A theoretical validation of the B-matrix spatial distribution approach to diffusion tensor imaging

Borkowski, Karol; Kłodowski, Krzysztof; Figiel, H.; Krzyżak, Artur

doi:10.1016/j.mri.2016.10.002

Cited by 22 publications

(20 citation statements)

References 26 publications

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“…The gradient non-linearities can have a significant impact on diffusion studies in ways other than simple image distortion, if positioning is not well controlled. Depending on the non-linearity, the bmatrices at each voxel location in the brain will be spatially dependent and differ from the nominal values (Bammer et al, 2003;Borkowski, Kłodowski, Figiel, & Krzyżak, 2017;Malyarenko, Ross, & Chenevert, 2014;Mohammadi et al, 2012). Besides, gradient non-linearity distorts the true image in concert with B 0 inhomogeneity, making it harder to disentangle the two effects in a sequential manner at the postprocessing stage.…”

Section: Gradient Non-linearitymentioning

confidence: 99%

Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI

et al. 2018

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The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.

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Section: Gradient Non-linearitymentioning

confidence: 99%

Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI

et al. 2018

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“…Apart from the mean value, diffusion gradient nonlinearity also influences the variance of the eigenvalues and the orientation of the diffusion ellipsoid . These effects have already been analyzed using a similar methodology and are therefore not considered herein.…”

Section: Methodsmentioning

confidence: 99%

“…Their shapes resemble the real G x distortion obtained using a direct field mapping for a Siemens Vision 1.5 T. Although the pattern function was designed to mimic the real properties of the gradient field, it constitutes a specific case. Nevertheless, its shape has little impact on statistical measures such as mean eigenvalues …”

Section: Methodsmentioning

confidence: 99%

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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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“…Even if an ideal preprocessing pipeline that addresses all the confounds listed above were to be developed, as noted above, gradient nonlinearities also lead to spatial variations in diffusion weighting. While this is benign for head scanning with most clinical scanners, where spatial uniformity of gradient amplitude is more achievable, the strong gradient nonlinearities in bespoke ultra‐strong gradient systems has prompted the development of dedicated diffusion data analysis pipelines 10 that account for gradient‐nonlinearity‐induced spatial variations in B‐matrices 8,11‐15 . Here, we consider for the first time, the interaction of subject motion with gradient nonuniformity on diffusion measurements, as the effect of diffusion‐weighting cannot be captured using spatially varying B‐matrices alone.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners

Rudrapatna

Parker

Roberts

et al. 2020

Magnetic Resonance in Med

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Purpose The analysis of diffusion data obtained under large gradient nonlinearities necessitates corrections during data reconstruction and analysis. While two such preprocessing pipelines have been proposed, no comparative studies assessing their performance exist. Furthermore, both pipelines neglect the impact of subject motion during acquisition, which, in the presence of gradient nonlinearities, induces spatio‐temporal B‐matrix variations. Here, spatio‐temporal B‐matrix tracking (STB) is proposed and its performance compared to established pipelines. Methods Diffusion tensor MRI (DT‐MRI) was performed using a 300 mT/m gradient system. Data were acquired with volunteers positioned in regions with pronounced gradient nonlinearities, and used to compare the performance of six different processing pipelines, including STB. Results Up to 30% errors were observed in DT‐MRI parameter estimates when neglecting gradient nonlinearities. Moreover, the order in which B 0 inhomogeneity, eddy current and gradient nonlinearity corrections were performed was found to impact the consistency of parameter estimates significantly. Although, no pipeline emerged as a clear winner, the STB approach seemed to yield the most consistent parameter estimates under large gradient nonlinearities. Conclusions Under large gradient nonlinearities, the choice of preprocessing pipeline significantly impacts the estimated diffusion parameters. Motion‐induced spatio‐temporal B‐matrix variations can lead to systematic bias in the parameter estimates, that can be ameliorated using the proposed STB framework.

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A theoretical validation of the B-matrix spatial distribution approach to diffusion tensor imaging

Cited by 22 publications

References 26 publications

Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI

Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI

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

A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners

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