DRTT : A Diffusion-based Framework for 4DCT Generation, Robust Thoracic Registration and Tumor Deformation Tracking
Dongyuan Li, Yixin Shan, Yuxuan Mao, Haochen Shi, Shenghao Huang, Weiyan Sun, Chang Chen, Xiaojun Chen
Abstract
In minimally invasive robotic thoracic surgery, the unavoidable respiratory motion of the patient causes lung lesions to move and deform, making precise tumor localiza-tion a significant challenge for surgeons. To address this, we introduce an RDDM (Recursive Deformable Diffusion Model)-based framework designed for real-time intraoperative tumor tracking, which can be used for registration and navigation in robot-assisted thoracic surgery. The RDDM reduces training complexity and enhances dataset utilization by employing a simplified DDM (Diffusion Deformable Model) iteratively, significantly lowering computational demands while maximizing the extraction of valuable information from limited 4D-CT (four-dimensional computed tomography) datasets. Considering the robustness required for intraoperative registration and navigation, we incorporate an ICP (Iterative Closest Point)-based point cloud registration method into the framework and validate our approach using publicly available datasets and volunteer trials. This innovation has the potential to reduce radiation exposure, trauma, and the risk of complications for patients undergoing minimally invasive thoracic surgery, and enables downstream tasks such as RAPNB (robot-assisted percutaneous needle biopsy) and radiation therapy.
BibTeX
@inproceedings{iros2025_drttadiffusionba,
title = {DRTT : A Diffusion-based Framework for 4DCT Generation, Robust Thoracic Registration and Tumor Deformation Tracking},
author = {Dongyuan Li and Yixin Shan and Yuxuan Mao and Haochen Shi and Shenghao Huang and Weiyan Sun and Chang Chen and Xiaojun Chen},
booktitle = {IROS 2025},
year = {2025}
}