Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine
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To combine a 3D saturation-recovery-based myocardial T1 mapping (3D SASHA) sequence with a 2D image navigator with fat excitation (fat-iNAV) to allow 3D T1 maps with 100% respiratory scan efficiency and predictable scan time. ⋯ We demonstrate the feasibility to combine the 3D SASHA T1 mapping imaging sequence with a 2D fat-iNAV for respiratory motion compensation, allowing 100% respiratory scan efficiency and predictable scan time.
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Comparative Study
Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual-gas calibrated BOLD imaging.
To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2 ) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual-gas calibrated BOLD imaging (DGC-BOLD) in healthy subjects. ⋯ Hypercapnic challenge-assisted QSM-OEF is a feasible approach to quantify regional brain OEF and CMRO2 . Compared with DGC-BOLD, hypercapnia QSM-OEF results in smaller intersubject variability and requires only 1 gas challenge.
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To develop an automated adaptive preconditioner for QSM reconstruction with improved susceptibility quantification accuracy and increased image quality. ⋯ An automated adaptive preconditioner allows high-quality QSM from the total field in imaging various anatomies with dynamic susceptibility ranges.
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To demonstrate feasibility and performance of prospective motion and B0 shim correction for MRS in human brain at 7T. ⋯ Prospective motion and B0 shim correction is feasible at 7T and should help improve the robustness of MRS, particularly in motion-prone populations.
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To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. ⋯ The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.