Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion

• URL: http://arxiv.org/abs/2408.09315v1
• Date: Sun, 18 Aug 2024 00:13:48 GMT
• Title: Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion
• Authors: Mengqi Wu, Minhui Yu, Shuaiming Jing, Pew-Thian Yap, Zhengwu Zhang, Mingxia Liu,
• Abstract summary: We propose a novel 3D MRI Harmonization framework through Conditional Latent Diffusion (HCLD) It comprises a generalizable 3D autoencoder that encodes and decodes MRIs through a 4D latent space. HCLD learns the latent distribution and generates harmonized MRIs with anatomical information from source MRIs while conditioned on target image style.
• Score: 13.563413478006954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-site structural MRI is increasingly used in neuroimaging studies to diversify subject cohorts. However, combining MR images acquired from various sites/centers may introduce site-related non-biological variations. Retrospective image harmonization helps address this issue, but current methods usually perform harmonization on pre-extracted hand-crafted radiomic features, limiting downstream applicability. Several image-level approaches focus on 2D slices, disregarding inherent volumetric information, leading to suboptimal outcomes. To this end, we propose a novel 3D MRI Harmonization framework through Conditional Latent Diffusion (HCLD) by explicitly considering image style and brain anatomy. It comprises a generalizable 3D autoencoder that encodes and decodes MRIs through a 4D latent space, and a conditional latent diffusion model that learns the latent distribution and generates harmonized MRIs with anatomical information from source MRIs while conditioned on target image style. This enables efficient volume-level MRI harmonization through latent style translation, without requiring paired images from target and source domains during training. The HCLD is trained and evaluated on 4,158 T1-weighted brain MRIs from three datasets in three tasks, assessing its ability to remove site-related variations while retaining essential biological features. Qualitative and quantitative experiments suggest the effectiveness of HCLD over several state-of-the-arts

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