Respiratory Subtraction for Pulmonary Microwave Ablation Evaluation
<|reference_start|>Respiratory Subtraction for Pulmonary Microwave Ablation Evaluation: Currently, lung cancer is a leading cause of global cancer mortality, often necessitating minimally invasive interventions. Microwave ablation (MWA) is extensively utilized for both primary and secondary lung tumors. Although numerous clinical guidelines and standards for MWA have been established, the clinical evaluation of ablation surgery remains challenging and requires long-term patient follow-up for confirmation. In this paper, we propose a method termed respiratory subtraction to evaluate lung tumor ablation therapy performance based on pre- and post-operative image guidance. Initially, preoperative images undergo coarse rigid registration to their corresponding postoperative positions, followed by further non-rigid registration. Subsequently, subtraction images are generated by subtracting the registered preoperative images from the postoperative ones. Furthermore, to enhance the clinical assessment of MWA treatment performance, we devise a quantitative analysis metric to evaluate ablation efficacy by comparing differences between tumor areas and treatment areas. To the best of our knowledge, this is the pioneering work in the field to facilitate the assessment of MWA surgery performance on pulmonary tumors. Extensive experiments involving 35 clinical cases further validate the efficacy of the respiratory subtraction method. The experimental results confirm the effectiveness of the respiratory subtraction method and the proposed quantitative evaluation metric in assessing lung tumor treatment.<|reference_end|>
@article{li2024respiratory,
    title={Respiratory Subtraction for Pulmonary Microwave Ablation Evaluation},
    author={Wan Li, Xinyun Zhong, Wei Li, Song Zhang, Moheng Rong, Yan Xi, Peng
  Yuan, Zechen Wang, Xiaolei Jiang, Rongxi Yi, Hui Tang, Yang Chen, Chaohui
  Tong, Zhan Wu, Feng Wang},
    journal={arXiv preprint arXiv:2408.04299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04299},
    primaryClass={cs.CV}
}