Coronary calcium scoring with partial volume correction in anthropomorphic thorax phantom and screening chest CT images.

INTRODUCTION: The amount of coronary artery calcium determined in CT scans is a well established predictor of cardiovascular events. However, high interscan variability of coronary calcium quantification may lead to incorrect cardiovascular risk assignment. Partial volume effect contributes to high interscan variability. Hence, we propose a method for coronary calcium quantification employing partial volume correction. METHODS: Two phantoms containing artificial coronary artery calcifications and 293 subject chest CT scans were used. The first and second phantom contained nine calcifications and the second phantom contained three artificial arteries with three calcifications of different volumes, shapes and densities. The first phantom was scanned five times with and without extension rings. The second phantom was scanned three times without and with simulated cardiac motion (10 and 30 mm/s). Chest CT scans were acquired without ECG-synchronization and reconstructed using sharp and soft kernels. Coronary calcifications were annotated employing the clinically used intensity value thresholding (130 HU). Thereafter, a threshold separating each calcification from its background was determined using an Expectation-Maximization algorithm. Finally, for each lesion the partial content of calcification in each voxel was determined depending on its intensity and the determined threshold. RESULTS: Clinical calcium scoring resulted in overestimation of calcium volume for medium and high density calcifications in the first phantom, and overestimation of calcium volume for high density and underestimation for low density calcifications in the second phantom. With induced motion these effects were further emphasized. The proposed quantification resulted in better accuracy and substantially lower over- and underestimation of calcium volume even in presence of motion. In chest CT, the agreement between calcium scores from the two reconstructions improved when proposed method was used. CONCLUSION: Compared with clinical calcium scoring, proposed quantification provides a better estimate of the true calcium volume in phantoms and better agreement in calcium scores between different subject scan reconstructions.

The Natural Course of Diffuse Idiopathic Skeletal Hyperostosis in the Thoracic Spine of Adult Males.

OBJECTIVE: Diffuse idiopathic skeletal hyperostosis (DISH) is characterized by flowing bony bridges on the right side of the spine. Knowledge of the development of these spinal bridges is limited. The current longitudinal computed tomography (CT) study was designed to bridge this gap. METHODS: Chest CT scans from elderly males with 2 scans (interval ≥ 2.5 yrs) were retrospectively included. Using the Resnick criteria, a pre-DISH group and a definite DISH group were identified. A scoring system based on the completeness of a bone bridge (score 0-3), extent of fluency, and location of the new bone was created to evaluate the progression of bone formation. RESULTS: In total, 145 of 1367 subjects were allocated to the DISH groups with a mean followup period of 5 years. Overall prevalence of a complete bone bridge increased in the pre-DISH group (11.3% to 31.0%) and in the definite DISH group (45.0% to 55.8%). The mean bridge score increased significantly in both the pre-DISH and definite DISH group (p < 0.001). The new bone gradually became more flowing and expanded circumferentially. CONCLUSION: Over the mean course of 5 years, the new bone developed from incomplete, pointy bone bridges to more flowing complete bridges. This suggests an ongoing and measurable bone-forming process that continues to progress, also in established cases of DISH.

Impact of automatically detected motion artifacts on coronary calcium scoring in chest computed tomography.

The amount of coronary artery calcification (CAC) quantified in computed tomography (CT) scans enables prediction of cardiovascular disease (CVD) risk. However, interscan variability of CAC quantification is high, especially in scans made without ECG synchronization. We propose a method for automatic detection of CACs that are severely affected by cardiac motion. Subsequently, we evaluate the impact of such CACs on CAC quantification and CVD risk determination. This study includes 1000 baseline and 585 one-year follow-up low-dose chest CTs from the National Lung Screening Trial. About 415 baseline scans are used to train and evaluate a convolutional neural network that identifies observer determined CACs affected by severe motion artifacts. Therefore, 585 paired scans acquired at baseline and follow-up were used to evaluate the impact of severe motion artifacts on CAC quantification and risk categorization. Based on the CAC amount, the scans were categorized into four standard CVD risk categories. The method identified CACs affected by severe motion artifacts with 85.2% accuracy. Moreover, reproducibility of CAC scores in scan pairs is higher in scans containing mostly CACs not affected by severe cardiac motion. Hence, the proposed method enables identification of scans affected by severe cardiac motion, where CAC quantification may not be reproducible.