BlindHarmony

Official implementation of BlindHarmony: Blind harmonization for MR image

H. Jeong, H. Byun, D. Kang, and J. Lee, BlindHarmony: “Blind” Harmonization for MR Images via Flow model, ICCV 2023, [arXiv]

Dependencies

Neural spline flow is used for flow model training. See https://github.com/bayesiains/nsf.git

Use environment.yml for required packages, or create a Conda environment with all dependencies:

conda env create -f environment.yml

Dataset

The whole data of OASIS-3 can be accessed by https://www.oasis-brains.org.

Pretrained models

The checkpoints can be downloaded from google drive link in https://drive.google.com/drive/folders/1AuCYGiNOZ8hWrqiV_npsjmcodNVfRb6z?usp=share_link

Usage

DATAROOT environment variable needs to be set before running experiments.

Flow model training

Use train_flow.py.

Harmonization using simulation data

Use BlindHarmony_simulated_data.py.

Harmonization using simulation data

Use BlindHarmony_real_data.py.