Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework

• URL: http://arxiv.org/abs/2402.06875v2
• Date: Thu, 30 May 2024 01:34:02 GMT
• Title: Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework
• Authors: Mengqi Wu, Lintao Zhang, Pew-Thian Yap, Hongtu Zhu, Mingxia Liu,
• Abstract summary: We develop a novel framework for unpaired image-level MRI harmonization. It consists of (a) site-invariant image generation ( SIG), (b) site-specific style translation (SST), and (c) site-specific MRI synthesis (SMS) By disentangling image generation and style translation in latent space, the DLEST can achieve efficient style translation.
• Score: 20.269574292365107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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