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.
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.
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:
- 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.
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
Additional relevant papers:
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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
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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
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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
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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
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 ###