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Abstract #4138

Automatic Segmentation of the Aorta and Supra-Aortic Trunks from 4D Flow MRI

You Zhou1,2, Armando Barrera-Naranjo2, Thomas Decourselle2, Diana M. Marin-Castrillon1, Benoit Presles1, Marylène Delcey3, Olivier Bouchot1,4, Jean-Joseph Christophe2, and Alain Lalande1,5
1ICMUB laboratory, CNRS 6302, University of Burgundy, Dijon, France, 2CASIS—Cardiac Simulation & Imaging Software, Quetigny, France, 3Siemens Healthcare SAS, Saint Denis, France, 4Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France, 5Medical Imaging Department, University Hospital of Dijon, Dijon, France

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, segmentation; 4D flow; aorta

Motivation: Few studies have explored segmenting both the supra-aortic trunks and the thoracic aorta, which is essential for analyzing hemodynamics in 4D flow.

Goal(s): The automatic segmentation of the aorta and the supra-aortic trunks from 4D flow MRI.

Approach: We trained a model to segment the aorta in systolic and diastolic phases and the supra-aortic trunks in systolic phase from both magnitude and phase images.

Results: Segmentation of the entire aorta was consistent across systolic and diastolic phases. For supra-aortic trunks, the Dice coefficient was 0.72±0.07 and the Hausdorff distance was 5.42±2.53 mm, which are acceptable given the trunks' small size and complex shape.

Impact: We proposed an automatic approach to segment the aorta on different time steps from magnitude images from 4D flow MRI. Additionally, we presented the outcomes of segmenting the supra-aortic trunks using phase and magnitude from the systolic period.

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Keywords