4D segmentation of the thoracic aorta from 4D flow MRI using deep learning

Diana M Marin-Castrillon; Alain Lalande; Sarah Leclerc; Khalid Ambarki; Marie-Catherine Morgant; Alexandre Cochet; Siyu Lin; Olivier Bouchot; Arnaud Boucher; Benoit Presles

doi:10.1016/j.mri.2022.12.021

4D segmentation of the thoracic aorta from 4D flow MRI using deep learning

Magn Reson Imaging. 2023 Jun:99:20-25. doi: 10.1016/j.mri.2022.12.021. Epub 2023 Jan 5.

Affiliations

¹ Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France.
² Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France; Medical Imaging Department, University Hospital of Dijon, Dijon 21000, France.
³ Siemens Healthcare SAS, Saint-Denis 93200, France.
⁴ Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France; Department of cardiovascular and thoracic surgery, University Hospital of Dijon, Dijon 21000, France.
⁵ Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France. Electronic address: benoit.presles@u-bourgogne.fr.

PMID: 36621555
DOI: 10.1016/j.mri.2022.12.021

Abstract

Background: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure iteration which can be obtained from 4D flow MRI. However, the generation of these biomarkers requires prior 4D segmentation of the aorta.

Objective: To develop an automatic deep learning model to segment the aorta in 4D from 4D flow MRI.

Methods: Segmentation is addressed with a U-Net based segmentation model that treats each 4D flow MRI frame as an independent sample. Performance is measured with respect to Dice score (DS) and Hausdorff distance (HD). In addition, the maximum and minimum surface areas at the level of the ascending aorta are measured and compared with those obtained from cine-MRI.

Results: The segmentation performance was 0.90 ± 0.02 for the DS and the mean HD was 9.58 ± 4.36 mm. A correlation coefficient of r = 0.85 was obtained for the maximum surface and r = 0.86 for the minimum surface between the 4D flow MRI and cine-MRI.

Conclusion: The proposed automatic approach of 4D aortic segmentation from 4D flow MRI seems to be accurate enough to contribute to the wider use of this imaging technique in the analysis of pathologies such as TAA.

Keywords: 4D flow MRI; Deep learning; Segmentation; Thoracic aortic aneurysm.

MeSH terms

Aorta
Aorta, Thoracic
Aortic Aneurysm, Thoracic*
Blood Flow Velocity
Deep Learning*
Humans
Magnetic Resonance Imaging / methods
Magnetic Resonance Imaging, Cine / methods