Convergence of Non-Cartesian MRI Reconstructions is being accelerated. Preconditioning with k-Space PROJECT TITLE : Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning ABSTRACT: If you want to speed up the convergence of iterative MRI reconstructions from non-uniformly sampled data, you can use this preconditioning concept. Either sampling density compensations or circulant preconditioners are now used to boost per-iteration computing speed. Both of these flaws are addressed by our method. This is demonstrated by employing density-compensation-like techniques to precondition in k-space when we approach the reconstruction problem in the dual formulation With the hybrid gradient approach, the proposed preconditioning method has no inner loops and is competitive with known algorithms for faster convergence. Using experiments, we show that the suggested technique converges in roughly 10 iterations when applied to real-world problems. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Estimating Smoke Density with a Wave-Shaped Deep Neural Network Removing Haze and Noise from a Single Image Using Accurate Transmission Estimation
