Integrating partial volume modeling into automatic coronary lumen segmentation from CCTA raises flow-simulation specificity from 0.6 to 0.68 and AUC from 0.76 to 0.8 for detecting lesions with invasive FFR below 0.8.
Robust and accurate coronary artery centerline extraction in CTA by combining model-driven and data-driven approaches
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Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation
Integrating partial volume modeling into automatic coronary lumen segmentation from CCTA raises flow-simulation specificity from 0.6 to 0.68 and AUC from 0.76 to 0.8 for detecting lesions with invasive FFR below 0.8.