Optimizing gadolinium-enhanced CNS MRI: From physics to clinical practice.

Abstract

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.