Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI

2026-10-02

AI Summary

This study addresses the limitation of existing accelerated multi-contrast MRI reconstruction methods, which typically process each contrast independently and fail to fully exploit shared anatomical information across contrasts. To overcome this, we propose MAX, a framework that learns subject-specific anatomical representations via manifold expansion for efficient reconstruction. By employing intensity augmentation to expand the manifold, MAX effectively decouples shared anatomical structures from contrast-dependent components, integrating decoupled implicit neural representations with unrolled optimization techniques. Experimental results demonstrate that MAX achieves state-of-the-art PSNR and SSIM performance in both brain and knee MRI reconstruction, yielding improvements exceeding 1 dB. Furthermore, the proposed method exhibits significant robustness against motion artifacts and noise, highlighting its potential for reliable clinical application.

Abstract

Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization. However, current accelerated MRI methods typically reconstruct each contrast independently, without fully exploiting shared anatomical information. This work aims to learn anatomical representations invariant to contrast-dependent appearance for reconstruction of accelerated multi-contrast MRI. We propose MAX (MAnifold eXpansion), a subject-specific framework that learns anatomical representations from a single fully sampled reference contrast. To address the under-constrained separation of shared anatomy and contrast-dependent components from a single image, MAX expands the multi-contrast manifold using anatomy-preserving intensity augmentations. A disentangled implicit neural representation models augmented samples using shared spatial coordinates for anatomy and spatially invariant coordinates for contrast appearance. The learned anatomical representation is then fixed, with the contrast representation adapted to the undersampled target data, followed by unrolled refinement. Theoretical analyses further provide insight into the disentangled representation learning and explain how the learned anatomical representation improves the target contrast reconstruction. At R = 8 for brain MRI and R = 6 for knee MRI, MAX achieves the highest mean PSNR and SSIM across all tasks, improving PSNR by more than 1 dB over the strongest baseline for both brain contrasts. MAX more faithfully recovers subtle anatomical and pathological structures and remains robust to inter-contrast motion, structural heterogeneity between reference and target contrasts, and measurement noise. Therefore, MAX provides a general strategy for leveraging high-quality reference scans in accelerated MRI and has the potential to be extended to other reference-assisted MRI inverse problems.

Problem

• accelerated MRI
• multi-contrast MRI
• anatomical representation
• image reconstruction

Innovation

• Manifold Expansion
• Disentangled Implicit Neural Representation
• Multi-Contrast MRI
• Accelerated MRI
• Anatomical Representation