PURPOSE: 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. The authors developed and evaluated a computer-aided system for automatic detection and quantification of aortic calcifications in low-dose noncontrast-enhanced chest CT. METHODS: The system was trained and tested on scans from participants of a lung cancer screening trial. A total of 433 low-dose, non-ECG-synchronized, noncontrast-enhanced 16 detector row examinations of the chest was 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 multiatlas-based segmentation method was developed to delineate the aorta. Segmented volume was thresholded and potential calcifications (candidate objects) were extracted by three-dimensional connected component labeling. Due to image resolution and noise, in rare cases extracted candidate objects were connected to the spine. They were separated into a part outside and parts inside the aorta, and only the latter was further analyzed. All candidate objects were represented by 63 features describing their size, position, and texture. Subsequently, a two-stage classification with a selection of features and k-nearest neighbor classifiers was performed. Based on the detected aortic calcifications, total calcium volume score was determined for each subject. RESULTS: The computer system correctly detected, on the average, 945 mm3 out of 965 mm3 (97.9%) calcified plaque volume in the aorta with an average of 64 mm3 of false positive volume per scan. Spearman rank correlation coefficient was p = 0.960 between the system and the first observer compared to p = 0.961 between the two observers. CONCLUSIONS: Automatic calcium scoring in the aorta thus appears feasible with good correlation between manual and automatic scoring.
PURPOSE: 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. The authors developed and evaluated a computer-aided system for automatic detection and quantification of aortic calcifications in low-dose noncontrast-enhanced chest CT. METHODS: The system was trained and tested on scans from participants of a lung cancer screening trial. A total of 433 low-dose, non-ECG-synchronized, noncontrast-enhanced 16 detector row examinations of the chest was 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 multiatlas-based segmentation method was developed to delineate the aorta. Segmented volume was thresholded and potential calcifications (candidate objects) were extracted by three-dimensional connected component labeling. Due to image resolution and noise, in rare cases extracted candidate objects were connected to the spine. They were separated into a part outside and parts inside the aorta, and only the latter was further analyzed. All candidate objects were represented by 63 features describing their size, position, and texture. Subsequently, a two-stage classification with a selection of features and k-nearest neighbor classifiers was performed. Based on the detected aortic calcifications, total calcium volume score was determined for each subject. RESULTS: The computer system correctly detected, on the average, 945 mm3 out of 965 mm3 (97.9%) calcified plaque volume in the aorta with an average of 64 mm3 of false positive volume per scan. Spearman rank correlation coefficient was p = 0.960 between the system and the first observer compared to p = 0.961 between the two observers. CONCLUSIONS: Automatic calcium scoring in the aorta thus appears feasible with good correlation between manual and automatic scoring.
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Authors: Sanne G M van Velzen; Nikolas Lessmann; Birgitta K Velthuis; Ingrid E M Bank; Desiree H J G van den Bongard; Tim Leiner; Pim A de Jong; Wouter B Veldhuis; Adolfo Correa; James G Terry; John Jeffrey Carr; Max A Viergever; Helena M Verkooijen; Ivana Išgum Journal: Radiology Date: 2020-02-11 Impact factor: 29.146
Authors: Amanda J Cox; Fang-Chi Hsu; Subhashish Agarwal; Barry I Freedman; David M Herrington; J Jeffrey Carr; Donald W Bowden Journal: Cardiovasc Diabetol Date: 2014-12-12 Impact factor: 9.951