Overview of Scanner Invariant RepresentationsDownload PDF

25 Jan 2020 (modified: 27 Jun 2020)MIDL 2020 Conference Blind SubmissionReaders: Everyone
  • Keywords: Harmonization, diffusion MRI, Invariant Representation
  • TL;DR: Scanner-to-scanner harmonization transformations, without paired scans; original paper includes info-theory bounds on remaining bias.
  • Track: short paper
  • Paper Type: methodological development
  • Abstract: Pooled imaging data from multiple sources is subject to bias from each source. Studies that do not correct for these scanner/site biases at best lose statistical power, and at worst leave spurious correlations in their data. Estimation of the bias effects is non-trivial due to the paucity of data with correspondence across sites, so called "traveling phantom" data, which is expensive to collect. Nevertheless, numerous solutions leveraging direct correspondence have been proposed. In contrast to this, Moyer et al. (2019) proposes an unsupervised solution using invariant representations, one which does not require correspondence and thus does not require paired images. By leveraging the data processing inequality, an invariant representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to the underlying structure. In the present abstract we provide an overview of this method.
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