Modality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI
Summary: arXiv:2604.05171v1 Announce Type: cross
Abstract
Learning a robust Variational Autoencoder (VAE) is a fundamental step for many deep learning applications in medical image analysis, such as MRI synthesis. Existing brain VAEs predominantly focus on single-modality data (i.e., T1-weighted MRI), overlooking the complementary diagnostic value of other modalities like T2-weighted MRIs. Here, we propose a modality-aware and anatomically grounded 3D vector-quantized VAE (VQ-VAE) for reconstructing multi-modal brain MRIs.
Introduction
NeuroQuant is a novel approach that first learns a shared latent representation across modalities using factorized multi-axis attention, effectively capturing relationships between distant brain regions. This innovative framework significantly enhances the capabilities of VAEs in medical imaging.
Methodology
The NeuroQuant model employs a dual-stream 3D encoder, which explicitly separates the encoding of modality-invariant anatomical structures from modality-dependent appearance features. This dual-stream approach is crucial for accurately reconstructing brain MRIs from different modes.
Key Features of NeuroQuant
- Modality-Aware Representation: Utilizing factorized multi-axis attention, NeuroQuant learns to discern important features across varying modalities.
- Dual-Stream Encoder: This design allows for the distinct encoding of anatomical structures and appearance features, enhancing reconstruction fidelity.
- Anatomical Encoding: The anatomical encoding is discretized using a shared codebook, promoting a unified representation across modalities.
- Feature-wise Linear Modulation (FiLM): During the decoding phase, modality-specific features are integrated with anatomical encodings, allowing for nuanced reconstruction.
- Joint Training Strategy: The model is trained using a joint 2D/3D strategy to effectively handle the slice-based acquisition of 3D MRI data.
Results
Extensive experiments conducted on two multi-modal brain MRI datasets reveal that NeuroQuant achieves superior reconstruction fidelity compared to existing VAEs. The results indicate significant improvements in both visual quality and diagnostic potential of the generated images.
Conclusion
NeuroQuant represents a significant advancement in the field of medical image analysis, particularly in the synthesis of multi-modal brain MRIs. By effectively leveraging the complementary diagnostic information from different modalities, this approach provides a scalable foundation for downstream generative modeling and cross-modal brain image analysis.
Future Directions
Future work will focus on enhancing the model’s performance further and exploring its application in various clinical scenarios. The integration of additional modalities and the refinement of the training strategies will also be considered to improve the robustness and utility of NeuroQuant in real-world applications.