Efficient and robust computation of PDF features from diffusion MR signal

Haz-Edine Assemlal (1) , David Tschumperlé (2) , Luc Brun (2)

1 GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
2 Equipe Image - Laboratoire GREYC - UMR6072

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.

Mots clés: Gaussian-Laguerre; Diffusion MRI; Variational framework; Rician noise; Spherical harmonics

Domaines: Imagerie médicale

Haz-Edine Assemlal, David Tschumperlé, Luc Brun. Efficient and robust computation of PDF features from diffusion MR signal. Medical Image Analysis, 2009, pp.1-16. ⟨10.1016/j.media.2009.06.004⟩. ⟨hal-00410615⟩