Visualization, classification and quantification of coronary atherosclerotic plaque using CT soft- and hardware phantom models

PhD ceremony: Mr. W. Kristanto, 12.45 uur, Academiegebouw, Broerstraat 5, Groningen

Dissertation: Visualization, classification and quantification of coronary atherosclerotic plaque using CT soft- and hardware phantom models

Promotor(s): prof. M. Oudkerk

Faculty: Medical Sciences

Non-invasive coronary imaging has the potential to replace invasive coronary angiography as the common technique for detection of coronary heart disease, if the key-morphology to diagnose coronary artery disease (CAD) can be determined by CT acquisition, processing, and post-processing techniques. Both the vessel width (lumen opening) and wall morphology are important characteristics in disease detection. Multi-detector-row computed-tomography (MDCT) is the preferred imaging modality to assess CAD because of its excellence in quantifying the coronary lumen opening and the calcified portion of plaques infesting the coronary wall. However, visualizing the coronaries with MDCT still has its limitations. In this thesis possible solutions to identify and quantify these limitations are investigated.

It was demonstrated that motion artefacts, resulting in a false representation of lumen narrowing, can be identified using a combination of quantitative and qualitative assessment, and that presence of very small calcifications causes distortion of measurements thus scrutinizing the meaning of a zero calcium measurement. In early diagnosis characterization of non-calcified plaques is of interest. A literature review shows little agreement on the measurement methods of these plaques. A new characterization method is proposed that directly measures the plaque lipid-rich content percentage. The method has limited applicability due to several inherent limitations. The major effect of injected contrast on plaque measurement was quantified and a correction scheme was proposed. In conclusion, MDCT is a powerful tool for assessing CAD, but still leaves room for improvements. The results in this thesis will help to improve early detection of CAD using MDCT.