Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers

Aja‐Fernández, Santiago; Pieciak, Tomasz; Tristán‐Vega, Antonio; Vegas-Sánchez-Ferrero, Gonzalo; Molina, Vicente; Luis‐García, Rodrigo de

doi:10.1016/j.mri.2018.03.001

Cited by 12 publications

(14 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This implies the results of clinical trials could be compared against each other only if the same b-value is preserved across the studies. This is by no means something new to diffusion imaging: it is well-known that a change in the acquisition parameters (number of gradients, b-value, resolution, scanner vendor, etcetera) seriously affects scalar measures like the FA or the MD (Aja-Fernández et al, 2018;Barrio-Arranz et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Apparent Propagator Anisotropy from reduced Diffusion MRI acquisitions

Aja‐Fernández

Tristán‐Vega

Jones

2019

Preprint

Self Cite

View full text Add to dashboard Cite

The Propagator Anisotropy (PA) is a measurement of the orientational variability inside a tissue estimated from diffusion MRI using the Ensemble Average diffusion Propagator (EAP). It is based on the quantification of the angular difference between the propagator in a specific voxel and its isotropic counterpart. The PA has shown the ability to reveal microstructural information of interest and meaningful descriptive maps inside the white matter. However, the use of PA is not generalized among the clinical community, due to the great amount of data needed for its calculation, together with the associated long processing times. In order to calculate the PA, the EAP must also be properly estimated. This task would require a dense sampling of the Cartesian q-space. Alternatively, more efficient techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values and long processing times.In this work, we propose an alternative implementation to drastically reduce the number of samples needed, as well as boosting the estimation procedure. We avoid the calculation of the whole EAP by assuming that the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, we achieve a closed-form expression for a measure similar to the PA but using information from one single shell: the Apparent Propagator Anisotropy (APA). The new measure remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). The intention of the APA is not to exactly replicate the PA but inferring microstructural information with comparable discrimination power as the PA but using a reduced amount of data.We report extensive results showing that the proposed measures present a robust behavior in clinical studies and they are computationally efficient and robust when compared with PA and other anisotropy measures.

show abstract

Section: Discussionmentioning

confidence: 99%

“…we have approximated the surface integral by SH. However, for a noise analysis, it would be more convenient to use a summation, similar to what was done inAja-Fernández et al (2018):where H i are N samples of H(S) uniformly distributed over the surface of the sphere. THis way, ve can write…”

mentioning

confidence: 99%

Apparent Propagator Anisotropy from reduced Diffusion MRI acquisitions

Aja‐Fernández

Tristán‐Vega

Jones

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The DiA can be seen as a generalization of the coefficient of variation of the diffusion (CVD) defined in Ref. [24] as a robust alternative for the FA. According to Ref.…”

Section: Methodsmentioning

confidence: 99%

Apparent propagator anisotropy from single‐shell diffusion MRI acquisitions

Aja‐Fernández

Tristán‐Vega

Jones

2020

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

The apparent propagator anisotropy (APA) is a new diffusion MRI metric that, while drawing on the benefits of the ensemble averaged propagator anisotropy (PA) compared to the fractional anisotropy (FA), can be estimated from single-shell data. Theory and Methods: Computation of the full PA requires acquisition of large datasets with many diffusion directions and different b-values, and results in extremely long processing times. This has hindered adoption of the PA by the community, despite evidence that it provides meaningful information beyond the FA. Calculation of the complete propagator can be avoided under the hypothesis that a similar sensitivity/specificity may be achieved from apparent measurements at a given shell. Assuming that diffusion anisotropy (DiA) is nondependent on the b-value, a closedform expression using information from one single shell (ie, b-value) is reported. Results: Publicly available databases with healthy and diseased subjects are used to compare the APA against other anisotropy measures. The structural information provided by the APA correlates with that provided by the PA for healthy subjects, while it also reveals statistically relevant differences in white matter regions for two pathologies, with a higher reliability than the FA. Additionally, APA has a computational complexity similar to the FA, with processing-times several orders of magnitude below the PA. Conclusions: The APA can extract more relevant white matter information than the FA, without any additional demands on data acquisition. This makes APA an attractive option for adoption into existing diffusion MRI analysis pipelines.

show abstract

“…A common implementation in commercial scanners, like EPI-DWI, acquires only 3 separate orthogonal diffusion weighted images (DWIs) with diffusion gradients aligned with directions ( x, y, z ). These 3 DWIs are averaged into a final combined image [1] that resembles measures like the Mean Diffusivity (MD) [2] or the Average Sample Diffusion [5]. Due to the limitation in the number of gradient directions, no extra information is provided.…”

Section: Introductionmentioning

confidence: 99%

Anisotropy Measure from Three Diffusion-Encoding Gradient Directions

Aja‐Fernández¹,

París²,

Tristán‐Vega³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

PurposeWe propose a method that can provide information about the anisotropy and orientation of diffusion in the brain from only 3 orthogonal gradient directions without imposing additional assumptions.MethodsThe method is based on the Diffusion Anisotropy (DiA) that measures the distance from a diffusion signal to its isotropic equivalent. The original formulation based on a Spherical Harmonics basis allows to go down to only 3 orthogonal directions in order to estimate the measure. In addition, an alternative simplification and a color-coding representation are also proposed.ResultsAcquisitions from a publicly available database are used to test the viability of the proposal. The DiA succeeded in providing anisotropy information from the white matter using only 3 diffusion-encoding directions. The price to pay for such reduced acquisition is an increment in the variability of the data and a subestimation of the metric.ConclusionsThe calculation of anisotropy information from DMRI is feasible using fewer than 6 gradient directions by using DiA. The method is totally compatible with existing acquisition protocols and it may provide complementary information about orientation in fast diffusion acquisitions.

show abstract

Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers

Cited by 12 publications

References 59 publications

Apparent Propagator Anisotropy from reduced Diffusion MRI acquisitions

Apparent Propagator Anisotropy from reduced Diffusion MRI acquisitions

Apparent propagator anisotropy from single‐shell diffusion MRI acquisitions

Anisotropy Measure from Three Diffusion-Encoding Gradient Directions

Contact Info

Product

Resources

About