High-resolution 3D aortic segmentations from standard 2D CMR localisers: an AI application to clinical care and population studies

Motivation: Undiagnosed aortic aneurysms can be fatal. We aim to use machine learning to measure the aorta from standard CMR localisers, allowing screening and characterisation of aneurysms without the need for additional sequences. Goal(s): We aim to generate accurate 3D segmentations (1-1.5mm slice thickness) from standard 2D trans-axial SSFP localisers stacks (10-12mm slice thickness). Approach: We trained an AI model using high-resolution segmentations alongside simulated low-resolution images (2D localisers). This enables the model to predict high-resolution segmentations from unseen, low-resolution images by generalising from the learned patterns. Results: Our model shows promising performance in generating high-resolution segmentations from various unseen low-resolution validation dataset. Impact: With our model, the dilated aorta can be identified from routine CMR scans without the need for extra sequences. Additionally, 3D aorta morphology information can be obtained from previous clinical CMR studies or population studies without additional cost.