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Background Computed tomography coronary angiography (CTCA) image analysis enables plaque characterization and non-invasive fractional flow reserve (FFR) calculation. We analyzed various parameters derived from CTCA images and evaluated their associations with ischemia. Methods 49 (61 lesions) patients underwent CTCA and invasive FFR. Lesions with diameter stenosis (DS) ≥ 50% were considered obstructive. CTCA image processing incorporating analytical and numerical methods were used to quantify anatomical parameters of lesion length (LL) and minimum lumen area (MLA); plaque characteristic parameters of plaque volume, low attenuation plaque (LAP) volume, dense calcium volume (DCV), normalized plaque volume (NP Vol), plaque burden, eccentricity index and napkin-ring (NR) sign; and hemodynamic parameters of resistance index, stenosis flow reserve (SFR) and FFR B . Ischemia was defined as FFR ≤ 0.8. Results Plaque burden and plaque volume were inversely related to FFR. Multivariable logistic regression analysis identified the best anatomical, plaque and hemodynamic predictors, respectively, as DS (≥50% vs B (≤0.8 vs >0.8; OR: 44.4; 95% CI: 8.8–224.8). AUC increased from 0.70 with DS as the sole predictor to 0.81 after adding NP Vol and NR Sign; further addition of FFR B increased AUC to 0.93. Conclusion Normalized plaque volume, napkin-ring derived from plaque analysis, and FFR B from numerical simulations on CTCA images substantially improved discrimination of ischemic lesions, compared to assessment by DS alone.

Invasive fractional flow reserve (FFR) is the gold standard to assess the functional coronary stenosis. The non-invasive assessment of diameter stenosis (DS) using coronary computed tomography angiography (CTA) has high false positive rate in contrast to FFR. Combining CTA with computational fluid dynamics (CFD), recent studies have shown promising predictions of FFRCT for superior assessment of lesion severity over CTA alone. The CFD models tend to be computationally expensive, however, and require several hours for completing analysis. Here, we introduce simplified models to predict noninvasive FFR at substantially less computational time. In this retrospective pilot study, 21 patients received coronary CTA. Subsequently a total of 32 vessels underwent invasive FFR measurement. For each vessel, FFR based on steady-state and analytical models (FFRSS and FFRAM, respectively) were calculated non-invasively based on CTA and compared with FFR. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 90.6% (87.5%), 80.0% (80.0%), 95.5% (90.9%), 88.9% (80.0%) and 91.3% (90.9%) respectively for FFRSS (and FFRAM) on a per-vessel basis, and were 75.0%, 50.0%, 86.4%, 62.5% and 79.2% respectively for DS. The area under the receiver operating characteristic curve (AUC) was 0.963, 0.954 and 0.741 for FFRSS, FFRAM and DS respectively, on a per-patient level. The results suggest that the CTA-derived FFRSS performed well in contrast to invasive FFR and they had better diagnostic performance than DS from CTA in the identification of functionally significant lesions. In contrast to FFRCT, FFRSS requires much less computational time.

Novel minimally invasive mitral valve interventions for dysfunctional mitral bioprosthesis are being performed through the apex of the left ventricle. This Transapical approach is also being developed to replace diseased native mitral valve using new generations of catheter-mounted prosthesis. These

Local hemodynamic parameters, such as wall shear stress (WSS), oscillatory shear index and relative resident time (RRT), have been linked to coronary plaque initiation and progression. In this study, a left coronary artery tree model was reconstructed from computed tomography angiography images of a patient with multiple stenoses. The geometry of the coronary artery tree model was virtually restored by eliminating the lesions, essentially re-creating the virtually healthy artery anatomy. Using numerical simulations, flow characteristics and hemodynamic parameter distributions in the stenosed and virtually healthy models were investigated. In the virtually healthy artery model, disturbed flows were found at four locations, prone to initialization of plaque formation. Low WSS and high RRT were exhibited in three of the four locations, and high WSS and low RRT were exhibited in the fourth. These findings suggest that coronary plaque is more likely to form in locations with disturbed flow conditions characterized by low WSS and high RRT or high WSS and low RRT. In addition, clinical index of fractional flow reserve was found to significantly correlate with blood flow rate, rather than anatomic parameters, such as diameter stenosis, which implied the importance of hemodynamic environment in stenosis formation.

Local hemodynamic parameters, such as wall shear stress (WSS), oscillatory shear index and relative resident time (RRT), have been linked to coronary plaque initiation and progression. In this study, a left coronary artery tree model was reconstructed from computed tomography angiography images of a patient with multiple stenoses. The geometry of the coronary artery tree model was virtually restored by eliminating the lesions, essentially re-creating the virtually healthy artery anatomy. Using numerical simulations, flow characteristics and hemodynamic parameter distributions in the stenosed and virtually healthy models were investigated. In the virtually healthy artery model, disturbed flows were found at four locations, prone to initialization of plaque formation. Low WSS and high RRT were exhibited in three of the four locations, and high WSS and low RRT were exhibited in the fourth. These findings suggest that coronary plaque is more likely to form in locations with disturbed flow conditions characterized by low WSS and high RRT or high WSS and low RRT. In addition, clinical index of fractional flow reserve was found to significantly correlate with blood flow rate, rather than anatomic parameters, such as diameter stenosis, which implied the importance of hemodynamic environment in stenosis formation.