Unified Multi-Site Multi-Sequence Brain MRI Harmonization Enriched by Biomedical Semantic Style

• URL: http://arxiv.org/abs/2601.08193v1
• Date: Tue, 13 Jan 2026 03:47:23 GMT
• Title: Unified Multi-Site Multi-Sequence Brain MRI Harmonization Enriched by Biomedical Semantic Style
• Authors: Mengqi Wu, Yongheng Sun, Qianqian Wang, Pew-Thian Yap, Mingxia Liu,
• Abstract summary: We introduce MMH, a unified framework for multi-site multi-sequence brain MRI harmonization.<n>MMH operates in two stages: (1) a diffusion-based global harmonizer that maps MR images to a sequence-specific unified domain using style-agnostic gradient conditioning, and (2) a target-specific fine-tuner that adapts globally aligned images to desired target domains.<n> Evaluations on 4,163 T1- and T2-weighted MRIs demonstrate MMH's superior harmonization over state-of-the-art methods in image feature clustering, voxel-level comparison, tissue segmentation, and downstream age and site
• Score: 13.422551989566983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aggregating multi-site brain MRI data can enhance deep learning model training, but also introduces non-biological heterogeneity caused by site-specific variations (e.g., differences in scanner vendors, acquisition parameters, and imaging protocols) that can undermine generalizability. Recent retrospective MRI harmonization seeks to reduce such site effects by standardizing image style (e.g., intensity, contrast, noise patterns) while preserving anatomical content. However, existing methods often rely on limited paired traveling-subject data or fail to effectively disentangle style from anatomy. Furthermore, most current approaches address only single-sequence harmonization, restricting their use in real-world settings where multi-sequence MRI is routinely acquired. To this end, we introduce MMH, a unified framework for multi-site multi-sequence brain MRI harmonization that leverages biomedical semantic priors for sequence-aware style alignment. MMH operates in two stages: (1) a diffusion-based global harmonizer that maps MR images to a sequence-specific unified domain using style-agnostic gradient conditioning, and (2) a target-specific fine-tuner that adapts globally aligned images to desired target domains. A tri-planar attention BiomedCLIP encoder aggregates multi-view embeddings to characterize volumetric style information, allowing explicit disentanglement of image styles from anatomy without requiring paired data. Evaluations on 4,163 T1- and T2-weighted MRIs demonstrate MMH's superior harmonization over state-of-the-art methods in image feature clustering, voxel-level comparison, tissue segmentation, and downstream age and site classification.

Related papers

• DIST-CLIP: Arbitrary Metadata and Image Guided MRI Harmonization via Disentangled Anatomy-Contrast Representations [0.7201894411169433]
  We propose DIST-CLIP (Disentangled Style Transfer with CLIP Guidance), a unified framework for MRI harmonization.<n>Our framework explicitly disentangles anatomical content from image contrast, with the contrast representations being extracted using pre-trained CLIP encoders.<n>We trained and evaluated DIST-CLIP on diverse real-world clinical datasets, and showed significant improvements in performance.
  arXiv  Detail & Related papers  (2025-12-08T16:09:10Z)
• Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentation [56.52520416420957]
  We propose Multimodal Causal-Driven Representation Learning (MCDRL) to tackle domain generalization in medical image segmentation.<n>MCDRL consistently outperforms competing methods, yielding superior segmentation accuracy and exhibiting robust generalizability.
  arXiv  Detail & Related papers  (2025-08-07T03:41:41Z)
• MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation [4.760537994346813]
  Medical image reporting aims to generate structured clinical descriptions from radiological images.<n>We propose MicarVLMoE, a vision-language mixture-of-experts model with gated cross-aligned fusion.<n>We extend MIR to CT scans, retinal imaging, MRI scans, and gross pathology images, reporting state-of-the-art results.
  arXiv  Detail & Related papers  (2025-04-29T01:26:02Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion [13.563413478006954]
  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.
  arXiv  Detail & Related papers  (2024-08-18T00:13:48Z)
• Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets [6.3467517115551875]
  Masked image modeling (MIM) has shown promise in utilizing unlabeled data.<n>We analyze and address model collapse in two types: complete collapse and dimensional collapse.<n>We construct the enhanced MIM (E-MIM) with HMP and PBT module to avoid model collapse multi-modal MRI.
  arXiv  Detail & Related papers  (2024-07-15T01:11:30Z)
• Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training [99.2891802841936]
  We introduce the Med-ST framework for fine-grained spatial and temporal modeling. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR)
  arXiv  Detail & Related papers  (2024-05-30T03:15:09Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework [20.269574292365107]
  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.
  arXiv  Detail & Related papers  (2024-02-10T03:42:37Z)
• Explainable unsupervised multi-modal image registration using deep networks [2.197364252030876]
  MRI image registration aims to geometrically 'pair' diagnoses from different modalities, time points and slices. In this work, we show that our DL model becomes fully explainable, setting the framework to generalise our approach on further medical imaging data.
  arXiv  Detail & Related papers  (2023-08-03T19:13:48Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.