Evaluation of the Efficacy of Computed Tomographic Coronary Angiography in Assessing Coronary Artery Morphology and Physiology

STUDY DESIGN

1. Patent recruitment Seventy patients with typical angina symptoms who had elective coronary angiography showing at least one complex (i.e., bifurcation lesion, long lesion, calcified lesion) obstructive lesion (>70% diameter stenosis on coronary angiography, or a fractional flow reserve <0.80) that is considered suitable for percutaneous coronary intervention (PCI) under IVUS guidance will be included in the study. Exclusion criteria are: 1) age >75 years, 2) ACS within <3 months, 2) eGFR <60ml/min/1.73m², 3) previous coronary artery bypass surgery, 3) decompensated heart failure, or left ventricular ejection fraction ≤30%, 4) intravenous contrast allergy or inability to receive treatment with aspirin, heparin, or thienopyridines, 5) anticipated life expectancy <1 year, 6) history of heart transplantation, 7) patient that requires surgical revascularization, and 8) extensive coronary artery disease (i.e., multiple chronic total occlusions) or tortuous coronary anatomy that does not allow assessment of the coronary arteries with NIRS-IVUS imaging. The recruited patients will provide informed consent and undergo CTCA imaging using a dedicated 3rd generation CT dual-source scanner (Siemens Force). The first 4 months of the study effort will be made to optimise image acquisition protocols so as the obtained CTCA data to be suitable for automated segmentation. Efficient imaging matrixes will be used to improve in-plane spatial resolution and sharper reconstruction kernels and iterative reconstruction algorithms will be implemented to enhance image segmentation.

Two weeks after CTCA imaging the patients will undergo planned PCI. During PCI effort will be made to study all the 3 epicardial coronary arteries - including the stenotic lesion - and some of their major side branches (i.e., large diagonals, obtuse marginals, the posterior descending artery or the left ventricular branch of the right coronary artery) with the combined NIRS-IVUS catheter. Following PCI the participants will be discharged on optimal medical treatment. 2. Segmentation of the CTCA imaging data and reconstruction of coronary artery anatomy Imaging data will be anonymised and analysed blinded to clinical details by an expert operator using dedicated workstation. Anatomical landmarks (i.e., side branches) will be identified in the CTCA and NIRS-IVUS imaging data and will be used to define segments of interest.

Analysis of the CTCA data will be performed using dedicated software that enables automated extraction of the luminal centreline, semi-automated detection of the lumen and outer vessel wall borders, and quantification of the plaque burden and incorporates a plaque characterisation algorithm that allows automated characterisation of the composition of the plaque. The plaque characterisation algorithm takes into account predefined fixed intensity cut-off values of the Hounsfield units and an adaptive approach that allows modification of these cut-off values according to image attenuation. Currently the segmentation process takes on average 3h per patient. In this project the investigators aim to optimise CTCA image acquisition and segmentation algorithms so as this process to become automated and reduce the time for CTCA segmentation to <1 hour. 3. Segmentation of the NIRS-IVUS imaging data and reconstruction of coronary artery anatomy The NIRS-IVUS data portraying the segments of interest will be analysed by an expert operator, blinded to the clinical details and CTCA imaging data, with a 3-month interval from the analysis of the CTCA data using a software that enables detection of the lumen and outer vessel wall borders, quantification of plaque burden and annotation of the calcific tissue component in IVUS. The output of the analysis of the NIRS imaging data is the chemogram which is a colour coded map of the distribution of the lipid component along the vessel wall (yellow indicates increased probability and red low probability of lipid tissue). A metric of the lipid burden is the lipid core burden index (LCBI) which is computed as the fraction of the yellow pixels that correspond to lipid component divided by 1000. In addition, for each 2mm segments the block chemogram is generated that provides a summary of the chemogram for this segment and displays the probability of the presence of lipid tissue in a 2mm block of the coronary artery. The block chemogram has been validated against histology and it has been shown that it enables accurate detection of lipid-rich plaques.

The segmented NIRS-IVUS data will be used to reconstruct the coronary anatomy using an established and well-validated methodology. Side branches with a diameter >1.5mm will be reconstructed from the angiographic data and fused with the main vessel geometry reconstructed from the NIRS-IVUS, since it has been shown that side branches affect ESS distribution. 4. Blood flow simulation Identical boundary conditions will be applied to both IVUS-based and CTCA-based models. Blood will be considered to be a laminar and incompressible Newtonian fluid with a dynamic viscosity of 0.0035 Pa•s and a density of 1,050 kg/m3. A steady flow profile will be imposed at the inflow of the lumen as this reduces computation time and there is evidence that there is no significant difference in the estimated ESS when a steady or a pulsatile flow profile is used. Murray's theory of constant ESS will be used to derive boundary conditions in the main and side branches. The arterial wall will be considered to be rigid and no-slip conditions will be applied at the luminal surface. Flow velocity will be estimated from the angiographic data by measuring the number of frames required for the contrast agent to pass from the inlet to the outlet of the reconstructed segment, the volume of the segment at baseline, and the cine frame rate. 5. Analysis of the NIRS-IVUS and CTCA imaging data It is anticipated that NIRS-IVUS imaging will be performed on average in 2.5 vessels per patient; from these 40 randomly selected vessels will be used to train the algorithms for CTCA segmentation and plaque characterisation (training dataset) and the remaining for validation purposes (validation dataset).