Cardiovascular magnetic resonance (CMR) based flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases such as valvular regurgitation, cardiac shunts and vascular health. Clinically, flow volume quantification is often performed based on semi-automatic segmentation of a vessel throughout the cardiac cycle in a manually positioned 2D phase-contrast (PC) CMR plane. In this work, we proposed a quality-controlled AI-based framework for automatic flow quantification from a full CMR scan that includes automated view selection. Results show high accuracy in view selection and excellent agreement between manual and automated flow quantification analysis.
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