Literature DB >> 20418038

Simulating the effects of time-varying magnetic fields with a realistic simulated scanner.

Ivana Drobnjak1, Gaby S Pell, Mark Jenkinson.   

Abstract

Transient magnetic fields induce changes in magnetic resonance (MR) images ranging from small, visually undetectable effects (caused, for instance, by neuronal currents) to more significant ones, such as those created by the gradient fields and eddy currents. Accurately simulating these effects may assist in correcting or optimising MR imaging for many applications (e.g., diffusion imaging, current density imaging, use of magnetic contrast agents, neuronal current imaging, etc.). Here we have extended an existing MR simulator (POSSUM) with a model for changing magnetic fields at a very high-resolution time-scale. This simulator captures a realistic range of scanner and physiological artifacts by modeling the scanner environment, pulse sequence details and subject properties (e.g., brain geometry and air-tissue boundaries). The simulations were validated by using previously published experimental data sets. In the first dataset a transient magnetic field was produced by a single conducting wire with varying current amplitude (between 17 muA and 765 muA). The second was identical except that current amplitude was fixed (at 7.8 mA) and current timing varied. A very close match between simulated images and experimental data was observed. In addition, these validation results led to the observation that the current-induced effects included ringing in the image, which extended away from the conductor, primarily in the phase-encode direction. This effect had previously not been noticed in the noisy, experimentally-acquired images, demonstrating one way in which simulated images can provide potential insight into imaging experiments.

Mesh:

Year:  2010        PMID: 20418038     DOI: 10.1016/j.mri.2010.03.029

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  11 in total

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2.  Magnetic resonance imaging of ionic currents in solution: the effect of magnetohydrodynamic flow.

Authors:  Mukund Balasubramanian; Robert V Mulkern; William M Wells; Padmavathi Sundaram; Darren B Orbach
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3.  Double diffusion encoding MRI for the clinic.

Authors:  Grant Yang; Qiyuan Tian; Christoph Leuze; Max Wintermark; Jennifer A McNab
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4.  Prospective active marker motion correction improves statistical power in BOLD fMRI.

Authors:  Jordan Muraskin; Melvyn B Ooi; Robin I Goldman; Sascha Krueger; William J Thomas; Paul Sajda; Truman R Brown
Journal:  Neuroimage       Date:  2012-12-05       Impact factor: 6.556

5.  Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement.

Authors:  Jesper L R Andersson; Mark S Graham; Ivana Drobnjak; Hui Zhang; Nicola Filippini; Matteo Bastiani
Journal:  Neuroimage       Date:  2017-03-08       Impact factor: 6.556

6.  Simulation of phase contrast angiography for renal arterial models.

Authors:  Artur Klepaczko; Piotr Szczypiński; Michał Strzelecki; Ludomir Stefańczyk
Journal:  Biomed Eng Online       Date:  2018-04-16       Impact factor: 2.819

7.  Detection of aberrant hippocampal mossy fiber connections: Ex vivo mesoscale diffusion MRI and microtractography with histological validation in a patient with uncontrolled temporal lobe epilepsy.

Authors:  Michel Modo; T Kevin Hitchens; Jessie R Liu; R Mark Richardson
Journal:  Hum Brain Mapp       Date:  2015-11-27       Impact factor: 5.038

8.  D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.

Authors:  Daniele Perrone; Ben Jeurissen; Jan Aelterman; Timo Roine; Jan Sijbers; Aleksandra Pizurica; Alexander Leemans; Wilfried Philips
Journal:  PLoS One       Date:  2016-03-01       Impact factor: 3.240

9.  Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI.

Authors:  Mark S Graham; Ivana Drobnjak; Mark Jenkinson; Hui Zhang
Journal:  PLoS One       Date:  2017-10-02       Impact factor: 3.240

10.  Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines.

Authors:  Seyyed M H Haddad; Christopher J M Scott; Miracle Ozzoude; Melissa F Holmes; Stephen R Arnott; Nuwan D Nanayakkara; Joel Ramirez; Sandra E Black; Dar Dowlatshahi; Stephen C Strother; Richard H Swartz; Sean Symons; Manuel Montero-Odasso; Robert Bartha
Journal:  PLoS One       Date:  2019-12-20       Impact factor: 3.240

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