Test Time Optimized Generalized AI-based Medical Image Registration Method

• URL: http://arxiv.org/abs/2512.14556v1
• Date: Tue, 16 Dec 2025 16:29:27 GMT
• Title: Test Time Optimized Generalized AI-based Medical Image Registration Method
• Authors: Sneha Sree C., Dattesh Shanbhag, Sudhanya Chatterjee,
• Abstract summary: We introduce an AI-driven framework for 3D non-rigid registration that generalizes across multiple imaging modalities and anatomical regions.<n>Unlike conventional methods that rely on application-specific models, our approach eliminates anatomy- or modality-specific customization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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