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Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging., , , , , , , , , and . NeuroImage, (2013)One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction., , , , , , , , , and 17 other author(s). CoRR, (2023)Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex., , , , , , , , , and . NeuroImage, (2018)fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG., , , , , , , , , and 3 other author(s). NeuroImage, (2013)Corrigendum to "fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG" Neuroimage 78 (2013) 372-384., , , , , , , , , and 3 other author(s). NeuroImage, (2014)Ultrafast inverse imaging techniques for fMRI., , , , , , , , and . NeuroImage, 62 (2): 699-705 (2012)Outlier Detection for Analog Tests Using Deep Learning Techniques., , , , , and . VTS, page 1-7. IEEE, (2023)Mitigate B1+ inhomogeneity by nonlinear gradients and RF shimming., , , , , and . EMBC, page 1085-1088. IEEE, (2013)Concurrent electrophysiological and hemodynamic measurements of evoked neural oscillations in human visual cortex using sparsely interleaved fast fMRI and EEG., , , , , , and . NeuroImage, (2020)Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals., , , , , , and . NeuroImage, (2015)