发表机构
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Harvard Medical School; Massachusetts General Hospital（马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心; 哈佛医学院; 马萨诸塞州总医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MAX框架，通过流形扩展从单个参考对比学习个体特异性解剖表示，用于加速多对比MRI重建，在脑和膝关节数据上显著提升PSNR和SSIM，并保持对噪声和结构差异的鲁棒性。
AI 中文摘要
临床MRI常规采集同一解剖结构的多种对比加权图像，以实现互补的组织表征。然而，当前的加速MRI方法通常独立重建每种对比，未能充分利用共享的解剖信息。本研究旨在学习对对比度依赖外观不变的解剖表示，用于加速多对比MRI的重建。我们提出MAX（流形扩展），一种个体特异性框架，从单个全采样的参考对比中学习解剖表示。为解决从单幅图像中分离共享解剖和对比度依赖组件的欠约束问题，MAX通过保持解剖结构的强度增强来扩展多对比流形。一种解耦的隐式神经表示使用共享的空间坐标建模增强样本的解剖结构，并使用空间不变坐标建模对比外观。学习到的解剖表示随后被固定，对比表示适应于欠采样的目标数据，随后进行展开式细化。理论分析进一步提供了对解耦表示学习的见解，并解释了学习到的解剖表示如何改善目标对比重建。在脑MRI的R=8和膝关节MRI的R=6下，MAX在所有任务中实现了最高的平均PSNR和SSIM，对于两种脑对比，PSNR比最强基线提高了超过1 dB。MAX更忠实地恢复了细微的解剖和病理结构，并且对对比间运动、参考与目标对比之间的结构异质性以及测量噪声保持鲁棒。因此，MAX提供了一种在加速MRI中利用高质量参考扫描的通用策略，并有可能扩展到其他参考辅助的MRI逆问题。
英文摘要
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.
