Disentangled Latent Energy-Based Style Translation: An Image-Level
  Structural MRI Harmonization Framework
Mengqi Wu Lintao Zhang Pew-Thian Yap Hongtu Zhu Mingxia Liu
Brain magnetic resonance imaging (MRI) has been extensively employed across
clinical and research fields, but often exhibits sensitivity to site effects
arising from nonbiological variations such as differences in field strength and
scanner vendors. Numerous retrospective MRI harmonization techniques have
demonstrated encouraging outcomes in reducing the site effects at image level.
However, existing methods generally suffer from high computational requirements
and limited generalizability, restricting their applicability to unseen MRIs.
In this paper, we design a novel disentangled latent energy-based style
translation (DLEST) framework for unpaired image-level MRI harmonization,
consisting of (1) site-invariant image generation (SIG), (2) site-specific
style translation (SST), and (3) site-specific MRI synthesis (SMS).
Specifically, the SIG employs a latent autoencoder to encode MRIs into a
low-dimensional latent space and reconstruct MRIs from latent codes. The SST
utilizes an energy-based model to comprehend the global latent distribution of
a target domain and translate source latent codes toward the target domain,
while SMS enables MRI synthesis with a target-specific style. By disentangling
image generation and style translation in latent space, the DLEST can achieve
efficient style translation. Our model was trained on T1-weighted MRIs from a
public dataset (with 3,984 subjects across 58 acquisition sites/settings) and
validated on an independent dataset (with 9 traveling subjects scanned in 11
sites/settings) in 4 tasks: (1) histogram and clustering comparison, (2) site
classification, (3) brain tissue segmentation, and (4) site-specific MRI
synthesis. Qualitative and quantitative results demonstrate the superiority of
our method over several state-of-the-arts.