Efficient and robust computation of PDF features from diffusion MR signal

Abstract : We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.
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Medical Image Analysis, Elsevier, 2009, pp.1-16. 〈10.1016/j.media.2009.06.004〉
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Haz-Edine Assemlal, David Tschumperlé, Luc Brun. Efficient and robust computation of PDF features from diffusion MR signal. Medical Image Analysis, Elsevier, 2009, pp.1-16. 〈10.1016/j.media.2009.06.004〉. 〈hal-00410615〉

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