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Compressive Sensing DSI

Sylvain L. Merlet 1 Michael Paquette 2 Rachid Deriche 1 Maxime Descoteaux 3 
1 ATHENA - Computational Imaging of the Central Nervous System
CRISAM - Inria Sophia Antipolis - Méditerranée
SCIL - Sherbrooke Connectivity Imaging Lab [Sherbrooke]
Abstract : Compressive Sensing (CS) [2], [1] offers an efficient way to decrease the number of measurements required in Diffusion Spectrum Imaging (DSI). This method aims to reconstruct the Ensemble Average Propagator (EAP) and, for the purpose of this contest, we compute the numerical Orientation Distribution Function (ODF) by integrating the EAP over a solid angle. In this abstract, we briefly describe three important points underlying the CS technique in order to accelerate DSI, namely the sparsity, the Restricted Isometry Property (RIP) and the ï¿¿1 reconstruction scheme. Due to the high b-values required in the sampling protocol, our approach enters the heavyweight sampling category. Nevertheless, only 64 measurements are used for the reconstruction.
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Submitted on : Friday, November 22, 2013 - 2:33:16 PM
Last modification on : Saturday, June 25, 2022 - 11:11:53 PM


  • HAL Id : hal-00908209, version 1



Sylvain L. Merlet, Michael Paquette, Rachid Deriche, Maxime Descoteaux. Compressive Sensing DSI. International Symposium on BIOMEDICAL IMAGING: From Nano to Macro - ISBI HARDI Reconstruction Challenge, Apr 2013, San Francisco, United States. ⟨hal-00908209⟩



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