Quantitative Susceptibility Mapping: From Theory to Application for Static and Dynamic Imaging

Abstract

Quantitative Susceptibility Mapping (QSM) is a magnetic resonance imaging (MRI) post-processing technique that uses the image phase to map the spatial distribution of magnetic susceptibility in tissues, which arises from biological sources such as iron, myelin, and calcium. QSM is a broad research field due to the complexity of computing magnetic susceptibility. As such, QSM research has focused on refining the processing algorithms, leading to the development of numerous approaches with different advantages. These approaches may incorporate assumptions about the susceptibility distribution, such as spatial smoothness, but most do not consider information from other image contrasts, which may better delineate the edges between different tissue types. Additionally, QSM has been widely applied to measure iron deposition in the brain and stroke induced microbleeds and hemorrhages, among other applications. Although typically used to obtain static information about the brain, recent applications have expanded QSM to dynamically measure susceptibility changes due to the hemodynamic response of blood to neuronal activation, demonstrating the potential for QSM to measure acute susceptibility dynamics. To holistically address QSM for the brain, a multifaceted research approach was employed, which included the following: 1) implementation of a susceptibility mapping algorithm that incorporates prior knowledge of the brain’s magnetic susceptibility distribution based on multi-contrast MRI to inform the mapping process, 2) investigation of the impact of different QSM algorithms in a large-scale, multi-site study, and 3) extension of QSM from static to dynamic applications by developing a novel processing framework to analyze susceptibility changes within the brain during various physiological processes, such as normal breathing versus breath-holding and the cardiac cycle. Through this work, we demonstrate the broad applicability of QSM, from theoretical development to algorithm implementation, as well as practical applications – contributing to a deeper understanding of its capabilities and potential for future research.