 Pooled imaging data from multiple sources is subject to variation between the sources. Correcting for these biases has become incredibly important as the size of imaging studies increases and the multi-site case becomes more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to the underlying structure . To implement this, we use a machine learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data.  To evaluate our method, we use training data from the 2018 CDMRI Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method  .
Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures . To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data .
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Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods:
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is
are
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incredibly important as the size of imaging studies increases and the
very important as imaging studies increase in size and
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case becomes
cases become
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the underlying structure
underlying structures
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machine
deep
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Results:
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CDMRI
MICCAI Computational Diffusion MRI (CDMRI)
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on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data