README.md

Contents

Jupyter notebooks for the MR exercises. Recommended order:

Week 1

1. a_fully_sampled Here an introduction in the SIRF MR reconstrution is made. You will read fully sampled raw data, and reconstruct them by sending them to Gadgetron and retrieving the reconstructed images.

2. b_kspace_filter demonstrates how to access and process k-space data prior to reconstruction.

3. c_coil_combination shows different approaches for the computation of receiver coil sensitivities from acquired k-space data, as well as how to combine image data from different receiver coils.

Week 2

1. d_undersampled_reconstructions demonstrates different possibilities to reconstruct undersampled data. You will do a GRAPPA reconstruction using Gadgetron and implement your own conjugate-gradient SENSE parallel image reconstruction using the SIRF MR acquisition model.

2. e_advanced_recon shows how to do iterative SENSE image reconstruction by combining the SIRF MR acuqisition model with with the scipy package for optimisation.

3. f_create_undersampled_kspace demonstrates a retrospective data under-sampling.

Feel free to ignore

Old_notebooks, notebook_setup and tools contain examples scripts of SIRF MR reconstruction used in previous training events. Feel free to ignore.