A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers
Yue Jiang, Karan P. Punjabi, Iain Pierce, Daniel S. Knight, Tina Yao, Jennifer Anne Steeden, Alun David Hughes, Vivek Muthurangu, Rhodri Huw Davies · Magnetic Resonance Imaging · 2024
BACKGROUND: The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening routine cardiac scans and in population studies. METHODS: , a 3D U-Net variant trained directly on high-resolution 3D isotropic images. A second observer was recruited to investigate the interobserver variability. RESULTS: and two clinical observers in the diameter measurements at the mid ascending aorta, mid aortic arch, and descending aorta. CONCLUSIONS: A new method of producing isotropic 3D aortic segmentations from routine CMR 2D anisotropic localizers shows good agreement with segmentation made directly from 3D isotropic images. The method has the potential to be used as a simple screening method for aortic aneurysms without the need for additional sequences.