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Abstract #4269

Assessing Deep Learning's Ability to Segment Aortic Valve Calcifications in Contrast-Free Cardiac MRI

Enrique Almar-Munoz1, Christian Kremser1, Markus Haltmeier2, and Agnes Mayr1
1Radiology, Medical University of Innsbruck, Innsbruck, Austria, 2Mathematics, Medical University of Innsbruck, Innsbruck, Austria

Synopsis

Keywords: Diagnosis/Prediction, Cardiovascular, Aortic valve calcifications

Motivation: Contrast-enhanced CT is the gold standard for TAVI planning but unsuitable for patients with kidney disease or contrast allergies. Contrast-free MRI is an alternative but lacks visualization of calcifications.

Goal(s):

  • Use Deep Learning models to segment calcified regions on contrast-free CMR
  • Quantify from the CMR the calcified area, indicator of aortic stenosis severity

Approach: We performed CTA-CMR registration with Elastix using aorta masks, then trained several SOTA Deeo Leaning models using CMR images and CTA-to-CMR registered calcification masks.

Results: Segmentation of aorta valve calcifications showed low DSC (0.309), while aorta wall calcifications could not be estimated. Valves' calcified area was underestimated by 25.96%.

Impact: Calcifications are not visible on contrast-free CMR to the human eye, so we tested AI segmentation using CTA-registered labels, achieving low results (DSC 0.309). While a larger dataset or improved registration may help, the task's physical feasibility remains uncertain.

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Keywords