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• W4312878909 abstract "Quantitative Susceptibility Mapping (QSM) is an emerging contrast mechanism that aims to quantify the magnetic susceptibility of biological tissues. It provides insight into tissue iron and myelin deposition, as well as microstructural organization. QSM entails the solution of an inverse problem that relates the gradient-echo (GRE) signal phase to the underlying susceptibility distribution. This linear system is ill-posed and ill-conditioned, thus its solution benefits from additional regularization that applies prior constraints on the susceptibility map. These priors can be imposed in k-space or in image space. K-space constraints often lead to closed-form solutions of the inverse problem and are cheap to compute. Image space regularizers can exploit sparsity with respect to a mathematical transform. As these techniques often involve the minimization of cost functions that do not lend themselves to closed-form solutions, they are iterative in nature and can be computationally expensive. In addition to detailing both classes of algorithms, this chapter touches upon pre-processing steps that convert the raw GRE phase into a “tissue field map” that serves as the input to QSM reconstruction. These pre-processing steps and the QSM reconstruction itself can also be combined within a single forward model that relates the tissue susceptibility to the raw phase signal. Such “single-step” algorithms help prevent error propagation across these multiple steps. We provide example implementations for these pre-processing steps, benchmark k-space, and image space algorithms, as well as single-step approaches, and conclude the chapter with future directions, including deep-learning reconstruction models." @default.
• W4312878909 created "2023-01-05" @default.
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• W4312878909 date "2022-01-01" @default.
• W4312878909 modified "2023-10-18" @default.
• W4312878909 title "Quantitative Susceptibility-Mapping Reconstruction" @default.
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• W4312878909 doi "https://doi.org/10.1016/b978-0-12-822726-8.00027-0" @default.
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