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Proceedings Paper

Sensitivity of FFR-CT to manual segmentation
Author(s): Prem Venugopal; Xia Li; Lishui Cheng; Roshni Bhagalia; Peter M. Edic
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Paper Abstract

Fractional Flow Reserve (FFR), the ratio of arterial pressure distal to a coronary lesion to the proximal pressure, is indicative of its hemodynamic significance. This quantity can be determined from invasive measurements made with a catheter, or by using computational methods incorporating models of the the coronary vasculature. One of the inputs needed by a model-based approach for estimating FFR from Computed Tomography Angiography (CTA) images (denoted FFR-CT) is the geometry of the coronary arteries, which requires segmentation of the coronary lumen. Several algorithms have been proposed for coronary lumen segmentation, including the recent application of machine learning techniques. For evaluating these algorithms or for training machine learning algorithms, manual segmentation of the lumen has been considered as ground truth. However, since there is inter-subject variability in manual segmentation, it would be useful to first assess the extent to which this variability affects the predicted FFR values. In the current study, we evaluated the impact of inter-subject variability in manual segmentation on computed FFR, using datasets with three different manual segmentations provided as part of the Rotterdam Coronary Artery Evaluation Framework. FFR was computed using a coronary blood flow model. Our results indicate that variability in manual segmentations on FFR estimates depend on the FFR value. For FFR ≥ 0.97, variability in manual segmentations does not impact FFR estimates, while, for lower FFR values, the variability in manual segmentations leads to significant variability in FFR. The results of this study indicate that researchers should exercise caution when treating manual segmentations as ground truth for estimating FFR from CTA images.

Paper Details

Date Published: 12 March 2018
PDF: 8 pages
Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781T (12 March 2018); doi: 10.1117/12.2292836
Show Author Affiliations
Prem Venugopal, GE Global Research (United States)
Xia Li, GE Global Research (United States)
Lishui Cheng, GE Global Research (United States)
Roshni Bhagalia, GE Global Research (United States)
Peter M. Edic, GE Global Research (United States)


Published in SPIE Proceedings Vol. 10578:
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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