用3D高斯表示动态MRI，实现亚秒级重建与实时运动追踪
Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)
- 用3D高斯表示解剖结构和低秩运动模型，无需先验知识
- 实现400ms时间分辨率，单帧推理仅需10ms，支持实时成像
- 适用于放疗中患者运动追踪，对未知运动模式有强鲁棒性
快速动态三维MRI重建对于自适应放疗至关重要。本文提出基于时空高斯表示的DREME-GSMR框架，无需任何先验解剖或运动模型，即可从治疗前的3D MRI重建时间分辨动态影像。该方法将参考MRI体积和低秩运动模型（作为运动基底成分）用3D高斯表示，并引入双路径MLP/CNN运动编码器，从原始k空间信号中估计运动模型的时间系数。此外，利用求解出的运动模型，DREME-GSMR可直接从新采集的在线k空间数据推断运动系数，实现治疗过程中的三维MRI重建与运动追踪（实时成像）。进一步引入运动增强策略，提升对未知运动模式的鲁棒性。在XCAT数字体模、物理运动体模及6名健康志愿者与20名患者（含独立序列交叉评估）的MR-LINAC数据集上进行评估。DREME-GSMR实现约400ms时间分辨率，单帧推理时间约为10ms/体积。在XCAT实验中，动态重建/实时成像的均值SSIM、肿瘤质心误差(COME)、Dice相似系数(DSC)分别为0.92(0.01)/0.91(0.02)、0.50(0.15)/0.65(0.19)mm、0.92(0.02)/0.92(0.03)；物理体模下目标质心误差为1.19(0.94)/1.40(1.15)mm；健康志愿者与患者实时成像肝质心误差分别为1.31(0.82)mm与0.96(0.64)mm。
原文摘要 · Abstract (English)
Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiotherapy. Representing patient anatomy and associated motion fields as 3D Gaussians, we developed a spatiotemporal Gaussian representation-based framework (DREME-GSMR), which enables time-resolved dynamic MRI reconstruction from a pre-treatment 3D MR scan without any prior anatomical/motion model. DREME-GSMR represents a reference MRI volume and a corresponding low-rank motion model (as motion-basis components) using 3D Gaussians, and incorporates a dual-path MLP/CNN motion encoder to estimate temporal motion coefficients of the motion model from raw k-space-derived signals. Furthermore, using the solved motion model, DREME-GSMR can infer motion coefficients directly from new online k-space data, allowing subsequent intra-treatment volumetric MR imaging and motion tracking (real-time imaging). A motion-augmentation strategy is further introduced to improve robustness to unseen motion patterns during real-time imaging. DREME-GSMR was evaluated on the XCAT digital phantom, a physical motion phantom, and MR-LINAC datasets acquired from 6 healthy volunteers and 20 patients (with independent sequential scans for cross-evaluation). DREME-GSMR reconstructs MRIs of a ~400ms temporal resolution, with an inference time of ~10ms/volume. In XCAT experiments, DREME-GSMR achieved mean(s.d.) SSIM, tumor center-of-mass-error(COME), and DSC of 0.92(0.01)/0.91(0.02), 0.50(0.15)/0.65(0.19) mm, and 0.92(0.02)/0.92(0.03) for dynamic reconstruction/real-time imaging. For the physical phantom, the mean target COME was 1.19(0.94)/1.40(1.15) mm for dynamic/real-time imaging, while for volunteers and patients, the mean liver COME for real-time imaging was 1.31(0.82) and 0.96(0.64) mm, respectively.
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