![]() The good diagnostic accuracy and low observer variability bear the potential of improved integration of intracoronary imaging and physiological assessment. OFR is a novel and fast method allowing assessment of flow-limiting coronary stenosis without pressure wire and induced hyperaemia. Intra- and inter-observer variability in OFR analysis was 0.00☐.02 and 0.00☐.03, respectively. Optical coherence tomography (OCT) is a noninvasive. Average OFR analysis time was 55☒3 seconds for each OCT pullback. Haemodialysis can affect various ocular parameters, particularly choroidal thickness and IOP. The AUC was higher for OFR than minimal lumen area (0.93 versus 0.80, p=0.002). Computational analysis was performed in 125 vessels from 118 patients. OFR was compared with FFR, both using a cut-off value of 0.80 to define ischaemia. Bifurcation fractal laws were applied to correct the change in reference lumen size due to the step-down phenomenon. The lumen of the interrogated vessel and the ostia of the side branches were automatically delineated and used to compute OFR. Patients who underwent both OCT and FFR prior to intervention were analysed. This study aimed to evaluate the diagnostic accuracy of a new OCT-based FFR (OFR) computational approach, using wire-based FFR as the reference standard. We assume this slope differs for diseased (obstructive and restrictive) lungs compared to normal lungs and will help to classify these groups.A novel method for computation of fractional flow reserve (FFR) from optical coherence tomography (OCT) was developed recently. Bottom panel shows the slope of linear curve fitting of alveoli diameter versus ventilator pressure. The higher the lung ventilation pressure, the greater alveoli diameter and 2D area. As expected, this parameter directly change with ventilator pressure. Top panel in figure 3 shows the histograms of alveoli diameters at aforementioned pressures. The images was reconstructed and segmentation algorithm was applied and the information about the structure changes of lung extracted. Based on polarization properties, OCT can differentiate tissue characteristics (fibrous, calcified, or lipid-rich plaque) and identify thin-cap. In order to see the dynamic change of structure, SD-OCT data collected from the surface of a fixated, buffer perfused and ventilated rat lung at 3 different ventilator pressures: 2.5, 5, and 6.5 TORR. Optical coherence tomography (OCT) is an optical analog of intravascular ultrasound (IVUS) that can be used to examine the coronary arteries and has 10-fold higher resolution than IVUS. The pressure-volume (PL-VL) curve of an excised lung inflated-deflated between minimum lung volume and total lung capacity (TLC) is characterized by a nonlinear relationship and is typified by a large hysteretic area indicating an irreversible energy loss. Total number of alveoli in this image is 145, 2D cross section area of an alveolus is 3467 (µm)2 on average, and diameter of an alveolus is 72µm on average. Right-bottom: Green contours show segmented alveolar walls. Right-top: Average quality of 2D images of alveoli from rat lung at a depth of 63µm captured and reconstructed by SD-OCT in the ventilated pressure of 5 TORR. 2: Left: Head of the OCT probe on the ex vivo rat lung. The segmentation code is capable of finding the contour of each alveolus and calculating 2D cross section area of each alveolus, the total area of alveoli, maximum, minimum, and average diameter of alveolar sacs in the image and the total number of alveolar sacs.įig. Right-bottom panel in figure 2 reveals that our segmentation algorithm works accurate even on the pretty poor resolution images with accuracy of 98.6% compared to manual detection. Figure 2 (right- top panel) shows one of the images with average quality which the contour of individual alveoli can’t be clearly seen. Please note that scale bar is 100um in both x and y directions Aveolar Structure SegmentationģD OCT scans were taken from different spots at all lobes of an ex vivo rat lung (left panel in figure 2). Right: The 3D volume image of alveolar walls. These images show the alveoli structure in different depth constructed from the SD-OCT data collected from the surface of the ex vivo rat lung. 1: Left : A stack of 2D cross section images with field of view of 3mm × 3mm.
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