Multimodal and multi-sequence integration for deep learning-based 3D MRI reconstruction

Determine robust methods for integrating multiple MRI sequences and modalities (e.g., T1-weighted, T2-weighted, and FLAIR) within deep learning pipelines for 3D anatomical shape reconstruction from 2D MRI, including inter-sequence registration and feature fusion that handle differences in contrast, resolution, and noise to produce coherent, anatomically accurate 3D reconstructions.

Background

The review identifies several persistent barriers to 3D MRI reconstruction with deep learning, including inconsistent voxel spacing, scanner heterogeneity, limited generalizability, and scarce annotated data. Among these, leveraging complementary information across MRI sequences is highlighted as a distinct challenge.

Although sequences such as T1, T2, and FLAIR provide different but complementary tissue contrasts, they often differ in resolution, noise characteristics, and orientation. Aligning these volumes and extracting unified representations suitable for 3D reconstruction remains algorithmically demanding and computationally intensive. Addressing this integration problem is positioned as a key step toward more accurate, generalizable, and clinically robust 3D reconstructions.