Does Low B-value can Handle Q-ball and DTI Reconstructions? - Diffusion MRI Experiment of Ex-vivo Pigs Spinal Cord Phantom

Aleksandra Klimas, Kamil Gorczewski, Przemysław Pencak, Zofia Drzazga, Uwe Klose

Abstract

The direction of axons in white matter can be estimated using a deterministic fibre tracking algorithms and diffusion weighted imaging. The aim of this work was to evaluate the data, obtained from pig spines phantom measurements with relatively low b-value, using two types of reconstructions: diffusion tensor imaging (DTI) and q-ball approach. Pigs spines submerged in agar gel were used to prepare a phantom with two crossing populations of fibres. The phantoms were measured in 3T MR scanned for b-value of 1000 and 2000 s/mm2 for q-ball and 200-2000s/mm2 for DTI reconstruction. Analysis of crossing and single fibre population regions in the scanners showed that the median dispersions from the reference directions in case of single fibre population were c.a. 4° and for crossing area c.a. 12° and 6.5° for b-value of 1000 s/mm2 and 2000 s/mm2 respectively. The q-ball approach was able to resolve crossing problem for both low b-values. It was shown here that coherent results can be achieved even with lower b-values than proposed by the theory.

References

  1. Le Bihan D., Turner R., Douek P., Patronas N. (1992). Diffusion MR imaging: clinical applications. American Journal of Roentgenology,159(3), 591-599.
  2. Basser P. J., Mattiello J., LeBihan D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance - Series B, 103(3), 247-254.
  3. Bammer R. (2003). Basic principles of diffusion-weighted imaging. European Journal of Radiology, 45(3), 169- 184.
  4. Jones D.K. (2004). The Effect of Gradient Sampling Schemes on Measures Derived From Diffusion Tensor MRI: A Monte Carlo Study. Magnetic Resonance in Medicine. 51(4), 807-815.
  5. Jellison B. J., Field A. S., Medow J., Lazar M., M. Shariar Salamat and Alexander A. L. (2004). Diffusion Tensor Imaging of Cerebral White Matter: A Pictorial Review of Physics, Fiber Tract Anatomy, and Tumor Imaging Patterns. American Journal of Neuroradiology. 25, 356-369.
  6. Masutani Y., Aoki S., Abe O., Hayashi N., Otomo K. (2003). MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. European Journal of Radiology, 46, 53-/66.
  7. Madi, S., Hasan, K. M., Narayana, P. A. (2005). Diffusion tensor imaging of in vivo and excised rat spinal cord at 7 T with an icosahedral encoding scheme. Magnetic Resonance in Medicine, 53, 118-125.
  8. Campbell, J. S., Siddiqi, K., Rymar, V. (2005). Flowbased fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques. Neuroimage; 27(4) 725-736.
  9. Assaf, Y., Freidlin, R. Z., Rohde, G. K., Basser, P. J. (2004). New Modeling and Experimental Framework to Characterize Hindered and Restricted Water Diffusion in Brain White Matter. Magnetic Resonance in Medicine, 52, 965-978.
  10. Perrin, M., Poupon, C., Rieul, B., Leroux, P. (2005). Validation of q-ball imaging with a diffusion fibrecrossing phantom on a clinical scanner. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 881-891.
  11. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R. (2007). Regularized, Fast, and Robust Analytical QBall Imaging. Magnetic Resonance in Medicine, 58, 497-510.
  12. Freidlin, R. Z., Ozarslan, E., Komlosh, M. E., Chang, L. (2007). Parsimonious Model Selection for DTI Tissue Segmentation and Classification: Study on Simulated and Experimental Data. IEEE Transactions on Medical Imaging,26(11), 1576-1584.
  13. Sundgren, P. C., Dong, Q., Gómez-Hassan, D., Mukherji (2004). Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology, 46, 339-350.
  14. Tuch D. S. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52( 6), 1358-1372.
  15. Ozarslan, E., Shepherd, T. M., Vemuri, B. C., Blackband, S. J. (2006). Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). Neuroimage, 31(3), 1086-1103.
  16. Tournier, J. D., Calamante, F., Gadian, D. G., Connelly, A. (2004). Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage, 23( 3), 1176-1185.
  17. Parker, G. J., Alexander, D. C. (2005). Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 893-902.
  18. Klimas, A., Gorczewski, K., Klose, U., Drzazga, Z. (2008). Preparation and validation of an easy-to-make organic DTI phantom. 41st Polish Seminar on Nuclear Magnetic Resonance and Its Applications.
  19. Reese, T. G., Heid, O., Weisskoff, R. M., Wedeen, V. J. (2003). Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magnetic Resonance in Medicine, 49(1), 177-182.
  20. Gorczewski K. (2010). Multi-directional diffusion weighted imaging: Implementation, verification and clinical application, PhD thesis.
  21. McEwen J. D., Wiaux Y. (2011). A novel sampling theorem on the sphere. IEEE Transactions on Signal Processing, 59, 5876-5887.
  22. Daducci A., McEwen J., Van De Ville D., Thiran J. P., Wiaux Y. (2011). Harmonic analysis of spherical sampling in diffusion MRI. Proceedings of the International Society for Magnetic Resonance in Medicine, 3929.
  23. Gorczewski, K., Mang, S, Klose, U. (2009). Reproducibility and consistency of evaluation techniques for HARDI data. Magnetic Resonance Materials in Physics Biology and Medicine, 22(1), 63- 70.
Download


Paper Citation


in Harvard Style

Klimas A., Gorczewski K., Pencak P., Drzazga Z. and Klose U. (2013). Does Low B-value can Handle Q-ball and DTI Reconstructions? - Diffusion MRI Experiment of Ex-vivo Pigs Spinal Cord Phantom . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 401-406. DOI: 10.5220/0004329304010406


in Bibtex Style

@conference{biosignals13,
author={Aleksandra Klimas and Kamil Gorczewski and Przemysław Pencak and Zofia Drzazga and Uwe Klose},
title={Does Low B-value can Handle Q-ball and DTI Reconstructions? - Diffusion MRI Experiment of Ex-vivo Pigs Spinal Cord Phantom},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={401-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004329304010406},
isbn={978-989-8565-36-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Does Low B-value can Handle Q-ball and DTI Reconstructions? - Diffusion MRI Experiment of Ex-vivo Pigs Spinal Cord Phantom
SN - 978-989-8565-36-5
AU - Klimas A.
AU - Gorczewski K.
AU - Pencak P.
AU - Drzazga Z.
AU - Klose U.
PY - 2013
SP - 401
EP - 406
DO - 10.5220/0004329304010406