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Updated: Sep 19, 2025
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
Published on: December 9, 2010
DiffM4RI: A Latent Diffusion Model With Modality Inpainting for Synthesizing Missing Modalities in MRI Analysis
Abstract:
Foundation Models (FMs) have shown great promise for multimodal medical image analysis such as Magnetic Resonance Imaging (MRI). However, certain MRI sequences may be unavailable due to various constraints, such as limited scanning time, patient discomfort, or scanner limitations. The absence of certain modalities can hinder the performance of FMs in clinical applications, making effective missing modality imputation crucial for ensuring their applicability. Previous approaches, including generative adversarial networks (GANs), have been employed to synthesize missing modalities in either a one-to-one or many-to-one manner. However, these methods have limitations, as they require training a new model for different missing scenarios and are prone to mode collapse, generating limited diversity in the synthesized images. To address these challenges, we propose DiffM4RI, a diffusion model for many-to-many missing modality imputation in MRI. DiffM4RI innovatively formulates the missing modality imputation as a modality-level inpainting task, enabling it to handle arbitrary missing modality situations without the need for training multiple networks. Experiments on the BraTs datasets demonstrate DiffM4RI can achieve an average SSIM improvement of 0.15 over MustGAN, 0.1 over SynDiff, and 0.02 over VQ-VAE-2. These results highlight the potential of DiffM4RI in enhancing the reliability of FMs in clinical applications.
Insights
Foundation Models (FMs) for medical imaging face challenges with missing MRI sequences. DiffM4RI, a novel diffusion model, effectively imputes missing modalities in a many-to-many fashion, improving FM reliability.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Multimodal data fusion
Background:
- Foundation Models (FMs) show promise for multimodal medical image analysis, particularly Magnetic Resonance Imaging (MRI).
- Missing MRI sequences due to constraints like scan time or patient comfort can limit FM performance in clinical settings.
- Existing imputation methods, such as Generative Adversarial Networks (GANs), often require scenario-specific training and suffer from limited synthesis diversity.
Purpose of the Study:
- To develop a robust method for imputing missing MRI modalities that can handle arbitrary missing scenarios without retraining.
- To enhance the applicability and reliability of Foundation Models in clinical practice by addressing data incompleteness.
Main Methods:
- Proposed DiffM4RI, a diffusion model designed for many-to-many missing modality imputation in MRI.
- Formulated missing modality imputation as a modality-level inpainting task, allowing for flexible handling of various missing data patterns.
- Evaluated performance on the BraTs datasets.
Main Results:
- DiffM4RI achieved significant improvements in imputation accuracy compared to existing methods.
- Demonstrated an average Structural Similarity Index Measure (SSIM) improvement of 0.15 over MustGAN, 0.1 over SynDiff, and 0.02 over VQ-VAE-2.
- The model's ability to handle diverse missing scenarios without retraining was validated.
Conclusions:
- DiffM4RI offers an effective solution for many-to-many missing modality imputation in MRI.
- The proposed approach enhances the robustness and clinical applicability of Foundation Models by addressing data gaps.
- This work paves the way for more reliable AI-driven medical image analysis in real-world scenarios.
