Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events (CVEs). CAC can be quantified in chest CT scans acquired in lung screening. However, in these images the reproducibility of CAC quantification is compromised by cardiac motion that occurs during scanning, thereby limiting the reproducibility of CVE risk assessment. We present a system for the identification of CACs strongly affected by cardiac motion artifacts by using a convolutional neural network (CNN).
This study included 125 chest CT scans from the National Lung Screening Trial (NLST). Images were acquired with CT scanners from four different vendors (GE, Siemens, Philips, Toshiba) with varying tube voltage, image resolution settings, and without ECG synchronization. To define the reference standard, an observer manually identified CAC lesions and labeled each according to the presence of cardiac motion: strongly affected (positive), mildly affected/not affected (negative). A CNN was designed to automatically label the identified CAC lesions according to the presence of cardiac motion by analyzing a patch from the axial CT slice around each lesion.
From 125 CT scans, 9201 CAC lesions were analyzed. 8001 lesions were used for training (19% positive) and the remaining 1200 (50% positive) were used for testing. The proposed CNN achieved a classification accuracy of 85% (86% sensitivity, 84% specificity).
The obtained results demonstrate that the proposed algorithm can identify CAC lesions that are strongly affected by cardiac motion. This could facilitate further investigation into the relation of CAC scoring reproducibility and the presence of cardiac motion artifacts.
Jurica Šprem, Bob D. de Vos, Pim A. de Jong, Max A. Viergever, and Ivana Išgum, "Classification of coronary artery calcifications according to motion artifacts in chest CT using a convolutional neural network," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330R (Presented at SPIE Medical Imaging: February 13, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2253669.
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