CALAMITI

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Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI)

Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI) is our current MR harmonization method. It was designed to achieve unsupervised multi-site MR harmonization. The associated publication is:

• L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince, "Information-based Disentangled Representation Learning for Unsupervised MR Harmonization", 27^th Conference on Information Processing in Medical Imaging (IPMI 2021), Virtually in Bornholm, Denmark, June 28 - July 2, 2021.

Software

CALAMITI code (2D)

220k

Instructions

CALAMITI requires paired multi-contrast MR images~(e.g., T1-w and T2-w) during training. The ideal structure of the data directory and naming convention are as follows:

absolute_path_to_data

   ├──train
    |   └──subject_*
        |         ├── H.png
        |         ├── Z.png
        |         └── M.png
        └──val
            └──subject_*
                  ├── H.png
                  ├── Z.png
                  └── M.png

If you have questions regarding the method or software, please email Lianrui Zuo at lr_zuo@jhu.edu