Stability of gradient field corrections for quantitative diffusion MRI

Rogers, Baxter P.; Blaber, Justin A.; Welch, E. Brian; Ding, Zhaohua; Anderson, Adam W.; Landman, Bennett A.

doi:10.1117/12.2254609

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…We measured the magnetic field produced by each gradient coil using a large oil-filled phantom as described in previous work Rogers 2017a,b . Solid harmonic basis functions were then fit to the measured field, and the fitted field was used to estimate the actually achieved diffusion gradient directions and b-values on a voxel-wise basis.…”

Section: Methods and Datamentioning

confidence: 99%

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging

Rogers

Blaber

Newton

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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Gradient coils in magnetic resonance imaging do not produce perfectly linear gradient fields. For diffusion imaging, the field nonlinearities cause the amplitude and direction of the applied diffusion gradients to vary over the field of view. This leads to site- and scan-specific systematic errors in estimated diffusion parameters such as diffusivity and anisotropy, reducing reliability especially in studies that take place over multiple sites. These errors can be substantially reduced if the actual scanner-specific gradient coil magnetic fields are known. The nonlinearity of the coil fields is measured by scanner manufacturers and used internally for geometric corrections, but obtaining and using the information for a specific scanner may be impractical for many sites that operate without special-purpose local engineering and research support. We have implemented an empirical field-mapping procedure using a large phantom combined with a solid harmonic approximation to the coil fields that is simple to perform and apply. Here we describe the accuracy and precision of the approach in reproducing manufacturer gold standard field maps and in reducing spatially varying errors in quantitative diffusion imaging for a specific scanner. Before correction, median B value error ranged from 33 – 41 relative to manufacturer specification at 100 mm from isocenter; correction reduced this to 0 – 4. On-axis spatial variation in the estimated mean diffusivity of an isotropic phantom was 2.2% – 4.1% within 60 mm of isocenter before correction, 0.5% – 1.6% after. Expected fractional anisotropy in the phantom was 0; highest estimated fractional anisotropy within 60 mm of isocenter was reduced from 0.024 to 0.012 in the phase encoding direction (48% reduction) and from 0.020 to 0.006 in the frequency encoding direction (72% reduction).

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Section: Methods and Datamentioning

confidence: 99%

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging

Rogers

Blaber

Newton

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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“…Water phantom studies (Nagy et al, 2009;Rogers et al, 2018Rogers et al, , 2017 reveal gradient field inhomogeneities as one of the main factors contributing to inaccuracies in the estimated apparent diffusion coefficient (ADC). Biases of up to 10% in diffusion measures derived from diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) resulting from gradient field nonlinearities have been reported (Bammer et al, 2003;Mesri, David, Viergever, & Leemans, 2020.…”

Section: Introductionmentioning

confidence: 99%

“…The effect of gradient nonlinearities in diffusion MRI and their corrections have been investigated in several studies (Bammer et al, 2003; Jovicich et al, 2006; Malyarenko et al, 2014; Mohammadi et al, 2012; Nagy et al, 2007; Setsompop et al, 2013). Water phantom studies (Nagy et al, 2009; Rogers et al, 2018, 2017) reveal gradient field inhomogeneities as one of the main factors contributing to inaccuracies in the estimated apparent diffusion coefficient (ADC). Biases of up to 10% in diffusion measures derived from diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) resulting from gradient field nonlinearities have been reported (Bammer et al, 2003; Mesri, David, Viergever, & Leemans, 2020, 2018).…”

Section: Introductionmentioning

confidence: 99%

The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data

Guo

Luca

Parker

et al. 2020

Human Brain Mapping

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Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson-Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b-matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b-values in contrast to the perhaps common assumption that only high b-value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable.

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“…10 A powerful method for the correction of gradient nonlinearity was proposed by Bammer et al 11 and adapted by Malyarenko 12,13 and others. 14 It consists in indicating the so-called coil tensor, which defines the spatial distribution of the gradient field. The tensor can be estimated either on the basis of the specification provided by the manufacturer or by a mapping of the magnetic field.…”

Section: Introductionmentioning

confidence: 99%

“…A powerful method for the correction of gradient nonlinearity was proposed by Bammer et al and adapted by Malyarenko and others . It consists in indicating the so‐called coil tensor, which defines the spatial distribution of the gradient field.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the systematic errors caused by diffusion gradient inhomogeneity in DTI‐computer simulations

Borkowski

Krzyżak

2019

NMR in Biomedicine

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Diffusion tensor imaging (DTI) is a powerful MRI modality that allows the investigation of the microstructure of tissues both in vivo and noninvasively. Its reliability is strictly dependent on the performance of diffusion‐sensitizing gradients, of which spatial nonuniformity is a known issue in the case of virtually all clinical MRI scanners. The influence of diffusion gradient inhomogeneity on the accuracy of the diffusion tensor imaging was investigated by means of computer simulations supported by an MRI experiment performed at the isocenter and 15 cm away. The DTI measurements of two diffusion phantoms were simulated assuming a nonuniform diffusion‐sensitizing gradient and various levels of noise. Thereafter, the tensors were calculated by two methods: (i) assuming a spatially constant b‐matrix (standard DTI) and (ii) applying the b‐matrix spatial distribution in the DTI (BSD‐DTI) technique, a method of indicating the b‐matrix for each voxel separately using an anisotropic phantom as a standard of diffusion. The average eigenvalues and fractional anisotropy across the homogeneous region of interest were calculated and compared with the expected values. Diffusion gradient inhomogeneity leads to overestimation of the largest eigenvalue, underestimation of the smallest one and thus overestimation of fractional anisotropy. The effect is similar to that caused by noise; however, it could not be corrected by increasing SNR. The MRI measurements, performed using a 3 T clinical scanner, revealed that the split of the eigenvalues measured 15 cm away from the isocenter is significant (up to 25%). The BSD‐DTI calibration allowed the reduction of the measured fractional anisotropy of the isotropic medium from 0.174 to 0.031, suggesting that gradient inhomogeneity was the main cause of this error. For the phantom measured at the isocenter, however, the split was almost not observed; the average eigenvalues were shifted from the expected value by ~ 5%.

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Stability of gradient field corrections for quantitative diffusion MRI

Cited by 15 publications

References 13 publications

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging

The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data

Assessment of the systematic errors caused by diffusion gradient inhomogeneity in DTI‐computer simulations

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