  Purpose: The goal of this study was to assess the potential added benefit of
accounting for partial volume effects (PVE) in an automatic coronary lumen
segmentation algorithm from coronary computed tomography angiography (CCTA).
Materials and methods: We assessed the potential added value of PVE integration
as a part of the automatic coronary lumen segmentation algorithm by means of
segmentation accuracy using the MICCAI 2012 challenge framework and by means of
flow simulation overall accuracy, sensitivity, specificity, negative and
positive predictive values and the receiver operated characteristic (ROC) area
under the curve. We also evaluated the potential benefit of accounting for PVE
in automatic segmentation for flow-simulation for lesions that were diagnosed
as obstructive based on CCTA, which could have indicated a need for an invasive
exam and revascularization. Results: Our segmentation algorithm improves the
maximal surface distance error by ~39% compared to previously published method
on the 18 datasets 50 from the MICCAI 2012 challenge with comparable Dice and
mean surface distance. Results with and without accounting for PVE were
comparable. In contrast, integrating PVE analysis into an automatic coronary
lumen segmentation algorithm improved the flow simulation specificity from 0.6
to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved
the area under the ROC curve for detecting hemodynamically significant CAD from
0.76 to 0.8 compared to automatic segmentation without PVE analysis with
invasive FFR threshold of 0.8 as the reference standard. The improvement in the
AUC was statistically significant (N=76, Delong's test, p=0.012). Conclusion:
Accounting for the partial volume effects in automatic coronary lumen
segmentation algorithms has the potential to improve the accuracy of CCTA-based
hemodynamic assessment of coronary artery lesions.
