AHEAD

This project folder contains the configuration files, preprocessing, and visualization scripts for the Amsterdam Ultra-high field adult lifespan database (AHEAD) dataset.

Data were scanned using the MP2RAGEME sequence for T1, T2* and Quantitative Susceptibility Mapping in one sequence at 7 Tesla. Data are motion-corrected using Fat navigators (FatNavs), and defaced in image-domain. In total 77 subjects are included, scanned with a resolution of 0.7mm isotropic. Data of the MP2RAGEME-sequence are stored according to the ISMRMRD-standard in h5-format (https://ismrmrd.github.io/). Detailed scanner parameters are included in the h5-files of all subjects. Coil sensitivity maps per subjects are included in native h5-format. Demographics of all subjects are included in a separate csv-file, being sex and age decade, covering the life span.

For more information and dataset download link for the AHEAD project, please check https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/IHZGQM.

Note

When running the preprocessing scripts please make sure you have the ismrmrd package installed. You can install it with the following command:

pip install -r requirements/requirements-ahead_stanfordknees.txt

Visualization An example notebook for visualizing and preprocessing the data is provided in the visualize. You just need to set the path where the dataset is downloaded.

Preprocessing The preprocessing pipeline is implemented in the batch_preprocessing.sh script, consisting of the following steps: 1. Read the raw data in ISMRMRD format. 2. Preprocess the coil sensitivity maps. 3. Compute the imspace and ground-truth target data. 4. Compute the masks. 5. Compute the quantitative maps. 6. Store the data in HDF5 format.

The preprocessing script can be run with the following command:

bash projects/qMRI/AHEAD/batch_preprocessing.sh

Training/Testing For training a model, you just need to set up the data and export paths to the configuration file of the model you want to train. In train_ds and validation_ds please set the data_path to the generated json files. In exp_manager please set the exp_dir to the path where you want to save the model checkpoints and tensorboard or wandb logs.

atommic run -c /projects/qMRI/AHEAD/conf/train/{model}.yaml`

For testing a model, you just need to set up the data and export paths to the configuration file of the model you want to test. In checkpoint (line 2) set the path the trained model checkpoint and in test_ds please set the data_path. In exp_manager please set the exp_dir to the path where the predictions and logs will be saved.

You can test a model with the following command:

atommic run -c /projects/qMRI/AHEAD/conf/test/{model}.yaml`

Note: The default logger is tensorboard.