ArticleInternational journal of computer assisted radiology and surgery2022
A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Deep learning-based estimation of respiration-induced deformation from surface motion: A proof-of-concept study on 4D thoracic image synthesis.Medical physics · 2025Article
- Personalized Respiratory Motion Modeling Incorporating Longitudinal Data through Two-stage Transfer Learning.Current medical imaging · 2025Article
- Robust thoracic CT image registration with environmental adaptability using dynamic Welsch's function and hierarchical structure-awareness strategy.Quantitative imaging in medicine and surgery · 2024Article
- Investigating 4D respiratory cone-beam CT imaging for thoracic interventions on robotic C-arm systems: a deformable phantom study.Physical and engineering sciences in medicine · 2024Article
- A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.The British journal of radiology · 2024Article
- Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeDue to respiratory motion, precise tracking of lung nodule movement is a persistent challenge for guiding percutaneous lung biopsy during image-guided intervention. We developed an automated image-guided system incorporating effective and robust tracking algorithms to address this challenge. Accurate lung motion prediction and personalized image-guided intervention are the key technological contributions of this work.
methodsA patient-specific respiratory motion model is developed to predict pulmonary movements of individual patients. It is based on the relation between the artificial 4D CT and corresponding positions tracked by position sensors attached on the chest using an electromagnetic (EM) tracking system. The 4D CT image of the thorax during breathing is calculated through deformable registration of two 3D CT scans acquired at inspiratory and expiratory breath-hold. The robustness and accuracy of the image-guided intervention system were assessed on a static thorax phantom under different clinical parametric combinations.
resultsReal 4D CT images of ten patients were used to evaluate the accuracy of the respiratory motion model. The mean error of the model in different breathing phases was 1.59 ± 0.66 mm. Using a static thorax phantom, we achieved an average targeting accuracy of 3.18 ± 1.2 mm across 50 independent tests with different intervention parameters. The positive results demonstrate the robustness and accuracy of our system for personalized lung cancer intervention.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.