An Algorithm to Accurately Differentiate Growing and Stable Cerebrovascular Aneurysms
Mentor 1
Mahsa Dabagh
Start Date
28-4-2023 12:00 AM
Description
Aneurysm is a medical condition, rather than a disease, and its exact cause is not yet fully understood. It occurs when the walls of an artery weaken and bulge outward. The primary diagnostic methods used to identify aneurysm are Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, which can be used in conjunction with angiograms such as Magnetic Resonance Angiography (MRA) and CT angiography (CTA). However, both MIR and CT scans can present certain due to their challenges, such as lacking the capability in predicting if an aneurysm will grow or stay stable. The research project seeks to address these challenges by developing an algorithm that can quickly and accurately identify growing aneurysms in medical images. In this study, we convert existing patient-specific images from Digital Imaging and Communications in Medicines (.DICOM) files to Standard Triangle Language (.STL) files and then to Mesh (.MSH) files, and them simulate blood flow through these models. Our results will enable clinicians to diagnose growing aneurysms more efficiently and provide timely treatment to patients.
An Algorithm to Accurately Differentiate Growing and Stable Cerebrovascular Aneurysms
Aneurysm is a medical condition, rather than a disease, and its exact cause is not yet fully understood. It occurs when the walls of an artery weaken and bulge outward. The primary diagnostic methods used to identify aneurysm are Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, which can be used in conjunction with angiograms such as Magnetic Resonance Angiography (MRA) and CT angiography (CTA). However, both MIR and CT scans can present certain due to their challenges, such as lacking the capability in predicting if an aneurysm will grow or stay stable. The research project seeks to address these challenges by developing an algorithm that can quickly and accurately identify growing aneurysms in medical images. In this study, we convert existing patient-specific images from Digital Imaging and Communications in Medicines (.DICOM) files to Standard Triangle Language (.STL) files and then to Mesh (.MSH) files, and them simulate blood flow through these models. Our results will enable clinicians to diagnose growing aneurysms more efficiently and provide timely treatment to patients.
