This repo contains scripts to reproduce some experiments in Extreme MRI. It uses the Python package sigpy.
To start, we recommend trying the demo notebooks on Google Colab:
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DCE:
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Lung:
Example DCE and lung datasets can be found on Zenodo:
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DCE:
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Lung:
For more description about how the data was acquired, please see the manusciprt.
For description about the variable names, please see the Variables section below.
Install sigpy using pip:
pip install sigpy
GPU is recommended for running extreme MRI reconstruction. To do so, you will need to install cupy, a numpy-like package for CUDA, either through conda or pip:
pip install cupy
ksp: kspace data array of shape [# of channels, # of TRs, readout lengths].coord: kspace coordinate array of shape [# of TRs, readout lengths, # of dimensions].dcf: density compensation factor of shape [# of TRs, readout lengths].mps: sensitivity maps of shape [# of channels, nx, ny, nz]img: reconstructed image of shape [# of frames, nx, ny, nz]
To run the reconstruction, the general pipeline is to:
- run setup script to the folder containing numpy arrays (
ksp.npy,coord.npy,dcf.npy). - automatically select FOV to account for leakage from slab selection.
- perform gridding reconstruction to look at image.
- estimate sensitivity maps using JSENSE.
- running the low rank reconstruction.
Auto FOV and estimate sensitivity maps
source setup.sh path/to/folder/
python autofov.py $ksp $coord $dcf --device 0
python jsense_recon.py $ksp $coord $dcf $mps --device 0
Estimate respiratory signal and soft-gating weights with TR of 7.7 ms
python estimate_resp.py $ksp 0.0077 $resp
python soft_gating_weights.py $resp $sgw
Low rank reconstruction with 500 frames
python multi_scale_low_rank_recon.py $ksp $coord $dcf $mps 500 $img --device 0
Low rank reconstruction with 20 frames and soft-gating weights
python multi_scale_low_rank_recon.py $ksp $coord $dcf $mps 20 $img --device 0 --sgw_file $sgw
Motion resolved reconstruction with 5 bin
python motion_resolved_recon.py $ksp $coord $dcf $mps $resp 5 $mrimg --device 0