  The lack of standardization is a prominent issue in magnetic resonance (MR)
imaging. This often causes undesired contrast variations in the acquired images
due to differences in hardware and acquisition parameters. In recent years,
image synthesis-based MR harmonization with disentanglement has been proposed
to compensate for the undesired contrast variations. Despite the success of
existing methods, we argue that three major improvements can be made. First,
most existing methods are built upon the assumption that multi-contrast MR
images of the same subject share the same anatomy. This assumption is
questionable, since different MR contrasts are specialized to highlight
different anatomical features. Second, these methods often require a fixed set
of MR contrasts for training (e.g., both T1-weighted and T2-weighted images),
limiting their applicability. Lastly, existing methods are generally sensitive
to imaging artifacts. In this paper, we present Harmonization with
Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel
approach to address these three issues. HACA3 incorporates an anatomy fusion
module that accounts for the inherent anatomical differences between MR
contrasts. Furthermore, HACA3 is also robust to imaging artifacts and can be
trained and applied to any set of MR contrasts. HACA3 is developed and
evaluated on diverse MR datasets acquired from 21 sites with varying field
strengths, scanner platforms, and acquisition protocols. Experiments show that
HACA3 achieves state-of-the-art performance under multiple image quality
metrics. We also demonstrate the applicability and versatility of HACA3 on
downstream tasks including white matter lesion segmentation and longitudinal
volumetric analyses.
