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Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
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Robust non-rigid image-to-patient registration for contactless dynamic thoracic tumor localization using recursive
Dongyuan Li1, Yixin Shan1, Yuxuan Mao1
1organization=Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, city=Shanghai, postcode=200241, country=China.
Medical Image Analysis
|January 30, 2026
Summary
This study introduces a novel framework for real-time, contactless, non-rigid registration of thoracic tumors during surgery. The system reconstructs 4D CT scans and uses surface imaging for precise patient alignment, improving surgical navigation.
Area of Science:
- Medical Imaging
- Surgical Navigation
- Computational Anatomy
Background:
- Real-time deformable image-to-patient registration is crucial but challenging for cross-modal thoracic applications.
- Physiological motions like respiration complicate accurate tumor localization in dynamic thoracic environments.
Purpose of the Study:
- To develop a robust, contactless, non-rigid registration framework for dynamic thoracic tumor localization.
- To enable real-time spatial transformations across modalities, overcoming current clinical limitations.
Main Methods:
- A Recursive Deformable Diffusion Model (RDDM) reconstructs 4D CT sequences from limited scans to capture respiratory dynamics.
- A contactless non-rigid registration algorithm using GICP and stereo RGB-D imaging aligns patients in real-time.
- Normal vector and expansion-contraction constraints enhance registration robustness and prevent local minima.
Main Results:
- The RDDM demonstrated high anatomical fidelity across respiratory phases with a PSNR of 34.01 ± 2.78 dB.
- The contactless registration framework showed preliminary clinical viability for tumor localization and high-precision tracking.
- The system successfully captured intraoperative respiratory dynamics for improved localization.
Conclusions:
- The proposed framework offers a robust solution for contactless, non-rigid registration in dynamic thoracic environments.
- The system shows potential for seamless integration into surgical navigation systems for enhanced tumor localization.
- This approach addresses key challenges in real-time cross-modal registration for improved surgical outcomes.
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