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

  • Functional Neuroradiology
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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/mm2, 54 gradient directions) and low angular resolution (b = 1000 s/mm2, 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.

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References

  1. Basser PJ, Mattiello J, Le Bihan D (1994) Diffusion tensor spectroscopy and imaging. Biophys J 66:259–267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tournier JD, Mori S, Leemans A (2011) Diffusion tensor imaging and beyond. Magn Reson Med 65:1532–1556

    Article  PubMed  PubMed Central  Google Scholar 

  3. Stejskal EO, Tanner JE (1965) Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 42:288–292

    Article  CAS  Google Scholar 

  4. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G (1996) Diffusion tensor MR imaging of the human brain. Radiology 201:637–648

    Article  CAS  PubMed  Google Scholar 

  5. Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system a technical review. NMR BioMed 15:435–455

    Article  PubMed  Google Scholar 

  6. Caiazzo G, Corbo D, Trojsi F et al (2014) Distributed corpus callosum involvement in amyotrophic lateral sclerosis: a deterministic tractography study using q-ball imaging. J Neurol 261:27–36

    Article  CAS  PubMed  Google Scholar 

  7. Basser PJ (1995) Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8:333–344

    Article  CAS  PubMed  Google Scholar 

  8. Tuch DS (2004) Q-ball imaging. Magn Reson Med 52:1358–1372

    Article  PubMed  Google Scholar 

  9. Corbo D, Caiazzo G, Trojsi F et al (2013) Advantages of QBI in TBSS analyses. Magn Reson Imaging 32:184–189

    Article  PubMed  Google Scholar 

  10. Mang SC, Gembris D, Grodd W, Klose U (2009) Comparison of gradient encoding directions for higher order tensor diffusion data. Magn Reson Med 61:335–343

    Article  PubMed  Google Scholar 

  11. Prčkovska V, Roebroeck AF, Pullens WLPM, Vilanova A, ter Haar Romeny BM (2008) Optimal acquisition schemes in high angular resolution diffusion weighted imaging. Med Image Comput Comput Assist Interv 11:9–17

    PubMed  Google Scholar 

  12. Prčkovska V, Achterberg HC, Bastiani M et al (2013) Optimal short-time acquisition schemes in high angular resolution diffusion-weighted imaging. Int J Biomed Imaging 2013:658583

    PubMed  PubMed Central  Google Scholar 

  13. Tournier JD, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed 26:1775–1786

    Article  PubMed  Google Scholar 

  14. Aganj I, Lenglet C, Sapiro G, Yacoub E, Ugurbil K, Harel N (2010) Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn Reson Med 64:554–566

    PubMed  PubMed Central  Google Scholar 

  15. Mori S, Crain BJ, Chacko VP, van Zijl PC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45:265–269

    Article  CAS  PubMed  Google Scholar 

  16. Hua K, Zhang J, Wakana S et al (2008) Tract probability maps in stereotaxic spaces: analysis of white matter anatomy and tract specific quantification. NeuroImage 39:336–347

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang JY, Abdi H, Bakhadirov K, Diaz-Arrastia R, Devous MD Sr (2012) A comprehensive reliability assessment of quantitative diffusion tensor tractography. NeuroImage 60:1127–1138

    Article  PubMed  PubMed Central  Google Scholar 

  18. Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31:1487–1505

    Article  PubMed  Google Scholar 

  19. Jeurissen B, Leemans A, Tournier J-D, Jones DK, Sijbers J (2013) Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 34:2747–2766

    Article  PubMed  Google Scholar 

  20. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ (2002) High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 48:577–582

    Article  PubMed  Google Scholar 

  21. Hopea T, Westlyed LT, Bjørneruda A (2012) The effect of gradient sampling schemes on diffusion metrics derived from probabilistic analysis and tract-based spatial statistics. Magn Reson Imaging 30(3):402–12

    Article  Google Scholar 

  22. Xie S, Zuo N, Shang L, Song M, Fan L, Jiang T (2015) How does b-value affect HARDI reconstruction using clinical diffusion MRI data? PLoS One 10(3):e0120773

    Article  PubMed  PubMed Central  Google Scholar 

  23. Alexandera DC, Barkerb GJ (2005) Optimal imaging parameters for fiber-orientation estimation in diffusion MRI. NeuroImage 27:357–367

    Article  Google Scholar 

  24. Tournier JD, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23:1176–1185

    Article  PubMed  Google Scholar 

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Acknowledgments

The authors thank Dr. Antonella Paccone for her technical assistance.

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Authors and Affiliations

  1. MRI Research Center SUN-FISM–Neurological Institute for Diagnosis and Care “Hermitage Capodimonte”, Naples, Italy

    Giuseppina Caiazzo, Francesca Trojsi, Mario Cirillo & Gioacchino Tedeschi

  2. Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy

    Giuseppina Caiazzo, Francesca Trojsi, Mario Cirillo & Gioacchino Tedeschi

  3. Department of Medicine and Surgery, University of Salerno, Via Allende, 84081, Baronissi (Salerno), Italy

    Fabrizio Esposito

  4. Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands

    Fabrizio Esposito

Authors
  1. Giuseppina Caiazzo
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  2. Francesca Trojsi
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  3. Mario Cirillo
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  4. Gioacchino Tedeschi
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  5. Fabrizio Esposito
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Corresponding author

Correspondence to Fabrizio Esposito.

Ethics declarations

We declare that this study has been approved by the Ethics Committee of the Second University of Naples and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.

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We declare that we have no conflict of interest.

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Caiazzo, G., Trojsi, F., Cirillo, M. et al. Q-ball imaging models: comparison between high and low angular resolution diffusion-weighted MRI protocols for investigation of brain white matter integrity. Neuroradiology 58, 209–215 (2016). https://doi.org/10.1007/s00234-015-1616-3

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  • DOI: https://doi.org/10.1007/s00234-015-1616-3

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