Learning Multi-Site Harmonization of Magnetic Resonance Images Without Traveling Human Phantoms. (arXiv:2110.00041v1 [eess.IV])

Harmonization improves data consistency and is central to effective
integration of diverse imaging data acquired across multiple sites. Recent deep
learning techniques for harmonization are predominantly supervised in nature
and hence require imaging data of the same human subjects to be acquired at
multiple sites. Data collection as such requires the human subjects to travel
across sites and is hence challenging, costly, and impractical, more so when
sufficient sample size is needed for reliable network training. Here we show
how harmonization can be achieved with a deep neural network that does not rely
on traveling human phantom data. Our method disentangles site-specific
appearance information and site-invariant anatomical information from images
acquired at multiple sites and then employs the disentangled information to
generate the image of each subject for any target site. We demonstrate with
more than 6,000 multi-site T1- and T2-weighted images that our method is
remarkably effective in generating images with realistic site-specific
appearances without altering anatomical details. Our method allows
retrospective harmonization of data in a wide range of existing modern
large-scale imaging studies, conducted via different scanners and protocols,
without additional data collection.