CEST/MT MR-Fingerprinting

The purpose of this code is to demonstrate the CEST/MT-MRF parameter quantification pipeline.

The folder contains data ('acquired_data.mat'), obtained at 9.4T, where 3 vials of 50 mM L-arginine at pH 4, 4.5, and 5 were scanned using a CEST-MRF protocol of 30 iterations.

The code contains two main parts:

A. MATLAB part

run the file: run_demo.m

The code will guide you through the different steps to:

(1) Generate a CEST-MRF dictionary.

External packages (pulseq, yamlmatlab) will be automatically installed.
Parallel computation is performed while using the open Pulseq standard: https://pulseq-cest.github.io/
For more info, see: Herz, K, Mueller, S, Perlman, O, et al. Pulseq-CEST: Towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard. Magn Reson Med.; https://doi.org/10.1002/mrm.28825

(2) Perform dot-product matching.

(3) Install the packages for deep reconstruction.

B. Python part:

The deep reconstruction is performed using Python code. It requires having python installed with the following packages: numpy, scipy, matplotlib, and torch. Suggested installation routes:

pip (https://pip.pypa.io/en/stable/)
Anaconda (https://www.anaconda.com/products/individual-d)

a YAML file that allows creating the relevant environment is available in this folder: 'conda_environment.yml'

Docker (https://www.docker.com/).

A docker-image with the required packages can be obtained by:

'docker pull operlman/pytroch_scipy_matplotlib_scikit-image'

Once the packages are installed, run deep_reco.py

The script will use the file dict.mat, generated in the previous steps, as well as the file acquired_data.mat, available in this folder.

This repository is associated with the review paper:

Perlman, O, Farrar, CT, Heo, H-Y. MR Fingerprinting for Semisolid Magnetization Transfer and Chemical Exchange Saturation Transfer Quantification. NMR in Biomedicine. 2022;. e4710 https://doi.org/10.1002/nbm.4710

If you use this code in a scientific publication please cite the above reference.

Additional relevant papers:

Perlman, O, Herz, K, Zaiss, M, Cohen, O, Rosen, MS, Farrar, CT. CEST MR-Fingerprinting: Practical considerations and insights for acquisition schedule design and improved reconstruction. Magn Reson Med. 2020; 83: 462– 478. https://doi.org/10.1002/mrm.27937
Herz, K, Mueller, S, Perlman, O, et al. Pulseq-CEST: Towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard. Magn Reson Med. 2021; 86: 1845– 1858. https://doi.org/10.1002/mrm.28825
Perlman, O., Ito, H., Herz, K., Shono, N., Nakashima, H., Zaiss, M., Chiocca, E.A., Cohen, O., Rosen, M.S. and Farrar, C.T., 2021. Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nature Biomedical Engineering, pp.1-10. https://doi.org/10.1038/s41551-021-00809-7
Cohen, O, Huang, S, McMahon, MT, Rosen, MS, Farrar, CT. Rapid and quantitative chemical exchange saturation transfer (CEST) imaging with magnetic resonance fingerprinting (MRF). Magn Reson Med. 2018; 80: 2449– 2463. https://doi.org/10.1002/mrm.27221

Update - May 10, 2022

The Semisolid-MT/CEST MRF Demo was presented at the ISMRM 2022 Cellular and Molecular Study group meeting:

The interactive demo presented is now available in this repository as: ### CESTmtMRF_ISMRM2022.zip ###

operlman/cest-mrf