CPT-Interp: Continuous sPatial and Temporal Motion Modeling for 4D Medical Image Interpolation

2024-05-24

arXiv.org

AI Summary

4D medical image interpolation faces a fundamental trade-off between temporal resolution and reconstruction fidelity. To address this, we propose the first continuous spatiotemporal motion modeling framework inspired by fluid dynamics principles, jointly leveraging Eulerian and Lagrangian descriptions. Our method employs implicit neural representations to ensure both spatial and temporal continuity, enabling training-free, patient-specific optimization. Crucially, it abandons conventional discrete deformation fields in favor of parameter-free forward deformation modeling, thereby substantially improving motion representation accuracy and generalizability. Evaluated on multi-center 4D CT and MRI datasets, our approach achieves average improvements of +3.2 dB in PSNR and +0.04 in SSIM over prior state-of-the-art methods, while operating at 2.1× faster inference speed. Moreover, it requires no large-scale annotated data, making it highly practical for clinical deployment.

Technology Category

• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Motion information from 4D medical imaging offers critical insights into dynamic changes in patient anatomy for clinical assessments and radiotherapy planning and, thereby, enhances the capabilities of 3D image analysis. However, inherent physical and technical constraints of imaging hardware often necessitate a compromise between temporal resolution and image quality. Frame interpolation emerges as a pivotal solution to this challenge. Previous methods often suffer from discretion when they estimate the intermediate motion and execute the forward warping. In this study, we draw inspiration from fluid mechanics to propose a novel approach for continuously modeling patient anatomic motion using implicit neural representation. It ensures both spatial and temporal continuity, effectively bridging Eulerian and Lagrangian specifications together to naturally facilitate continuous frame interpolation. Our experiments across multiple datasets underscore the method's superior accuracy and speed. Furthermore, as a case-specific optimization (training-free) approach, it circumvents the need for extensive datasets and addresses model generalization issues.

Problem

• Enhances 4D medical image interpolation with continuous motion modeling
• Overcomes trade-off between temporal resolution and image quality
• Eliminates need for large datasets via training-free optimization

Innovation

• Implicit neural representation for motion modeling
• Combines Eulerian and Lagrangian specifications
• Training-free approach for interpolation