NMR in biomedicine
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Measurement of the cerebral blood flow (CBF) with whole-brain coverage is challenging in terms of both acquisition and quantitative analysis. In order to fit arterial spin labeling-based perfusion kinetic curves, an empirical three-parameter model which characterizes the effective impulse response function (IRF) is introduced, which allows the determination of CBF, the arterial transit time (ATT) and T(1,eff). The accuracy and precision of the proposed model were compared with those of more complicated models with four or five parameters through Monte Carlo simulations. ⋯ Standard statistical analysis was conducted to compare these fitting models in various brain regions. The fitted results indicated that: (i) the estimated CBF values using the two-parameter model show appreciable dependence on the assumed T(1,eff) values; (ii) the proposed three-parameter model achieves the optimal balance between the goodness of fit and model complexity when compared among the models with explicit IRF fitting; (iii) both the two-parameter model using fixed blood T1 values for T(1,eff) and the three-parameter model provide reasonable fitting results. Using the proposed three-parameter model, the estimated CBF (46 ± 14 mL/100 g/min) and ATT (1.4 ± 0.3 s) values averaged from different brain regions are close to the literature reports; the estimated T(1,eff) values (1.9 ± 0.4 s) are higher than the tissue T1 values, possibly reflecting a contribution from the microvascular arterial blood compartment.
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Quantitative susceptibility mapping (QSM) is a recently developed MRI technique that provides a quantitative measure of tissue magnetic susceptibility. To compute tissue magnetic susceptibilities based on gradient echoes, QSM requires reliable unwrapping of the measured phase images and removal of contributions caused by background susceptibilities. Typically, the two steps are performed separately. ⋯ Both numerical simulations and in vivo human brain images show that HARPERELLA effectively removes both phase wraps and background phase, whilst preserving all low spatial frequency components originating from brain tissues. When compared with other QSM phase preprocessing techniques, such as path-based phase unwrapping followed by background phase removal, HARPERELLA preserves the tissue phase signal in gray matter, white matter and cerebrospinal fluid with excellent robustness, providing a convenient and accurate solution for QSM. The proposed algorithm is provided, together with QSM and susceptibility tensor imaging (STI) tools, in a shared software package named 'STI Suite'.