A Multimodal Point Cloud-Based Method for Tumor Localization in Robotic Ultrasound-Guided Radiotherapy

• Published in: Technology in cancer research & treatment Vol. 23; p. 15330338241273149
• Main Authors: Song, Lintao, Li, Qixuan, Sai, Zhang, Sun, Kangkang, Chen, Wei, Zhang, Heng, Wang, Yibo, Jiao, Zhuqing, Ni, Xinye
• Format: Journal Article
• Language: English
• Published: United States SAGE Publications 01.01.2024; SAGE Publishing
• Subjects: Algorithms; Emerging technologies in cancer radiotherapy; Humans; Neoplasms - diagnostic imaging; Neoplasms - radiotherapy; Phantoms, Imaging; Radiotherapy Planning, Computer-Assisted - methods; Radiotherapy, Image-Guided - methods; Robotic Surgical Procedures - methods; Robotics - methods; Tomography, X-Ray Computed - methods; Ultrasonography - methods; point cloud processing; multimodal point cloud; tumor localization; ultrasound-guided radiotherapy; robotic
• ISSN: 1533-0346; 1533-0338; 1533-0338
• DOI: 10.1177/15330338241273149

Part of the tumor localization methods in radiotherapy have poor real-time performance and may generate additional radiation. We propose a multimodal point cloud-based method for tumor localization in robotic ultrasound-guided radiotherapy, which only irradiates computed tomography (CT) during radiotherapy planning to avoid additional radiation. The tumor position was determined using the CT point cloud, and the red green blue depth (RGBD) point cloud was used to determine body surface scanning location corresponding to the tumor location. The relationship between the CT point cloud and RGBD point cloud was established through multi-modal point cloud registration. The point cloud was then used for robot tumor localization through coordinate transformation between camera and robot. The maximum mean absolute error of the tumor location in the X, Y, and Z directions of the robot coordinate system were 0.781, 1.334, and 1.490 mm, respectively. The average point-to-point translation mean absolute error between the actual and predicted positions of the localization points was 1.847 mm. The maximum error in the random positioning experiment was 1.77 mm. The proposed method is radiation free and has real-time performance, with tumor localization accuracy that meets the requirements of radiotherapy. The proposed method, which potentially reduces the risks associated with radiation exposure while ensuring efficient and accurate tumor localization, represents a promising advancement in the field of radiotherapy.