Advances in neuroimaging applications of quantitative susceptibility mapping

This review article delves into the advancements of quantitative susceptibility mapping (QSM) in neuroimaging, highlighting its utility in detecting and quantifying magnetic susceptibility differences in tissues, particularly for paramagnetic substances like iron and diamagnetic substances such as calcifications in the brain. QSM has revolutionized the diagnosis and monitoring of neurodegenerative diseases by enabling the precise measurement of brain iron deposition and blood oxygen saturation. The review is partitioned into three sections. The first section underscores QSM's role in clinical applications related to microhemorrhages, cerebral amyloidosis, intracranial hematomas, and cerebrovascular malformations. The second section focuses on QSM's application in mapping iron content in neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. The final section discusses QSM's potential in assessing stroke by measuring oxygen saturation. The article also outlines the basic theory and development of QSM, emphasizing the importance of echo time selection for accurate QSM results. Challenges in clinical applications and future directions, including the integration of AI technology for image reconstruction and data analysis, are also discussed. QSM's ability to differentiate between microbleeds and calcifications, assess dynamic susceptibility changes in intracranial hematomas, and guide thrombolytic strategies in acute cerebrovascular disease is highlighted. The review concludes by emphasizing the need for further optimization of QSM algorithms and the expansion of its applications in biomedical imaging. • An overview of advances in the use of QSM in clinical ICH, CA, intracranial hematomas, cerebral vascular malformations, and thrombosis and recanalization from a novel perspective. • Recent advances in the mapping of QSM to iron levels in PD, AD, and other NBIA are described. • The future development of QSM technology is presented, where QSM applications are increasingly being combined with AI technologies.