Signal Modeling in Quantitative Susceptibility Mapping: Reconstruction and Applications
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Quantitative susceptibility mapping (QSM) is a contrast in magnetic resonance imaging (MRI) exploiting varying tissue content to visualize anatomy. Chapter 1 features an introductory discussion of the susceptibility signal model. While QSM has been widely investigated as a biomarker, like all quantitative imaging, it is sensitive to the presence of noise and artifacts. Artifact reduction measures are outlined in Chapters 2 and 3 using explicit signal modeling while remaining chapters focus on clinical applications enabled by QSM. The maximum principle of harmonic functions is applied in a novel whole brain filtering technique, and further field modeling considerations and image priors are discussed. While artifact reduction measures are useful beyond the brain (in ex vivo samples, joint brain and spine, and carotid QSM), neurodegenerative diseases such as Parkinson’s disease (PD) and multiple sclerosis (MS) are primary pathologies of interest. In Chapter 4, an outcome prediction model representing the first numerical estimation of motor outcomes using presurgical QSM for deep brain stimulation (DBS) in the treatment of PD is proposed. Barriers to clinical implementation are identified. The following chapters propose technical developments to address these barriers, including limited sample size, label noise, and efficient parameterization. Chapters 5, 6, and 7 discuss novel label denoising techniques, joint models, and emerging parameter-efficient strategies to mitigate such barriers and provide frameworks for robust clinical models using QSM.