Xiaochang Leng

Background: This study aimed to introduce a novel optical coherence tomography-derived fractional flow reserve (FFR) computational approach and assess the diagnostic performance of the algorithm for assessing physiological function. Methods: The fusion of coronary optical coherence tomography and angiography was used to generate a novel FFR algorithm (AccuFFRoct) to evaluate functional ischemia of coronary stenosis. In the current study, a total of 34 consecutive patients were included, and AccuFFRoct was used to calculate the FFR for these patients. With the wire-measured FFR as the reference standard, we evaluated the performance of our approach by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Per vessel accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRoct in identifying hemodynamically significant coronary stenosis were 93.8%, 94.7%, 92.3%, 94.7%, and 92.3%, respectively, were found. Good correlation (Pearson’s correlation coefficient r = 0.80, p < 0.001) between AccuFFRoct and FFR was observed. The Bland-Altman analysis showed a mean difference value of –0.037 (limits of agreement: –0.189 to 0.115). The area under the receiver-operating characteristic curve (AUC) of AccuFFRoct in identifying physiologically significant stenosis was 0.94, which was higher than the minimum lumen area (MLA, AUC = 0.91) and significantly higher than the diameter stenosis (%DS, AUC = 0.78). Conclusions: This clinical study shows the efficiency and accuracy of AccuFFRoct for clinical implementation when using invasive FFR measurement as a reference. It could provide important insights into coronary imaging superior to current methods based on the degree of coronary artery stenosis. Background: This study aimed to introduce a novel optical coherence tomography-derived fractional flow reserve (FFR) computational approach and assess the diagnostic performance of the algorithm for assessing physiological function. Methods: The fusion of coronary optical coherence tomography and angiography was used to generate a novel FFR algorithm (AccuFFRoct) to evaluate functional ischemia of coronary stenosis. In the current study, a total of 34 consecutive patients were included, and AccuFFRoct was used to calculate the FFR for these patients. With the wire-measured FFR as the reference standard, we evaluated the performance of our approach by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Per vessel accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRoct in identifying hemodynamically significant coronary stenosis were 93.8%, 94.7%, 92.3%, 94.7%, and 92.3%, respectively, were found. Good correlation (Pearson’s correlation coefficient r = 0.80, p < 0.001) between AccuFFRoct and FFR was observed. The Bland-Altman analysis showed a mean difference value of –0.037 (limits of agreement: –0.189 to 0.115). The area under the receiver-operating characteristic curve (AUC) of AccuFFRoct in identifying physiologically significant stenosis was 0.94, which was higher than the minimum lumen area (MLA, AUC = 0.91) and significantly higher than the diameter stenosis (%DS, AUC = 0.78). Conclusions: This clinical study shows the efficiency and accuracy of AccuFFRoct for clinical implementation when using invasive FFR measurement as a reference. It could provide important insights into coronary imaging superior to current methods based on the degree of coronary artery stenosis.

Background: Straightening of parent vessels happens for stent-assisted coiling embolization (SACE) treatment of intracranial aneurysms. This study aims to investigate aneurysmal hemodynamic modifications caused by stent-induced vessel straightening. Methods: Stent and coil deployments of a SACE-treated distal bifurcation aneurysm by finite element method were performed first with the preoperative (not straightened, NS) and postoperative (straightened, S) vessel models respectively. Computational fluid dynamics were then performed for eight models, including (I) NS only model, (II) NS+stent model, (III) NS+coils model, (IV) NS+stent+coils model, (V) S only model, (VI) S+stent model, (VII) S+coils model, and (VIII) S+stent+coils model. Finally, changes in aneurysmal flow velocity, isovelocity surface and wall shear stress (WSS) were analyzed qualitatively and quantitatively. Results: The flow was less in the S models than that in the corresponding NS models. Coils blocked most of the flow into the aneurysm sac in both NS models and S models and vessel straightening had more profound effect on the high aneurysmal flow volume reduction than coiling, while stenting generated adverse effect on flow reduction. Taking the NS only model as baseline (100%), the sac-averaged velocities of models II to VIII were 112%, 36%, 42%, 45%, 39%, 12%, 13%, and high flow volumes were 119%, 21%, 30%, 10%, 8%, 3%, 3%, while the sac-averaged WSSs were 106%, 37%, 44%, 41%, 35%, 17% and 24%, respectively. Conclusions: Stent-induced vessel straightening combined coil embolization has the best performance in hemodynamic modifications and may reduce the recurrence rate, whereas stenting may generate adverse effect on hemodynamic alterations.

In this study, we develop reliable and practical virtual coiling and stenting methods for intracranial aneurysm surgical planning. Since the purpose of deploying coils and stents is to provide device geometries for subsequent accurate post-treatment computational fluid dynamics analysis, we do not need to accurately capture all the details such as the stress and force distribution for the devices and vessel walls. Our philosophy for developing these methods is to balance accuracy and practicality. We consider the mechanical properties of the devices and recapitulate the clinical practice using a finite element method (FEM) approach. At the same time, we apply some simplifications for FEM modeling to make our methods efficient. For the virtual coiling, the coils are modeled as 3D Euler–Bernoulli beam elements, which is computationally efficient and provides good geometry representation. During the stent deployment process, the stent–catheter system is transformed according to the centerline of the parent vessel since the final configuration of the stent is not dependent of the deployment history. The aneurysm and vessel walls are assumed to be rigid and are fully constrained during the simulation. All stent–catheter system and coil–catheter system are prepared and packaged as a library which contains all types of stents, coils and catheters, which improves the efficiency of surgical planning process. The stent was delivered to the suitable position during the clinical treatment, achieving good expansion and apposition of the stent to the arterial wall. The coil was deployed into the aneurysm sac and deformed to different shapes because of the stored strain energy during coil package process and the direction of the microcatheter. The method which we develop here could become surgical planning for intracranial aneurysm treatment in the clinical workflow.