A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

Wu, Yu-Chien; Alexander, Andrew L.

doi:10.1097/rct.0b013e31805152fa

Cited by 20 publications

(17 citation statements)

References 20 publications

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“…This confirms similar findings by others 1,2,4,13 . To improve the quality and reproducibility of quantitative studies and to facilitate multi-center studies, a correction for these effects is needed.…”

Section: Discussionsupporting

confidence: 93%

Stability of gradient field corrections for quantitative diffusion MRI

et al. 2017

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In magnetic resonance diffusion imaging, gradient nonlinearity causes significant bias in the estimation of quantitative diffusion parameters such as diffusivity, anisotropy, and diffusion direction in areas away from the magnet isocenter. This bias can be substantially reduced if the scanner- and coil-specific gradient field nonlinearities are known. Using a set of field map calibration scans on a large (29 cm diameter) phantom combined with a solid harmonic approximation of the gradient fields, we predicted the obtained b-values and applied gradient directions throughout a typical field of view for brain imaging for a typical 32-direction diffusion imaging sequence. We measured the stability of these predictions over time. At 80 mm from scanner isocenter, predicted b-value was 1-6% different than intended due to gradient nonlinearity, and predicted gradient directions were in error by up to 1 degree. Over the course of one month the change in these quantities due to calibration-related factors such as scanner drift and variation in phantom placement was <0.5% for b-values, and <0.5 degrees for angular deviation. The proposed calibration procedure allows the estimation of gradient nonlinearity to correct b-values and gradient directions ahead of advanced diffusion image processing for high angular resolution data, and requires only a five-minute phantom scan that can be included in a weekly or monthly quality assurance protocol.

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Section: Discussionsupporting

confidence: 93%

Stability of gradient field corrections for quantitative diffusion MRI

et al. 2017

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“…In this approach, we decided to concentrate on global corrections for FA and tractography analysis, i.e., without attempting to calibrate the absolute gradient amplitude. If the absolute quantification of diffusivity is important, methods like or should be adopted.…”

Section: Discussionmentioning

confidence: 99%

“…Several phantoms have been proposed for analyzing gradient performance. Liquids of known diffusivity are used to calibrate the absolute gradient power , but require a constant temperature check, which can be tricky in the MR scan room. Phantoms for checking FA and fiber orientation estimates are generally built ad hoc .…”

Section: Introductionmentioning

confidence: 99%

RAPID: A routine assurance pipeline for imaging of diffusion

Santis

Evans

Jones

2012

Magnetic Resonance in Med

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Despite the widespread adoption of diffusion MRI techniques, there is still no consensus on a comprehensive quality assurance routine specific for diffusion acquisitions. We propose here a routine assurance pipeline for imaging of diffusion. The routine simply comprises diffusion-weighted acquisitions on a phantom; each repetition lasts less than 5 min and can be performed using a variety of isotropic test liquids. The proposed QA script checks for the linearity of G, the uniformity of G max across the field-of-view, the mutual agreement of gradient power across the three logical axes and the temporal stability. Optionally, the routine can correct for the mutual agreement of gradient power along the three axes, returning a set of gradient orientations to be used in data analysis. The effectiveness of the scheme in the presence of mismatched gradient amplitudes is reported using both simulations and in vivo data. The script is freely available online.

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“…Full DTI determination becomes more impractical for multi‐ b‐ value studies . To streamline correction for background and imaging gradient errors in DTI, a simplified empiric calibration algorithm was introduced based on a regression model, without reference to the system's hardware characteristics. Such approach is dependent on specific gradient hardware and waveforms, and may require recalibration and error analysis for each clinical scan.…”

mentioning

confidence: 99%

Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements

Malyarenko

Ross

Chenevert

2013

Magnetic Resonance in Med

108

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Purpose Gradient nonlinearity of MRI systems leads to spatially-dependent b-values and consequently high non-uniformity errors (10–20%) in ADC measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. Methods All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Results Spatial dependence of nonlinearity correction terms accounts for the bulk (75–95%) of ADC bias for FA = 0.3–0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. Conclusions The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients.

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A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

Cited by 20 publications

References 20 publications

Stability of gradient field corrections for quantitative diffusion MRI

Stability of gradient field corrections for quantitative diffusion MRI

RAPID: A routine assurance pipeline for imaging of diffusion

Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements

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