Keywords: Diagnosis/Prediction, Inflammation
Motivation: Vulnerable atherosclerotic plaque in carotid artery is a significant contributor to cerebral mortality,with carotid wall inflammation being closely associated with plaque progression and rupture.
Goal(s): This study aims to develop a deep learning-based approach to improve the segmentation of inflammation uptake in carotid PET images,utilizing MRI anatomy and a novel radiotracer to assess plaque inflammation.
Approach: We employed a two-stage neural network with a multiscale Residual(MSR) backbone for segmentation of PET uptake.A simulation pipeline was created using PET-MR images to generate PET data for training.
Results: Our approach demonstrated superior accuracy to identify PET uptake regions compared to three other deep learning models.
Impact: By improving the assessment of carotid inflammation, this methodology has the potential to inform clinical decisions and interventions, ultimately reducing cardiovascular risk and mortality.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords