用神经场与张量积表示磁共振信号，实现高效动态三维重建。
Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions
- 将磁化和线圈敏感度建模为可微连续函数，避免离散化
- 在16倍加速下仍保持结构与运动细节，优于现有方法
- 适合高维动态3D MRI，尤其心脏成像等低采样场景
传统MRI重建方法将图像和线圈敏感度视为离散对象，导致内存开销大且结构感知能力弱，限制了在高度欠采样情况下的准确重建，如动态3D心脏磁共振（CMR）。本文提出一种无离散化、内存高效的模型基框架，用于从高度欠采样的数据中重建动态2D和3D MRI。通过张量积形式的单变量神经场，将磁化强度和线圈敏感度表示为连续可微函数，该张量积结构使高维时空优化具有可扩展性。所提方法在动态2D和3D MR设置中显著优于当前最优模型基重建方法，在极端欠采样条件下（如加速度因子16）仍能有效保留结构与运动信息。
原文摘要 · Abstract (English)
Conventional MRI reconstruction methods treat images and coil sensitivities as discrete objects, leading to high memory demands and limited structural awareness that hamper effective regularization. These limitations hinder accurate reconstruction in highly undersampled scenarios, such as dynamic 3D cardiac magnetic resonance (CMR). We introduce a discretization-free, memory-efficient, model-based framework for dynamic 2D and 3D MRI reconstruction from highly undersampled data. We represent magnetization and coil sensitivities as continuous objects -- differentiable functions -- using tensor products of univariate neural fields. This tensor product structure enables scalable optimization in high-dimensional spatiotemporal settings. Our method outperforms state-of-the-art model-based reconstructions in dynamic 2D and 3D MR settings, preserving structure and motion even under aggressive undersampling (e.g., acceleration factor 16).
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