  Brain magnetic resonance imaging (MRI) has been extensively employed across
clinical and research fields, but often exhibits sensitivity to site effects
arising from non-biological 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 the 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 (a) site-invariant image generation (SIG), (b) site-specific
style translation (SST), and (c) 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 four tasks: histogram and feature visualization, site
classification, brain tissue segmentation, and site-specific structural MRI
synthesis. Qualitative and quantitative results demonstrate the superiority of
our method over several state-of-the-arts.
