Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine
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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.
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Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the nonlinear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. ⋯ The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.
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To obtain whole-brain high-resolution T2 maps in 2 minutes by combining simultaneous multislice excitation and low-power PINS (power independent of number of slices) refocusing pulses with undersampling and a model-based reconstruction. ⋯ The proposed method is a fast T2 mapping technique that enables whole-brain acquisitions at 0.7-mm in-plane resolution, 3-mm slice thickness, and low specific absorption rate in 2 minutes.
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To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization. ⋯ ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.