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Complementing canonical fMRI with functional Quantitative Susceptibility Mapping (fQSM) in modern neuroimaging research.

, , , , , , and . NeuroImage, (2021)

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Non-invasive assessment of Neuromuscular Disorders by 7 tesla Magnetic Resonance Imaging and Spectroscopy: Dedicated radio-frequency coil development., , , , , , , , , and 3 other author(s). MeMeA, page 68-73. IEEE, (2015)Complementing canonical fMRI with functional Quantitative Susceptibility Mapping (fQSM) in modern neuroimaging research., , , , , , and . NeuroImage, (2021)Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping., , , , , , , , , and 5 other author(s). MIDL, volume 143 of Proceedings of Machine Learning Research, page 618-632. PMLR, (2021)Investigating Time-Varying Brain Connectivity with Functional Magnetic Resonance Imaging using Sequential Monte Carlo., , , , , and . EUSIPCO, page 1-5. IEEE, (2019)Astrophysical Source Separation Using Particle Filters., , and . ICA, volume 3195 of Lecture Notes in Computer Science, page 930-937. Springer, (2004)A Semiautomatic Image Processing Tool to Measure Small Structures in Magnetic Resonance Images of the Brain at 7 Tesla - Application to Hippocampus Subfields of Patients with Mild Cognitive Impairment., , , , , , , , and . BIOIMAGING, page 124-128. SciTePress, (2016)Echo-time dependency of quantitative susceptibility mapping reproducibility at different magnetic field strengths., , , , , and . NeuroImage, (2019)Modeling brain connectivity dynamics in functional magnetic resonance imaging via particle filtering., , , , , and . Brain Informatics, 8 (1): 19 (2021)Image separation using particle filters., and . Digit. Signal Process., 17 (5): 935-946 (2007)Evidence of a direct influence between the thalamus and hMT + independent of V1 in the human brain as measured by fMRI., , , , and . NeuroImage, 60 (2): 1440-1447 (2012)