git clone --recurse-submodules

build conda env via environments.yml

install pypulseq from dev branch fork using the recurse cloned submodule pip install -e <path_to_jstmc/pypulseq/>

• Default settings are given in options.py for (1mm)³ isotropic voxels with 10 slices in a acquisition, using a 2 fold acceleration (40 central slices).
• Default system is our 7T system. You should! be updating those specs to whatever system you are using.
• The repo should only aid development of the MESE sequence. You should be familiar with pypulseq and take all necessary steps to check if a generated .seq file can be savely transferred to your scanner.
• Per Default a number of plots are generated showing the gradient pulse setup and echo train generation. Slice acquisition scheme and sampling scheme.
• The sampling scheme additionally is output as .csv file.

• Correct setting of measurement headers is dependent on the system you are using. Hence reconstruction of the data could be streamlined. As is, the sequence and the output sampling_scheme.csv file would allow correct sorting of raw data and subsequent choice of custom reconstruction methods.
• At the moment we are using AC-LORAKS reconstruction to deal with the subsampling and subsequently dictionary based matching of the data using the emc approach also found on my git.