测试时间优化的通用人工智能医学图像配准方法
Test Time Optimized Generalized AI-based Medical Image Registration Method
AI总结：
本文提出了一种通用人工智能医学图像配准方法，能够跨多种成像模态和解剖区域进行高效配准，无需特定定制，提升临床应用的通用性和效率。
摘要：
医学图像配准对于在不同成像模态（如计算机断层扫描（CT）、磁共振成像（MRI）和超声）之间对齐解剖结构至关重要。在现有技术中，非刚性配准（NRR）尤其具有挑战性，因为需要捕捉由生理过程如呼吸或对比剂引起的信号变化导致的复杂解剖变形。传统NRR方法虽然在理论上是稳健的，但往往需要大量的参数调整，并且计算成本高，限制了它们在实时临床工作流程中的使用。最近的深度学习（DL）方法显示出潜力；然而，它们对任务特定的重新训练的依赖限制了在实践中可扩展性和适应性。这些限制凸显了需要高效且具有通用性的配准框架，能够处理异质成像环境。在本工作中，我们介绍了一种新的AI驱动的3D非刚性配准框架，该框架能够跨多种成像模态和解剖区域进行通用化。与传统方法依赖于应用特定模型不同，我们的方法消除了解剖或模态特定的定制，使该方法能够无缝集成到各种临床环境中。
英文摘要：
Medical image registration is critical for aligning anatomical structures across imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. Among existing techniques, non-rigid registration (NRR) is particularly challenging due to the need to capture complex anatomical deformations caused by physiological processes like respiration or contrast-induced signal variations. Traditional NRR methods, while theoretically robust, often require extensive parameter tuning and incur high computational costs, limiting their use in real-time clinical workflows. Recent deep learning (DL)-based approaches have shown promise; however, their dependence on task-specific retraining restricts scalability and adaptability in practice. These limitations underscore the need for efficient, generalizable registration frameworks capable of handling heterogeneous imaging contexts. In this work, we introduce a novel AI-driven framework for 3D non-rigid registration that generalizes across multiple imaging modalities and anatomical regions. Unlike conventional methods that rely on application-specific models, our approach eliminates anatomy- or modality-specific customization, enabling streamlined integration into diverse clinical environments.
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