A generalized compressed sensing approach to high angular resolution diffusion imaging

Abstract

Among the existing methods of diffusion MRI, high angular resolution diffusion imaging (HARDI) excels in its ability to resolve the complex orientations of crossing and branching neural fibre tracts in the brain. Unfortunately, a widespread integration of HARDI into clinical workflows is still hindered by a few practical obstacles, chief among which relates to prohibitively long scan times required by current implementations of this protocol. In addition, the dependency of HARDI on rapid acquisition schemes, such as single-shot echo planar imaging, imposes limitations on the maximal spatial resolution that one can attain at an acceptable level of signal-to-noise ratio. A possible solution to the problem of limited spatial resolution of HARDI could be to modify the pattern of k-space encoding so as to maximally utilize the bandwidth efficiency of frequency encoding at the expense of using a smaller number of phase encoding steps. At the same time, a substantial reduction in the total acquisition time could be achieved through a subcritical sampling in the q-space. Although both the above mechanisms are bound to yield highly incomplete data, a stable and reliable reconstruction of the associated HARDI signals is still possible to achieve within the framework of compressed sensing. To solve this problem, we introduce an efficient reconstruction procedure, whose effectiveness is demonstrated through both in silico and in vivo experiments.