Abstract
Automatic three-dimensional (3-D) reconstruction of the coronary arteries (CA) from medical imaging modalities is still a challenging task. In this study, we present a deep learning-based method of automatic identification of the two ends of the vessel from X-ray coronary angiography (XCA). We also present a method of using template models of CA in matching the two-dimensional segmented vessels from two different angles of XCA. For the deep learning network, we used a U-net consisting of an encoder (Resnet) and a decoder. The two ends of the vessel were manually labeled to generate training images. The network was trained with 2,342, 1,907, and 1,523 labeled images for the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. For template models of CA, ten reconstructed 3-D models were averaged for each artery. The accuracy of correspondence using template models was compared with that of manual matching. The deep learning network pointed the proximal region (20% of the total length) in 97.7, 97.5, and 96.4% of 315, 201, and 167 test images for LAD, LCX, and RCA, respectively. The success rates in pointing the distal region were 94.9, 89.8, and 94.6%, respectively. The average distances between the projected points from the reconstructed 3-D model to the detector and the points on the segmented vessels were not statistically different between the template and manual matchings. The computed FFR was not significantly different between the two matchings either. Deep learning methodology is feasible in identifying the two ends of the vessel in XCA, and the accuracy of using template models is comparable to that of manual correspondence in matching the segmented vessels from two angles.
Highlights
Coronary artery (CA) diseases manifested by lumen narrowing is one of the leading causes of death worldwide (Virani et al, 2020)
The pressure drop is represented by fractional flow reserve (FFR) which is defined as the ratio of the distal pressure to the proximal pressure through a coronary stenosis (Corcoran et al, 2017)
Available imaging modalities have their own limitations in resolution and the appropriate interpretations of the resulting images require significant amount of the knowledge in the working of the imaging device and the anatomy of the arteries
Summary
Coronary artery (CA) diseases manifested by lumen narrowing is one of the leading causes of death worldwide (Virani et al, 2020). The coronary blood flow is not necessarily correlated with the visually inspected geometry of the vessel (Park et al, 2012; Toth et al, 2014). One of the methods to determine coronary blood flow is to measure the pressure drops along the Automatic 3D Reconstruction of Coronary Arteries stenosis by inserting pressure wire into the vessel (Corcoran et al, 2017). The pressure drop is represented by fractional flow reserve (FFR) which is defined as the ratio of the distal pressure to the proximal pressure through a coronary stenosis (Corcoran et al, 2017). FFR computation uses reconstructed three-dimensional (3-D) geometry of the vessel, and the accuracy of the 3-D reconstruction is critical to that of the computed FFR
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