In the context of the IEEE International Symposium on Biomedical Imaging (ISBI 2012) conference which will be held in Barcelona, Spain, from 2 to 5 May 2012, we organize a workshop on high angular resolution diffusion MRI reconstruction techniques. This workshop will be hosted in the workshops and tutorials special session of the main conference.
The anisotropy of diffusion in white matter can be exploited for mapping the structural neuronal connectivity of the brain, and structures invisible with other imaging modalities can be highlighted. The study of this connectivity is of course of major importance in a fundamental neuroscience perspective, for developing our understanding of the brain, but also in a clinical perspective, with particular applications for the understanding of diseases like stroke or schizophrenia. As a consequence, our ability to achieve high angular resolution diffusion magnetic resonance imaging (MRI) represents an important challenge for neuroscience.
The state-of-the-art Diffusion Spectrum Imaging (DSI) modality, which relies on cartesian signal sampling, is known to provide good imaging quality but is significantly too time-consuming to be of real interest in a clinical perspective. Accelerated acquisitions, relying on a smaller number of sampling points, are thus required. In the last few years, an increasing number of techniques have been proposed to recover the fiber directions inside the brain from diffusion MRI data. Some of them aim at reducing the long acquisition times required in diffusion MRI, while others focus on increasing the sensitivity and the angular resolution of the reconstructions.
The goal of the workshop is to gather researchers working in this field around a table to discuss, share thoughts and explore new challenges in the exciting field of diffusion MRI signal modelling and reconstruction. In this aim, we organize a diffusion MRI reconstruction contest inside the workshop itself, hence providing a way for different groups to propose their own algorithms and to fairly compare their methods against the others on a common set of ground-truth data.
