Patient-specific Planning of Minimally Invasive Brain Interventions Based on Vascular-hemodynamic Mapping
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorUniversity Hospital, Ghent
About this trial
Endovascular procedures for treating brain aneurysms and arteriovenous malformations (AVMs) are becoming increasingly popular due to their less invasive nature and lower risk of complications. However, they still face challenges such as difficult catheter navigation and incomplete embolization.
This study aims to improve the efficiency and safety of endovascular procedures by developing new preoperative planning methodologies. These methodologies involve mapping the cerebral vasculature and creating computational fluid dynamics (CFD) and artificial intelligence (AI) models to simulate blood flow. By using these models, interventional radiologists can better plan catheter navigation and predict embolization outcomes. This could lead to faster, more accurate procedures with reduced radiation exposure for patients.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Subjects who are diagnosed with aneurysm or arteriovenous malformation
Subjects who are treated (or will be treated) with endovascular embolization and/or arteriovenous coiling
During and before/after the subject's intervention, medical images (MRI, 3D rotational angiography, DSA) were collected of the cerebral arterial vascular tree (which can be used to develop patient-specific models), including catheter location and use of contrast fluid to visualize the cerebral to make the arterial tree more visible.
Disqualifiers
None
Trial population
subjects who had or will have endovascular treatment of an intracranial aneurysm or cerebral arteriovenous malformation
Trial design
Design model
Cohort
Time perspective
Other
Treatments tested in this trial
Endovascular treatment
DeviceEndovascular treatment
Treatment groups
500 Participants
are divided into 2 treatment groupsGroup A: Intracranial aneurysms1 intervention
Group B: Arteriovenous malformations1 intervention
Trial outcomes
Primary outcomes
1
Accuracy of CFD models
Correlation coefficient between CFD-predicted and measured blood flow velocity and pressure distribution
Time frame
4 year2
Identification of important procedure parameters
Identification of key procedure parameters (e.g., properties of embolization agent, procedural actions) using the computational framework.
Time frame
6 year3
Efficiency of AI-enhanced CFD modelling
Time, sensitivity, specificity, and accuracy of AI models in verifying the acceleration power while maintaining high-quality results.
Time frame
6 year4
Cerebral vasculature mapping
Development of accurate and efficient image processing methods to support clinical embolization procedures. Evaluation using independent datasets and metrics (e.g., accuracy, precision, recall, F1-score).
Time frame
6 yearSponsors and contacts
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University Hospital, Ghent
Lead sponsor
University Ghent
Collaborator
Source ClinicalTrials.gov