# Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches
## 3 Results and Discussion
### Implications on FFR Computation Using OCT Imaging  
The FFR estimation with OCT-based computational models have usually ignored the presence of the side branches [12; 13], which may cause a deviation compared with the FFR measurement. Some studies have shown an improvement in FFR prediction by implicitly accounting for the side branch flow [5; 14]. This work suggests that the side branch at the downstream to the stenosis is necessary to consider for a precise FFR estimation with CFD simulation. In contrast to other imaging modalities, such as computed tomography (CT) angiography and 3D rotational angiography, OCT images cannot directly capture the side branches, as the images are acquired through a catheter guided along the main vessel. Yet, these images contain the information of side branch ostia, which can be used to approximate the geometry of the vessel tree structure. The accurate segmentation of the side branch ostia from OCT imaging is critical for estimating the side branch flow. While manual segmentation can be time-consuming and labor-intensive, automatic side branch ostium segmentation algorithms for OCT imaging are also available [34]. Moreover, more accurate 3D vessel geometry can be reconstructed by combining OCT images with angiographic views [27].  
Varied hypereemic boundary conditions in different patients brings more uncertainty in FFR predictions [35]. Previous studies have suggested different methods of non-invasively estimating the patient-specific hypereemic boundary conditions. One popular method of estimating the patient-specific boundary conditions is based on determining the myocardial mass and vessel diameters [36]. This method employs allomeric laws to determine coronary flow from myocardial mass and subsequently estimates the distal resistances based on vessel diameters as governed by [PERSON]'s law. However, CT imaging is required for calculating the myocardial mass and is thus not appropriate for the OCT-based method. One previous study [14] suggested a similar method of estimating the distal resistances based on the length of coronary arteries extracted from angiographic views and used to predict FFR in vessel tree models reconstructed using OCT imaging. Another study [37] estimated the patient-specific hypereemic flow intake through allometric relations and implemented a non-linear boundary condition to estimate the distal resistances by allowing the modification of [PERSON]'s law flow distribution in the diseased artery tree. The same study [37] estimated the FFR in vessel tree models reconstructed from intravascular ultrasound (IVUS) imaging with the side branches manually segmented, which can also be implemented with OCT imaging. Moreover, more simple approaches have been used to specify the flow fraction boundary conditions at the outlets according to morphometric flow rules, assuming the deviations in flow distribution (from the healthy state) are insignificant in the diseased arteries [15; 38], which is also seen in our study. The use of morphometric flow rules to correct the flow in the main vessel can serve as a reasonable approximation, which can be combined with previously suggested equation-based FFR estimation methods [5; 13] to improve their performance.