Abstract
The non-Gaussian noise distribution in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging and on Mean Apparent Propagator Magnetic Resonance Imaging, i.e. q-space metrics. We show that phase correction is still a challenge, but also an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jones, D.K., Basser, P.J.: Squashing peanuts and smashing pumpkins: how noise distorts diffusion-weighted MR data. Magn. Reson. Med. 52 (5), 979–993 (2004)
Aja-Fernández, S., Tristán-Vega, A.: A review on statistical noise models for magnetic resonance imaging. LPI, ETSI Telecomunicacion, Universidad de Valladolid, Technical Report (2013)
Özarslan, E., Koay, C.G., Shepherd, T.M., Komlosh, M.E., İrfanoğlu, M.O., Pierpaoli, C., Basser, P.J.: Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. NeuroImage 78, 16–32 (2013)
Alexander, D.C.: A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features. Magn. Reson. Med. 60 (2), 439–448 (2008)
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61 (4), 1000–1016 (2012)
Bernstein, M.A., Thomasson, D.M., Perman, W.H.: Improved detectability in low signal-to-noise ratio magnetic resonance images by means of a phase-corrected real reconstruction. Med. Phys. 16 (5), 813–817 (1989)
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66 (1), 259–267 (1994)
Pizzolato, M., Ghosh, A., Boutelier, T., Deriche, R.: Magnitude and complex based diffusion signal reconstruction. In: CDMRI, pp. 127–140 (2014)
Liu, C., Bammer, R., Acar, B., Moseley, M.E.: Characterizing non-gaussian diffusion by using generalized diffusion tensors. Magn. Reson. Med. 51 (5), 924–937 (2004)
Pizzolato, M., Wassermann, D., Boutelier, T., Deriche, R.: Exploiting the phase in diffusion MRI for microstructure recovery: towards axonal tortuosity via asymmetric diffusion processes. In: MICCAI (2015)
Pizzolato, M., Wassermann, D., Duval, T., Campbell, J.S., Boutelier, T., Cohen-Adad, J., Deriche, R.: A temperature phantom to probe the ensemble average propagator asymmetry: an in-silico study. In: CDMRI, pp. 183–194 (2016)
Prah, D.E., Paulson, E.S., Nencka, A.S., Schmainda, K.M.: A simple method for rectified noise floor suppression: phase-corrected real data reconstruction with application to diffusion-weighted imaging. Magn. Reson. Med. 64 (2), 418–429 (2010)
Sprenger, T., Sperl, J.I., Fernandez, B., Haase, A., Menzel, M.I.: Real valued diffusion-weighted imaging using decorrelated phase filtering. Magn. Reson. Med. (2016). doi:10.1002/mrm.26138
Eichner, C., Cauley, S.F., Cohen-Adad, J., Möller, H.E., Turner, R., Setsompop, K., Wald, L.L.: Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast. NeuroImage 122, 373–384 (2015)
Caruyer, E., Daducci, A., Descoteaux, M., Houde, J.-C., Thiran J.-P., Verma, R.: Phantomas: a flexible software library to simulate diffusion MR phantoms. In: ISMRM14 (2014)
Caruyer, E., Lenglet, C., Sapiro, G., Deriche, R.: Design of multishell sampling schemes with uniform coverage in diffusion MRI. Magn. Reson. Med. 69 (6), 1534–1540 (2013)
Fick, R.H., Wassermann, D., Caruyer, E., Deriche, R.: MAPL: tissue microstructure estimation using Laplacian-regularized MAP-MRI and its application to HCP data. NeuroImage 134, 365–385 (2016)
Hellinger, E.: Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. J Reine Angew. Math. 136, 210–271 (1909)
Acknowledgements
Data for this project was provided by the MGH-USC Human Connectome Project. This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM).
Marco Pizzolato expresses his thanks to Olea Medical and the Provence-Alpes-Côte d’Azur (PACA) Regional Council for providing grant and support for this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Pizzolato, M., Fick, R., Boutelier, T., Deriche, R. (2017). Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-Space Metrics. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. MICCAI 2016. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-54130-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-54130-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54129-7
Online ISBN: 978-3-319-54130-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)