A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model.
Tengfei WangTiancheng HeZhenglin ZhangQi ChenLiwei ZhangGuoren XiaLizhuang YangHongzhi WangStephen T C WongHai LiPublished in: International journal of computer assisted radiology and surgery (2022)
The proposed system integrates a patient-specific respiratory motion compensation model to reduce the effect of respiratory motion during percutaneous lung biopsy and help interventional radiologists target the lesion efficiently. Our preclinical studies indicate that the image-guided system has the ability to accurately predict and track lung nodules of individual patients and has the potential for use in the diagnosis and treatment of early stage lung cancer.
Keyphrases
- early stage
- end stage renal disease
- ejection fraction
- newly diagnosed
- randomized controlled trial
- high speed
- chronic kidney disease
- ultrasound guided
- respiratory tract
- prognostic factors
- peritoneal dialysis
- artificial intelligence
- minimally invasive
- stem cells
- squamous cell carcinoma
- cell therapy
- patient reported outcomes
- machine learning
- high resolution
- deep learning
- climate change
- sentinel lymph node
- lymph node
- mesenchymal stem cells
- fine needle aspiration
- radiofrequency ablation
- human health
- rectal cancer