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Home arrow Computer Science arrow Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016

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Conclusion

We investigated the effects of phase correction of DWIs in terms of signal debiasing and Noise Floor removal. We quantitatively assess that phase correction has the potential of rendering nearly unbiased DTI and q-space metrics. Indeed, the noise distribution transformation, from Rician to Gaussian, allows compliance with the assumptions required to use standard least squares methods for signal estimation, thus avoiding noise floor related signal overestimation. In this work, we illustrate the importance of accurate phase estimation of complex DWIs, necessary condition for a good phase correction. We plan to extend this work to other diffusion signal metrics, such as those derived from NODDI [5]. We believe that phase correction is a still challenging but promising tool for boosting the estimation of diffusion metrics.

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-Cote d’Azur (PACA) Regional Council for providing grant and support for this work.

 
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