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Abstract #3187

Clinical Translation of Deep Learning-based Ascending Aortic Morphology Characterization Using 3D Non-Contrast-enhanced MRA

Louisa Fay1,2,3, Daniel Amsel1,4, Veronika Ecker1,2, Mario Lescan5, Till Hülnhagen4, Daniel Giese4, Bin Yang2, Sergios Gatidis1,3, and Thomas Kuestner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3Stanford Medicine, Department of Radiology, Stanford, CA, United States, 4Siemens Healthineers AG, Erlangen, Germany, 5University Heart Center Freiburg - Bad Krozingen, Freiburg, Germany

Synopsis

Keywords: Data Acquisition, Machine Learning/Artificial Intelligence, Causality, Confounder-free learning

Motivation: Enabling rapid and accurate assessment of thoracic aortic morphology from MR imaging is essential for effective cardiovascular risk evaluation and informed clinical decision-making

Goal(s): Implementation of an inline deep learning (DL) model on the MR scanner to automate aortic segmentation and morphology measurements, with evaluation of different MR angiography (MRA) sequences.

Approach: A residual-UNET-based DL model was deployed on a 3T-MR scanner, analyzing non-contrast-enhanced MRA data inline. Automated segmentation and measurements were displayed immediately after scanning.

Results: High test-retest reliability was achieved with the MRA sequences, confirming model robustness. Preliminary tests with a novel research Dixon-MRA sequence shows potential for a wider application.

Impact: Integration of deep learning image analysis for thoracic aorta inline on the MR scanner accelerates aortic morphology assessment across multiple sequences. Visualizing the results directly on the scanner supports rapid clinical decisions and advances cardiovascular imaging workflows.

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Keywords