Reconstruction of HARDI using compressed sensing and its application to contrast HARDI

Abstract

High angular resolution diffusion imaging (HARDI) is known to excel in delineating multiple diffusion flows through a given location within the white matter of the brain. Unfortunately, many current methods of implementation of HARDI require collecting a relatively large number of diffusion-encoded images, which is in turn translated in prohibitively long acquisition times. As a possible solution to this problem, one can undersample HARDI data by using fewer diffusion-encoding gradients than it is prescribed by the classical sampling theory, while exploiting the tools of compressed sensing (CS). Accordingly, the goal of the present paper is twofold. First, the paper presents a novel CS-based framework for the reconstruction of HARDI data using a reduced set of diffusion-encoding gradients. As opposed to similar studies reported in the literature, the proposed method has been optimized for the Rician statistics of measurement noises, which are known to be prevalent in HARDI, and in fact, in MRI in general. Second, we introduce the concept of rotational invariant Fourier signatures (RIFS), and show how they can be used to generate a composite HARDI contrast, which we refer to as colour-HARDI (cHARDI). Finally, via a series of experiments with both simulated and in vivo MRI data, we demonstrate that the quality and informativeness of the proposed contrast deteriorates little, when used in conjunction with the proposed CS-based reconstruction framework. Thus the present work proposes a way to improve the time efficiency of HARDI, and shows its application to the computation of a new HARDI-based contrast which has a potential to improve the clinical value of this important imaging modality.