Skip to content

Latest commit

 

History

History
135 lines (95 loc) · 4.27 KB

README.rst

File metadata and controls

135 lines (95 loc) · 4.27 KB
Raw

This repository can be used to reconstruct MR Fingerprinting data and perform single and multi-component matching.

This code heavily relies on SigPy, so many thanks to the developers of this great package!

MrfData

MRFrecon\Backend.py contains a python class MrfData to reconstruct MRF data and calculate multi-component maps using joint sparsity [1] resulting in a small number of tissues over the complete region of interest.

The class can:

  • reconstruct MRF image sequence from kspace data using:
    • Filtered back projection
    • Low rank Filtered back projection comparable to [2]
    • Parallel image reconstruction
    • Low rank inversion image reconstruction
    • Low rank ADMM image reconstruction
  • calculate quantitative data
    • Single component matching
    • SPIJN (joint sparse components from an image sequence)
    • Direct joint sparse components from kspace

Installation

The package requires several packages

  • Numpy
  • Sigpy
  • Matplotlib
  • h5py
  • Scipy
  • Cupy (optional)
  • Nibabel (optional)

environment.yml contains these packages.

Usage

Load the data class from the module

from MRFrecon import MrfData

Create an empty instance of the data class and add the raw mri data

data = MrfData()
data.ksp = ksp
data.coord = coord
data.dictmat = dictmat
data.dictt1 = dictt1
data.dictt2 = dictt2

# equivalently
data = MrfData(ksp, coord, dictmat, dictt1, dictt2)

Note the structure of the variables:

  • ksp: numpy array of shape (#slices, #coils, #dynamics, #spirals_per_image, #point_on_spiral)
  • coord: numpy array of shape (#dynamics, #spirals_per_image, #point_on_spiral, #spiral_coordinates)
  • dictmat: numpy array of shape (#atoms, #dynamics)
  • t1list: list of T_1 values of shape (#atoms)
  • t2list: list of T_2 values of shape (#atoms)

With the data initialized, the class can solve for the image sequence and components. The following code solves for the joint sparse components after low rank inversion image reconstruction.

# compress dictionary
data.compress_dictionary(10)

# Espirit calc for sensitivity maps
data.mrf_espirit(256)

# compress coils
data.coil_compression(5)

# Low rank inversion image reconstruction
data.lr_inversion()

# rotate image sequence to real axis
data.rotate2real()

# Solve for components
data.spijn_solve(0.25)

# Save to .h5
data.to_h5('./')

A working version of this is contained in example.py. First download the data from location as specified in example_data/readme.txt.

Usage with config.ini files

To smoothen this procedure and have more reusable methods or perform reconstruction with different settings, a configparser has been used to use different config files (see example data) together with main_from_config_files.py this allows to run the reconstruction from the terminal with predefined reconstruction steps as contained in mrf_recon.py. This is especially usefull on a cluster with a slurm job manager and the array command.

References

[1]Nagtegaal, M, Koken, P, Amthor, T, et al. Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting. Magn Reson Med. 2020; 83: 521– 534. https://doi.org/10.1002/mrm.27947
[2]Assländer, J., Cloos, M.A., Knoll, F., Sodickson, D.K., Hennig, J. and Lattanzi, R. (2018), Low rank alternating direction method of multipliers reconstruction for MR fingerprinting. Magn. Reson. Med., 79: 83-96. https://doi.org/10.1002/mrm.26639

Contact

(c) Emiel Hartsema, July 2021

(c) Martijn Nagtegaal, March 2022

Technical University of Delft, Faculty of Applied Sciences

[email protected]

[email protected]

Imphys

TUDelft