提出快速收敛的运动补偿重建算法，显著降低多阶段成像计算开销。
Accelerated Convergent Motion Compensated Image Reconstruction
- 采用随机化收敛算法，每轮迭代计算量与门数无关
- 理论收敛速度更快，合成数据上实现预期加速
- 适合需要高速重建的医学影像（如呼吸/心跳运动校正）
运动校正旨在防止由呼吸、心跳或头部移动等引起的运动伪影。在预处理阶段，测量数据被划分为对应于不同运动状态的门，从参考状态到各运动状态估计位移图。常见的运动补偿图像重建框架将位移图整合到对应门数据的前向模型中。标准算法每轮迭代的计算成本随门数线性增长。为加速重建，我们提出一种随机化且收敛的算法，其每轮迭代计算成本恒定，不随门数增加而上升。理论上证明了更快的收敛速率，并在两个合成数据集（分别模拟刚性与非刚性运动）上观察到预期加速效果。
原文摘要 · Abstract (English)
Motion correction aims to prevent motion artefacts which may be caused by respiration, heartbeat, or head movements for example. In a preliminary step, the measured data is divided in gates corresponding to motion states, and displacement maps from a reference state to each motion state are estimated. One common technique to perform motion correction is the motion compensated image reconstruction framework, where the displacement maps are integrated into the forward model corresponding to gated data. For standard algorithms, the computational cost per iteration increases linearly with the number of gates. In order to accelerate the reconstruction, we propose the use of a randomized and convergent algorithm whose per iteration computational cost scales constantly with the number of gates. We show improvement on theoretical rates of convergence and observe the predicted speed-up on two synthetic datasets corresponding to rigid and non-rigid motion.
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