk Mayer, A. Eldirdiri et al.· Magnetic Resonance in Medici...· 0 citations
ABSTRACT Hyperpolarized MRI is a medical imaging technology that has been used so far only in focused clinical studies. This technology enables metabolic imaging without ionizing radiation and is combined with standard‐of‐care MRI protocols. Unlike metabolic imaging performed with radiolabeled materials (e.g., PET and SPECT), hyperpolarized MRI enables differentiation between the injected metabolic substrate and its product(s). In this way, the number of imaging contrasts that may be attained is relatively large. The purpose of this review is two‐fold: to provide coherent terminology for the various contrasts obtained in HP‐MRI to facilitate further development in medicine and to highlight its potential contribution to liver nodule characterization. We reviewed 33 hyperpolarized MRI papers published from 2013 to 2024 that describe clinical studies in human subjects and patients, and characterized the metabolic contrast provided by this technology across various organs and in cancer tumors. We have further reviewed 20 preclinical studies of hyperpolarized MRI in the liver to evaluate the potential of this technology for early detection and characterization of hepatocellular carcinoma. Most clinical studies have used a single hyperpolarized substrate, namely, pyruvate, labeled with carbon‐13 at position 1 ([1‐13C]pyruvate). Even with this single substrate, the number of possible imaging contrasts is high, making the interpretation of these images challenging. We aimed to explain these imaging contrasts and provide a consistent nomenclature for them that will serve the medical community. Preclinical MRI studies using hyperpolarized substrates suggest the potential to characterize liver nodules in patients. In summary, hyperpolarized MRI is a promising clinical imaging modality in oncology; however, the small number of patients investigated so far precludes addressing possible biological confounding factors. Further studies are required for improving tumor characterization using this technology, and studies in liver imaging aimed at early detection of HCC are warranted.
N. Lev-Cohain, Jacob Sosna, R. Katz-Brull· NMR in Biomedicine· 0 citations
