Nagy, Z; Weiskopf, N; Alexander, DC; Deichmann, R; (2007) A method for improving the performance of gradient systems for diffusion-weighted MRI. MAGN RESON MED , 58 (4) 763 - 768. 10.1002/mrm.21379.
The MR signal is sensitive to diffusion. This effect can be increased by the use of large, balanced bipolar gradients. The gradient systems of MR scanners are calibrated at installation and during regular servicing visits. Because the measured apparent diffusion constant (ADC) depends on the square of the amplitude of the diffusion sensitizing gradients, errors in the gradient calibration are exaggerated. If the error is varying among the different gradient axes, it will affect the estimated degree of anisotropy. To assess the gradient calibration accuracy in a whole-body MRI scanner, ADC values were calculated for a uniform water phantom along each gradient direction while monitoring the temperature. Knowledge of the temperature allows the expected diffusion constant of water to be calculated independent of the MRI measurement. It was found that the gradient axes (+/- x, +/- y, +/- z) were calibrated differently, resulting in offset ADC values. A method is presented to rescale the amplitude of each of the six principal gradient axes within the MR pulse sequence. The scaling factor is the square root of the ratio of the expected and observed diffusion constants. In addition, fiber tracking results in the human brain were noticeably affected by improving the gradient system calibration.
|Title:||A method for improving the performance of gradient systems for diffusion-weighted MRI|
|Open access status:||An open access publication|
|Publisher version:||http://www.ncbi.nlm.nih.gov/pmc/ articles/PMC2683063/?tool=pubmed|
|Keywords:||diffusion tensor imaging, apparent diffusion constant, magnetic field gradient, fibre tracking, anisotropy, GEOMETRIC DISTORTION, SELF-DIFFUSION, 3D PHANTOM, IMAGES, ECHO|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Imaging Neuroscience|
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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