The paper proposes FireANTs, a multi-scale Adaptive Riemannian Optimization algorithm for dense diffeomorphic image matching. Existing state-of-the-art methods for diffeomorphic image matching are slow due to inefficient implementations and slow convergence due to the ill-conditioned nature of the optimization problem. Deep learning methods offer fast inference but require extensive training time, substantial inference memory, and fail to generalize across long-tailed distributions or diverse image modalities, necessitating costly retraining. We address these challenges by proposing a training-free, GPU-accelerated multi-scale Adaptive Riemannian Optimization algorithm for fast and accurate dense diffeomorphic image matching. FireANTs runs about 2.5x faster than ANTs on a CPU, and upto 1200x faster on a GPU. On a single GPU, FireANTs performs competitively with deep learning methods on inference runtime while consuming upto 10x less memory. FireANTs shows remarkable robustness to a wide variety of matching problems across modalities, species, and organs without any domain-specific training or tuning. Our framework allows hyperparameter grid search studies with significantly less resources and time compared to traditional and deep learning registration algorithms alike.

翻译：本文提出FireANTs，一种用于稠密微分同胚图像匹配的多尺度自适应黎曼优化算法。现有的微分同胚图像匹配方法因实现效率低下以及优化问题本身的病态性导致收敛缓慢，限制了其速度。深度学习方法虽能实现快速推理，但需要大量训练时间与显存，且难以泛化至长尾分布或多样化的图像模态，往往需要昂贵的重新训练。为应对这些挑战，我们提出一种无需训练、基于GPU加速的多尺度自适应黎曼优化算法，以实现快速而精确的稠密微分同胚图像匹配。FireANTs在CPU上的运行速度约为ANTs的2.5倍，在GPU上最高可达1200倍。在单GPU上，FireANTs在推理运行时与深度学习方法表现相当，同时显存消耗最多可降低10倍。FireANTs展现出对跨模态、跨物种、跨器官的多种匹配问题的卓越鲁棒性，且无需任何领域特定的训练或调参。相较于传统配准算法与深度学习配准算法，我们的框架能够以显著更少的资源与时间进行超参数网格搜索研究。