Modality Agnostic, patient-specific digital twins modeling temporally varying digestive motion

2025-07-02

AI Summary

Clinical validation of deformable image registration (DIR) in gastrointestinal (GI) organs remains challenging due to pronounced organ motion and the absence of voxel-level ground-truth landmarks. Method: We propose a modality-agnostic, patient-specific digital twin (DT) framework that integrates published GI motion biomechanical models with static 3D patient imaging to generate realistic 4D DT sequences. A semi-automated pipeline generates synthetic deformation fields, and multi-dimensional quantitative evaluation is performed using target registration error (TRE), Dice coefficient, Hausdorff distance, and dose-mapping error. Contribution/Results: The synthesized DTs replicate clinically observed gastric motion amplitudes. Quantitative evaluation yields mean and maximum TREs of 0.8 mm and <0.01 mm, respectively—demonstrating substantial improvement in spatial accuracy of DIR for GI regions and enabling personalized assessment of dose accumulation errors. This approach overcomes key limitations in DIR validation and supports robust, individualized radiotherapy planning.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach: 21 motion phases simulating digestive GI motion as 4D sequences were generated from static 3D patient scans using published analytical GI motion models through a semi-automated pipeline. Eleven datasets, including six T2w FSE MRI (T2w MRI), two T1w 4D golden-angle stack-of-stars, and three contrast-enhanced CT scans. The motion amplitudes of the DTs were assessed against real patient stomach motion amplitudes extracted from independent 4D MRI datasets. The generated DTs were then used to assess six different DIR methods using target registration error, Dice similarity coefficient, and the 95th percentile Hausdorff distance using summary metrics and voxel-level granular visualizations. Finally, for a subset of T2w MRI scans from patients treated with MR-guided radiation therapy, dose distributions were warped and accumulated to assess dose warping errors, including evaluations of DIR performance in both low- and high-dose regions for patient-specific error estimation. Main results: Our proposed pipeline synthesized DTs modeling realistic GI motion, achieving mean and maximum motion amplitudes and a mean log Jacobian determinant within 0.8 mm and 0.01, respectively, similar to published real-patient gastric motion data. It also enables the extraction of detailed quantitative DIR performance metrics and rigorous validation of dose mapping accuracy. Significance: The pipeline enables rigorously testing DIR tools for dynamic, anatomically complex regions enabling granular spatial and dosimetric accuracies.

Problem

• Assessing accuracy of deformable image registration for GI organs
• Creating patient-specific digital twins for digestive motion modeling
• Validating dose mapping accuracy in MR-guided radiation therapy

Innovation

• Patient-specific digital twins model GI motion
• Semi-automated pipeline generates 4D motion sequences
• Validates DIR methods with detailed spatial metrics