FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching

• URL: http://arxiv.org/abs/2404.01249v4
• Date: Sun, 19 Oct 2025 16:38:01 GMT
• Title: FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching
• Authors: Rohit Jena, Pratik Chaudhari, James C. Gee,
• Abstract summary: Existing state-of-the-art methods for diffeomorphic image matching are slow due to inefficient implementations and slow convergence.<n>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.<n>FireANTs runs about 2.5x faster than ANTs on a CPU, and upto 1200x faster on a GPU.
• Score: 20.35907245543535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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