An automated multi-modal object analysis approach to coronary calcium scoring of adaptive heart isolated MSCT images

Inter- and intra- observer variability is a problem often faced when an expert or observer is tasked with assessing the severity of a disease. This issue is keenly felt in coronary calcium scoring of patients suffering from atherosclerosis where in clinical practice, the observer must identify firstly the presence, followed by the location of candidate calcified plaques found within the coronary arteries that may prevent oxygenated blood flow to the heart muscle. This can be challenging for a human observer as it is difficult to differentiate calcified plaques that are located in the coronary arteries from those found in surrounding anatomy such as the mitral valve or pericardium. The inclusion or exclusion of false positive or true positive calcified plaques respectively will alter the patient calcium score incorrectly, thus leading to the possibility of incorrect treatment prescription. In addition to the benefits to scoring accuracy, the use of fast, low dose multi-slice CT imaging to perform the cardiac scan is capable of acquiring the entire heart within a single breath hold. Thus exposing the patient to lower radiation dose, which for a progressive disease such as atherosclerosis where multiple scans may be required, is beneficial to their health. Presented here is a fully automated method for calcium scoring using both the traditional Agatston method, as well as the Volume scoring method. Elimination of the unwanted regions of the cardiac image slices such as lungs, ribs, and vertebrae is carried out using adaptive heart isolation. Such regions cannot contain calcified plaques but can be of a similar intensity and their removal will aid detection. Removal of both the ascending and descending aortas, as they contain clinical insignificant plaques, is necessary before the final calcium scores are calculated and examined against ground truth scores of three averaged expert observer results. The results presented here are intended to show the requirement and feasibility for an automated scoring method that reduces the subjectivity and reproducibility error inherent with manual clinical calcium scoring.