Diffusion-based unsupervised image registration has been explored for cardiac cine MR, but expensive multi-step inference limits practical use. We propose FlowReg, a flow-matching framework in displacement field space that achieves strong registration in as few as two steps and supports further refinement with more steps. FlowReg uses warmup-reflow training: a single-step network first acts as a teacher, then a student learns to refine from arbitrary intermediate states, removing the need for a pre-trained model as in existing methods. An Initial Guess strategy feeds back the model prediction as the next starting point, improving refinement from step two onward. On ACDC and MM2 across six tasks (including cross-dataset generalization), FlowReg outperforms the state of the art on five tasks (+0.6% mean Dice score on average), with the largest gain in the left ventricle (+1.09%), and reduces LVEF estimation error on all six tasks (-2.58 percentage points), using only 0.7% extra parameters and no segmentation labels. Code is available at https://github.com/mathpluscode/FlowReg.

翻译：基于扩散的无监督图像配准方法已在心脏电影磁共振领域得到探索，但其昂贵的多步推理限制了实际应用。本文提出FlowReg——一种在位移场空间中构建的流匹配框架，该框架仅需两步即可实现高精度配准，并支持通过更多步骤进行进一步优化。FlowReg采用预热-回流训练策略：首先训练单步网络作为教师模型，随后学生模型学习从任意中间状态进行优化，无需如现有方法般依赖预训练模型。通过初始猜测策略将模型预测反馈为下一迭代起点，从第二步开始持续提升优化效果。在ACDC和MM2数据集涵盖的六项任务（包括跨数据集泛化）中，FlowReg在五项任务上超越现有最优方法（平均Dice分数提升0.6%），其中左心室提升幅度最大（+1.09%），并在全部六项任务中降低左心室射血分数估计误差（-2.58个百分点），仅增加0.7%参数量且无需分割标签。代码发布于https://github.com/mathpluscode/FlowReg。