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Daniel Gallichan

Researcher at Cardiff University

Publications -  60
Citations -  1697

Daniel Gallichan is an academic researcher from Cardiff University. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 23, co-authored 60 publications receiving 1521 citations. Previous affiliations of Daniel Gallichan include École Polytechnique Fédérale de Lausanne & John Radcliffe Hospital.

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A calibration method for quantitative BOLD fMRI based on hyperoxia.

TL;DR: The hyperoxia technique is able to provide an estimate of the calibration constant that has lower overall intersubject and intersession variability compared to the hypercapnia approach.
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Retrospective Correction of Involuntary Microscopic Head Movement Using Highly Accelerated Fat Image Navigators (3D FatNavs) at 7T

TL;DR: A three‐dimensional gradient echo volume is used in combination with a fat‐selective excitation as a 3D motion navigator (3D FatNav) for retrospective correction of microscopic head motion during high‐resolution 3D structural scans of extended duration.
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Addressing a systematic vibration artifact in diffusion-weighted MRI.

TL;DR: A way is suggested to improve the interpretation of affected DTI data by including a co‐regressor which accounts for the empirical response of regions affected by the artifact, and that subsequent increases in TE can be avoided by employing parallel acceleration.
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Flow-metabolism coupling in human visual, motor, and supplementary motor areas assessed by magnetic resonance imaging.

TL;DR: The results support the existence of regional differences in neurovascular coupling, and argue for the importance of achieving optimal accuracy in hypercapnia calibrations to resolve method‐dependent variations in published results.
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Measuring the effects of remifentanil on cerebral blood flow and arterial arrival time using 3D GRASE MRI with pulsed arterial spin labelling

TL;DR: Significant differences between physiologic conditions were observed in both cerebral blood flow and arterial arrival time maps, indicating that 3D GRASE-ASL has the sensitivity to study changes in physiology at a voxel level.