DLRS_CVR_BAT: Deep-learning-enabled-brain-hemodynamic-mapping-using-resting-state-fMRI

This repository contains the code and pre-trained models for our paper Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI

• Overview
• Getting Started
  • Installing
  • Creating data folder
  • Downloading pretrained weights
  • Executing program
• Citation

Overview

Here we purpose a deep-learning network to use resting-state CO2 fluctuation as a natural "contrast media" to map cerebrovascular reactivity (CVR) and bolus arrival time (BAT). Our proposed deep-learning framework is based on the “auto-encoder network” design and includes encoders and decoders. The following figure is an illustration of our network.

Getting Started

Installing

• Create a virtual environment with Python 3.9

conda create -n DLRS_CVR_BAT_env python=3.9
conda activate DLRS_CVR_BAT_env

• Install packages from requirements.txt

pip install -r ./requirements.txt

• Please download SPM12 into ./preprocessing folder.
• Please download SUIT (https://github.com/jdiedrichsen/suit/releases/tag/3.5) into ./processing/SPM12/toolbox.

Creating data folder

This pipeline requires each data folder includes both raw BOLD and MPRAGE images. For the code to run automatically, it's also essential to generate a parameter_RS.txt and slice_order_RS.txt file for each data folder, as demonstrated in the ./template/subject1 folder.

Downloading pretrained weights

You can obtain the pre-trained weights by submitting a request to Hanzhang Lu (hanzhang.lu@jhu.edu). After receiving the weights, they should be placed in the ./model directory.

Executing program

• Preprocess the raw BOLD data using matlab.

Open matlab 
Run ./preprocessing/rs_running.m

• Compute the resting-state CVR and BAT maps based on pretrained model

python ./src/DLRS_CVR_BAT_inference.py

Citation

Please cite our paper if you use DLRS CVR/BAT pipeline in your work:

Hou, X., Guo, P., Wang, P. et al. Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI. npj Digit. Med. 6, 116 (2023). https://doi.org/10.1038/s41746-023-00859-y