Type III aortic arch angulation increases aortic stiffness: Analysis from an ex vivo porcine model.

Objective: The relationship among increased aortic arch angulation, aortic flow dynamics, and vessel wall stiffness remains unclear. This experimental ex vivo study investigated how increased aortic arch angulation affects aortic stiffness and stent-graft induced aortic stiffening, assessed by pulse wave velocity (PWV). Methods: Porcine thoracic aortas were connected to a circulatory mock loop in a Type I and Type III aortic arch configuration. Baseline characteristics and blood pressures were measured. Proximal and distal flow curves were acquired to calculate PWV in both arch configurations. After that, a thoracic stent-graft (VAMF2626C100TU) was deployed in aortas with adequate proximal landing zone diameters to reach 10% t0 20% oversizing. Acquisitions were repeated for both arch configurations after stent-graft deployment. Results: Twenty-four aortas were harvested, surgically prepared, and mounted. Cardiac output was kept constant for both arch configurations (Type I: 4.74 ± 0.40 and Type III: 4.72 ± 0.38 L/minute; P = .703). Compared with a Type I arch, aortic PWV increased significantly in the Type III arch (3.53 ± 0.40 vs 3.83 ± 0.40 m/second; P < .001), as well as blood pressures. A stent-graft was deployed in 15 aortas. After deployment, Type I arch PWV increased (3.55 ± 0.39 vs 3.81 ± 0.44 m/second; P < .001) and Type III arch PWV increased although not significantly (3.86 ± 0.42 vs 4.03 ± 0.46 m/second; P = .094). Type III arch PWV resulted the highest and significantly higher compared with the Type I arch after stent-graft deployment (3.81 ± 0.44 vs 4.03 ± 0.46 m/second; P = .023). Conclusions: Increased aortic arch angulation-as in a Type III arch-is associated with higher aortic PWV and blood pressures and this may negatively influence cardiovascular health.

Principal Component Analysis of Dynamic Thermography Data from Pregnant and Non-Pregnant Women.

In this work we propose a novel approach for the analysis ofdynamic thermography data based on the application of principal component analysis to thermal video data. The proposed approach is applied to thermal video recordings of the abdominal region of pregnant and non-pregnant female participants, and reveals consistent temperature trends across participants that to date have not been reported. Both for the pregnant and non-pregnant participants, the first principal component was found to describe approximately 80% of the total variance, and when combined, the first three principal components explained more than 90% of the total variance. The presence of consistent temporal components across participants is indicative of common passive as well as active underlying mechanisms thatgive rise to the observed temperature patterns. The outcome of this investigation supports further development and application of the proposedmethod in obstetrics and other medical fields.

Registration of Dynamic Thermography Data of the Abdomen of Pregnant and Non-Pregnant Women.

To date the use of thermography in the context of obstetrics has been primarily limited to the acquisition and analysis of static thermal images. In contrast, dynamic thermography involves the acquisition of a sequence of thermal images, taking into account temporal variations that would otherise be overlooked. However, dynamic recordings of regions of interest in human participants are likely to be affected by unavoidable participant movement due to breathing and other involuntary movements. In this work, a triangulation-based video registration technique using local affine transformations is proposed to register the abdominal region in dynamic thermal sequences. The proposed method is tested on one hour recordings of thermal data obtained from 10 pregnant and 10 non-pregnant female participants. The results obtained show that the proposed approach can compensate for movements and significantly improve region alignment throughout the thermal image sequence, thereby facilitating subsequent analysis of spatiotemporal temperature data in the considered image sequence.