Literature DB >> 26573606

Q-ball imaging models: comparison between high and low angular resolution diffusion-weighted MRI protocols for investigation of brain white matter integrity.

Giuseppina Caiazzo1,2, Francesca Trojsi1,2, Mario Cirillo1,2, Gioacchino Tedeschi1,2, Fabrizio Esposito3,4.   

Abstract

INTRODUCTION: Q-ball imaging (QBI) is one of the typical data models for quantifying white matter (WM) anisotropy in diffusion-weighted MRI (DwMRI) studies. Brain and spinal investigation by high angular resolution DwMRI (high angular resolution imaging (HARDI)) protocols exhibits higher angular resolution in diffusion imaging compared to low angular resolution models, although with longer acquisition times. We aimed to assess the difference between QBI-derived anisotropy values from high and low angular resolution DwMRI protocols and their potential advantages or shortcomings in neuroradiology.
METHODS: Brain DwMRI data sets were acquired in seven healthy volunteers using both HARDI (b = 3000 s/mm(2), 54 gradient directions) and low angular resolution (b = 1000 s/mm(2), 32 gradient directions) acquisition schemes. For both sequences, tract of interest tractography and generalized fractional anisotropy (GFA) measures were extracted by using QBI model and were compared between the two data sets.
RESULTS: QBI tractography and voxel-wise analyses showed that some WM tracts, such as corpus callosum, inferior longitudinal, and uncinate fasciculi, were reconstructed as one-dominant-direction fiber bundles with both acquisition schemes. In these WM tracts, mean percent different difference in GFA between the two data sets was less than 5%. Contrariwise, multidirectional fiber bundles, such as corticospinal tract and superior longitudinal fasciculus, were more accurately depicted by HARDI acquisition scheme.
CONCLUSION: Our results suggest that the design of optimal DwMRI acquisition protocols for clinical investigation of WM anisotropy by QBI models should consider the specific brain target regions to be explored, inducing researchers to a trade-off choice between angular resolution and acquisition time.

Entities:  

Keywords:  DTI; Fiber tracts; Fractional anisotropy; HARDI; White matter

Mesh:

Year:  2015        PMID: 26573606     DOI: 10.1007/s00234-015-1616-3

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.804


  23 in total

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2.  Diffusion tensor MR imaging of the human brain.

Authors:  C Pierpaoli; P Jezzard; P J Basser; A Barnett; G Di Chiro
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3.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.

Authors:  J-Donald Tournier; Fernando Calamante; David G Gadian; Alan Connelly
Journal:  Neuroimage       Date:  2004-11       Impact factor: 6.556

4.  Optimal imaging parameters for fiber-orientation estimation in diffusion MRI.

Authors:  Daniel C Alexander; Gareth J Barker
Journal:  Neuroimage       Date:  2005-08-15       Impact factor: 6.556

5.  Comparison of gradient encoding directions for higher order tensor diffusion data.

Authors:  Sarah C Mang; Daniel Gembris; Wolfgang Grodd; Uwe Klose
Journal:  Magn Reson Med       Date:  2009-02       Impact factor: 4.668

6.  Optimal acquisition schemes in high angular resolution diffusion weighted imaging.

Authors:  V Prckovska; A F Roebroeck; W L P M Pullens; A Vilanova; B M ter Haar Romeny
Journal:  Med Image Comput Comput Assist Interv       Date:  2008

7.  Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging.

Authors:  J-Donald Tournier; Fernando Calamante; Alan Connelly
Journal:  NMR Biomed       Date:  2013-08-29       Impact factor: 4.044

8.  A comprehensive reliability assessment of quantitative diffusion tensor tractography.

Authors:  Jun Yi Wang; Hervé Abdi; Khamid Bakhadirov; Ramon Diaz-Arrastia; Michael D Devous
Journal:  Neuroimage       Date:  2011-12-29       Impact factor: 6.556

9.  How does B-value affect HARDI reconstruction using clinical diffusion MRI data?

Authors:  Sangma Xie; Nianming Zuo; Liqing Shang; Ming Song; Lingzhong Fan; Tianzi Jiang
Journal:  PLoS One       Date:  2015-03-24       Impact factor: 3.240

10.  Optimal short-time acquisition schemes in high angular resolution diffusion-weighted imaging.

Authors:  V Prčkovska; H C Achterberg; M Bastiani; P Pullens; E Balmashnova; B M Ter Haar Romeny; A Vilanova; A Roebroeck
Journal:  Int J Biomed Imaging       Date:  2013-03-11
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  4 in total

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Authors:  Carolin Weiss Lucas; Andrea Maria Faymonville; Ricardo Loução; Catharina Schroeter; Charlotte Nettekoven; Ana-Maria Oros-Peusquens; Karl Josef Langen; N Jon Shah; Gabriele Stoffels; Volker Neuschmelting; Tobias Blau; Hannah Neuschmelting; Martin Hellmich; Martin Kocher; Christian Grefkes; Roland Goldbrunner
Journal:  Front Oncol       Date:  2022-05-27       Impact factor: 5.738

2.  Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging.

Authors:  Kurt G Schilling; Vishwesh Nath; Justin A Blaber; Prasanna Parvathaneni; Adam W Anderson; Bennett A Landman
Journal:  Magn Reson Imaging       Date:  2017-04-24       Impact factor: 2.546

3.  Structural connectome with high angular resolution diffusion imaging MRI: assessing the impact of diffusion weighting and sampling on graph-theoretic measures.

Authors:  Giuseppina Caiazzo; Michele Fratello; Federica Di Nardo; Francesca Trojsi; Gioacchino Tedeschi; Fabrizio Esposito
Journal:  Neuroradiology       Date:  2018-03-08       Impact factor: 2.804

Review 4.  Evaluation and Prediction of Post-stroke Cerebral Edema Based on Neuroimaging.

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Journal:  Front Neurol       Date:  2022-01-11       Impact factor: 4.003

  4 in total

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