SPARKLING trajectories for 2D MRI
We work on a new optimization-driven design of optimal k-space trajectories in the context of compressed sensing: Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING).
This work results from a fruitful collaboration with Pierre Weiss (CNRS/ITAV).
Fast Self-calibrating CS-MRI reconstruction in parallel imaging
We develop fast techniques (10 times faster than ESPIRIT) to automatically extract sensitivty maps associated with the multiple receivers of multi-channel coils in the parallel imaging setting. The MR image reconstruction becomes self-calibrating and no longer requires a preliminary scan to collect this information.
Towards online CS-MR image reconstruction
Cartesian acquisition scenarios in MRI are lenghty but image reconstruction is very cheap in terms of computation time (Inverse FFT). Prospective compressed acquisitions speed up scan duration but at the expense of longer image reconstruction process, usually done off-line , i.e. once all data have been collected. Here, we propose to interleave data collection and image reconstruction using dedicated online and time-recursive optimization algorithms that work with part of data. The result is to deliver more quickly the MR image at the scanner console thanks to a Gadgetron implementation.