"Automated aortic calcium scoring on low-dose chest computed tomography"

Ivana Išgum, Annemarieke Rutten, Mathias Prokop, Marius Staring, Stefan Klein, Josien P.W. Pluim, Max A. Viergever and Bram van Ginneken

Abstract

Thoracic computed tomography (CT) scans provide information about cardiovascular risk status. These scans are non-ECG-synchronized, thus precise quantification of coronary calcifications is difficult. Aortic calcium scoring is less sensitive to cardiac motion, so it is an alternative to coronary calcium scoring as an indicator of cardiovascular risk. We developed and evaluated a computer-aided system for automatic detection and quantification of aortic calcifications in low-dose non-contrast-enhanced chest CT. A computer-aided system for automatic detection and quantification of aortic calcifications was trained and tested on scans from participants of a lung cancer screening trial. A total of 433 low-dose, non-ECG-synchronized, non-contrast enhanced 16-detector row examinations of the chest were randomly divided into 340 training and 93 test data sets. A first observer manually identified aortic calcifications on training and test scans. A second observer did the same on the test scans only. First, a multi-atlas-based segmentation method was developed to delineate the aorta. Subsequently, the training data was used to train the system based on statistical pattern recognition theory to automatically identify calcifications in the aortic wall. Calcium volume scores for computer system and first observer and for the two observers were compared using descriptive statistics and Spearman rank correlation coefficients. The computer system correctly detected on average 768 mm³ out of 871 mm³ calcified plaque volume in the aorta with an average 61 mm³ of false positive volume per scan. Spearman rank correlation coefficient was ρ=0.97 between system and first observer compared to ρ=0.99 between the two observers. Automatic calcium scoring in the aorta appears feasible with good correlation between manual and automatic scoring.

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