ABSTRACT
Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.
ABSTRACT
Bioreactors enabling direct perfusion of cell suspensions or culture media through the pores of 3D scaffolds have long been used in tissue engineering to improve cell seeding efficiency as well as uniformity of cell distribution and tissue development. A macro-scale U-shaped bioreactor for cell culture under perfusion (U-CUP) has been previously developed. In that system, the geometry of the perfusion chamber results in rather uniform flow through most of the scaffold volume, but not in the peripheral regions. Here, the design of the perfusion chamber has been optimized to provide a more homogenous perfusion flow through the scaffold. Then, the design of this macro-scale flow-optimized perfusion bioreactor (macro-Flopper) has been miniaturized to create a mini-scale device (mini-Flopper) compatible with medium-throughput assays. Computational fluid dynamic (CFD) modeling of the new chamber design, including a porous scaffold structure, revealed that Flopper bioreactors provide highly homogenous flow speed, pressure, and shear stress. Finally, a proof-of-principle of the functionality of the Flopper systems by engineering endothelialized stromal tissues using human adipose tissue-derived stromal vascular fraction (SVF) cells has been offered. Preliminary evidence showing that flow optimization improves cell maintenance in the engineered tissues will have to be confirmed in future studies. In summary, two bioreactor models with optimized perfusion flow and complementary sizes have been proposed that might be exploited to engineer homogenous tissues and, in the case of the mini-Flopper, for drug testing assays with a limited amount of biological material.
Subject(s)
Tissue Engineering , Tissue Scaffolds , Humans , Tissue Engineering/methods , Tissue Scaffolds/chemistry , Cell Culture Techniques/methods , Perfusion , BioreactorsABSTRACT
INTRODUCTION: Valve-sparing root replacement (VSRR) of the ascending aorta is a life-saving procedure for the treatment of aortic aneurysms, but patients remain at risk for post-operative events involving the downstream native aorta, the mechanism for which is uncertain. It is possible that proximal graft replacement of the ascending aorta induces hemodynamics alterations in the descending aorta, which could trigger adverse events. Herein, we present a fluid-structure interaction (FSI) protocol, based on patient-specific geometry and boundary conditions, to assess impact of proximal aortic grafts on downstream aortic hemodynamics and distensibility. METHODS: Cardiac magnetic resonance (CMR), including MRA, cine-CMR and 4D flow sequences, was performed prior and after VSRR on one subject. Central blood pressure was non-invasively acquired at the time of the CMR: data were used to reconstruct the pre- and post-VSRR model and derive patient-specific boundary conditions for the FSI and a computational fluid dynamic (CFD) analysis with the same settings. Results were validated comparing the predicted velocity field against 4D flow dataset, over four landmarks along the aorta, and the predicted distensibility against the cine-CMR derived value. RESULTS: Instantaneous velocity magnitudes extracted from 4D flow and FSI were similar (p > 0.05), while CFD-predicted velocity was significantly higher (p < 0.001), especially in the descending aorta of the pre-VSRR model (vmax was 73 cm/s, 76 cm/s and 99 cm/s, respectively). As measured in cine-CMR, FSI predicted an increase in descending aorta distensibility after grafting (i.e., 4.02 to 5.79 10-3 mmHg-1). In the descending aorta, the post-VSRR model showed increased velocity, aortic distensibility, stress and strain and wall shear stress. CONCLUSIONS: Our Results indicate that i) the distensibility of the wall cannot be neglected, and hence the FSI method is necessary to obtain reliable results; ii) graft implantation induces alterations in the hemodynamics and biomechanics along the thoracic aorta, that may trigger adverse vessel remodeling.
