Code for reproducing paper "MR Perfusion Source Mapping Depicts Venous Territories and Reveals Perfusion Modulation during Neural Activation"
The cerebral venous system plays a crucial role in neurological and vascular conditions, yet its hemodynamics remain underexplored due to its complexity and variability across individuals. To address this, we develop a venous perfusion source mapping method using Displacement Spectrum MRI, a non-contrast technique that leverages blood water as an endogenous tracer. Our technique encodes spatial information into the magnetization of blood water spins during tagging and detects it once the tagged blood reaches the brain's surface, where the signal-to-noise ratio is 3-4× higher. We resolve the sources of blood entering the imaging slice across short (10ms) to long (3s) evolution times, effectively capturing perfusion sources in reverse. This approach enables measurement of slow venous blood flow, including potential contributions from capillary beds and surrounding tissue. Here, we demonstrate perfusion source mapping in the superior cerebral veins, verify its sensitivity to global perfusion modulation induced by caffeine, and establish its specificity by showing repeatable local perfusion modulation during neural activation. From all blood within the imaging slice, our method localizes the portion originating from an activated region upstream.
To run this notebook in Google Colab, click on this link. After you've opened the notebook, run all the cells. This notebook reproduces the figures and processing from the manuscript.
Note: Due to RAM limitations in Google Colab, we have omitted some of the preprocessing steps. If you would like to run the full preprocessing pipeline, please refer to the Jupyter notebook provided in Method 2.
Installation: Install the required python packages (tested with python 3.6.9 on Ubuntu 18.04 LTS):
pip install -r requirements.txtDownload Dataset: The dataset can be downloaded from Zenodo using the following command:
wget https://zenodo.org/record/15041913/files/dataset.zipAfter downloading the dataset, you must unzip the file. Ensure that the extracted dataset folder is placed in the same location as the Jupyter notebook. Additionally, download the libs.py file from GitHub, which contains essential functions, and place it in the same location as the Jupyter notebook.
Run Notebook: Finally, you can open the notebook Reproduce Figures.ipynb and run all cells. This notebook reproduces the figures and processing from the manuscript.