SNR-Efficient Whole-Brain Pseudo-Continuous Arterial Spin Labeling Perfusion Imaging at 7 Tesla

# Readme This repository contains the MATLAB code and simulation data that was used in the paper titled "SNR-Efficient Whole-Brain Pseudo-Continuous Arterial Spin Labeling Perfusion Imaging at 7 Tesla" by Joseph G. Woods, Yang Ji, Hongwei Li, Aaron T. Hess, and Thomas W. Okell and published in Magnetic Resonance in Medicine in 2025. The code and data can be used to recreate the following simulation figures: * Figure 3 * Figure 7 * Figure S1 * Figure S2 * Figure S3 * Figure S5 * Figure S8 Privacy policies currently prevent us from freely sharing the in vivo data. ## How to * First run the setup.m to add the relevant folders to the MATLAB path. * The code used to optimize the PCASL parameters for max SNR and max labeling efficiency is in __Analyse_PCASL_optimization_data.m__. * This loads in the pre-generated data from the .mat file PCASLLE_PCASLtypebalanced_RFtypeHann_bVERSE1_RFdutycycle0.5_Gmax_staticLD1800ms_df50Hz.mat which was simulated using the bash script in runOptimisation_group.sh using a high performance cluster. * The code used to optimize the background suppression pulse waveforms is in __BGS_optimization.m__. * The code to generate the individual figures from the manuscript can be found in the folder "Code_for_figures" ## Citations If you use any of this code or data in your own work, please cite the above paper and this repository. ## Notes Activating MATLAB's parpool will greatly reduce the run time of some of the scripts because parfor loops are used. This code has only been tested in the Native Apple silicon version of MATLAB 2024a on a Apple MacBook Pro with an M1 chip.