用隐空间运动追踪实现快速动态3D MRI重建，适合临床实时成像。
Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
- 在低维流形上参数化运动场，缩小搜索空间加快在线适配。
- 在即时与延迟2分钟场景下均实现高保真、时序一致的重建结果。
- 适用于需快速运动估计的临床场景，如放射治疗引导成像。
前瞻性重建在许多临床应用中至关重要，例如磁共振引导放疗，需要从当前采集的数据中实现精确的图像重建和快速的运动估计。然而，由于采样极度稀疏且延迟要求严格，前瞻性重建仍具挑战性。本文提出PDMR框架，通过隐空间运动追踪实现前瞻性动态3D MRI重建。核心思想是离线学习一个高效且可泛化的运动场低维隐流形，从而实现快速在线适配。具体而言，我们将在低维流形上参数化变形向量场（DVFs），有效缩小搜索空间以加速在线适配，并采用三平面表示实现几何感知且内存高效的3D运动编码。在XCAT数字模体和自建腹部MRI数据集上的实验表明，PDMR在多种前瞻性场景（即时与延迟2分钟）下均实现了高保真且时序一致的重建，优于最先进的回顾式与在线方法。结果表明，该方法为临床实践中超快速、运动感知的前瞻性MRI重建提供了有前景的路径。
原文摘要 · Abstract (English)
Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fast motion estimation from currently acquired measurements. However, prospective reconstruction remains challenging due to ultra-sparse sampling and stringent latency requirements. In this work, we propose PDMR, a Prospective Dynamic 3D MRI Reconstruction framework with latent-space motion tracking. Our core idea is to learn an efficient and generalizable latent manifold of motion fields offline, enabling rapid online adaptation for prospective reconstruction. Specifically, we parameterize the deformation vector fields (DVFs) on a low-dimensional manifold, effectively reducing the search space for fast online adaptation, and employ a tri-plane representation to achieve geometry-aware and memory-efficient encoding of 3D motion. Experiments on both XCAT digital phantoms and in-house abdominal MRI datasets demonstrate that PDMR achieves high-fidelity and temporally consistent reconstruction across multiple prospective scenarios (Immediate and After-2min), outperforming state-of-the-art retrospective and online methods. Our results suggest a promising pathway toward ultra-fast, motion-aware prospective MRI reconstruction in clinical practice.
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