MRI collects samples in the Fourier domain, called as the k-space. The k-space is traversed along continuous trajectories using varying magnetic gradients. Limited by hardware and physiological factors such as gradient magnitude, gradient slew rate and MRI signal decay, long scan times are required to traverse the complete k-space. Scan time reduction in MRI is important to improve patient comfort, reduce image artifacts related to motion, improve dynamic imaging. With the development of the theory of compressed sensing, it is possible to reconstruct the MRI image with an undersampled k-space data. The objective is to design short and feasible trajectories such that they satisfy the gradient magnitude and slew rate constraints. A generalized framework based on projection of infeasible trajectories onto the set of feasible trajectories is developed. Multiple methods are explored under the generalized framework. The proposed methods result in shorter read-out times and/or better reconstruction performances as compared to the state-of-the-art methods.
This is joint work with Prof. Geert Leus at TU-Delft, The Netherlands.
S. Sharma, M. Coutino, S. P. Chepuri, G. Leus, and K. Hari, “Towards a general framework for fast and feasible k-space trajectories for MRI based on projection methods,” Magnetic Resonance Imaging, vol. 72, pp. 122–134, 2020.