Section: New Results

Improving Diffusion MRI Signal and Acquisition

Design of multishell sampling schemes with uniform coverage in diffusion MRI

Participants : Emmanuel Caruyer [SBIA, University of Pennsylvania Medical School,USA] , Christophe Lenglet [CMRR, Department of Radiology, University of Minnesota,USA] , Guillermo Sapiro [Electrical & Computer Engineering Dept, Duke University,USA] , Rachid Deriche.

In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques.

The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell.

The impact of our method on the angular resolution in one and two bundles of fiber configurations is evaluated using simulations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination.

This work has been published in [14] .

Motion detection in diffusion MRI via online ODF estimation

Participants : Emmanuel Caruyer [SBIA, University of Pennsylvania Medical School,USA] , Iman Aganj [Martinos Center for Biomedical Imaging, MGH, Harvard Medical School,USA] , Christophe Lenglet [CMRR, Department of Radiology, University of Minnesota,USA] , Guillermo Sapiro [Electrical & Computer Engineering Dept, Duke University,USA] , Rachid Deriche.

The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this work, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2 degrees) with no delay and robustness to noise.

This work has been published in [13] .

A Robust variational approach for simultaneous smoothing and estimation of DTI

Participants : Rachid Deriche, Meizhu Liu [Department of CISE, University of Florida, Gainesville, USA] , Baba C. Vemuri [Department of CISE, University of Florida, Gainesville, USA] .

Estimating diffusion tensors is an essential step in many applications — such as diffusion tensor image (DTI) registration, segmentation and fiber tractography. Most of the methods proposed in the literature for this task are not simultaneously statistically robust and feature preserving techniques. In this work, we propose a novel and robust variational framework for simultaneous smoothing and estimation of diffusion tensors from diffusion MRI. Our variational principle makes use of a recently introduced total Kullback–Leibler (tKL) divergence for DTI regularization. tKL is a statistically robust dissimilarity measure for diffusion tensors, and regularization by using tKL ensures the symmetric positive definiteness of tensors automatically. Further, the regularization is weighted by a non-local factor adapted from the conventional non-local means filters. Finally, for the data fidelity, we use the nonlinear least-squares term derived from the Stejskal–Tanner model. We present experimental results depicting the positive performance of our method in comparison to competing methods on synthetic and real data examples.

This work has been published in [20] .

Tensor estimation and visualization using dMRI

Participants : Dalila Cherifi [University of Boumerdes, Algeria] , Ali Chellouche [University of Boumerdes, Algeria] , Amazigh Ait-Ouakli [University of Boumerdes, Algeria] , Youcef Benamara [University of Boumerdes, Algeria] , Rachid Deriche.

Diffusion tensor imaging in a non-invasive in vivo image modality that allows us to measure molecular diffusion of water in tissues. We characterize diffusion transport of water by an effective diffusion tensor D. The practical importance of the effective diffusion tensor is that it contains new and useful structural and physiological informations about tissues that were previously unobtainable. In this work, we present a software implementation of the estimation of these tensors and their visualization in order to extract these informations.

This work has been published in [28]