Peripheral arterial disease treatment planning using noninvasive and invasive imaging methods

Preclinical evaluation of vascular closure devices

Vascular closure devices (VCDs) are a diverse class of cardiovascular devices intended to achieve hemostasis following arteriotomy in the common femoral artery for diagnostic and therapeutic interventional procedures. While the preclinical evaluation of VCDs parallel that of many other cardiovascular devices, there are device-specific nuances and model-specific technical considerations in assessing in vivo performance and handling, determining safety, and satisfying regulatory requirements. Despite the multi-decade use and continued development of novel VCD technologies, there is a paucity of published literature on their preclinical evaluation. This review intends to help mitigate this gap through a discussion of conventional animal models, their attributes and limitations, and standards in the in vivo assessment of performance and safety of VCDs.

Comparison of Duplex Ultrasound and Digital Subtraction Angiography for Assessing Tibial Vessel Disease

Background Duplex ultrasonography (DUS) is readily available and often used as the first diagnostic test for patients with peripheral artery diseases (PADs). PAD is a disease that affects the general population but more commonly affects diabetics. To date, the role of DUS in the assessment of tibial vessel disease is inconclusive at best. The goal of our study is to assess the validity of DUS in characterizing the presence and severity of tibial diseases via comparison with digital subtraction angiography (DSA) findings. Methods This is a single-center retrospective cohort study analyzing three arterial segments (anterior tibial, posterior tibial, and fibular arteries) in patients who received a duplex study followed by DSA within a 30-day period. All arterial segments were graded from normal (Grade 0) to occluded (Grade 4), based on duplex interpretation and directly compared to direct visualization findings from DSA. Using statistical methods, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of DUS were determined. Results A total of 171 tibial vessel segments from 57 enrolled subjects with critical limb ischemia symptoms were analyzed in this study. The agreement between both modalities was poor (Kappa=0.19, p < 0.05), with DUS demonstrating a significant underestimation of vessel pathologies. This is also reflected by the overall sub-optimal sensitivity (23%), specificity (84%), PPV (69%), and NPV (41%) in DUS when compared to DSA results as the gold standard. Conclusion Significant disagreements were noted in this study between DUS and DSA findings, primarily significant underestimation of tibial vessel disease by the DUS when compared with the DSA. Caution is advised in the clinical application of DUS in patients with chronic limb-threatening ischemia (CLTI) symptoms and multi-segment tibial vessels due to its demonstrated limitations in this study.

Loss of c-Kit in Endothelial Cells Protects against Hindlimb Ischemia

BACKGROUND

Critical limb ischemia (CLI) is the end stage of peripheral artery disease (PAD), and around 30% of CLI patients are ineligible for current treatments. The angiogenic benefits of c-Kit have been reported in the ischemia scenario; however, the present study demonstrates the effects of specific endothelial c-Kit signaling in arteriogenesis during hindlimb ischemia.

METHODS

We created conditional knockout mouse models that decrease c-Kit (c-Kit VE-Cadherin CreERT2-c-Kit) or its ligand (SCF VE-Cadherin CreERT2-SCF) specifically in endothelial cells (ECs) after tamoxifen treatment. These mice and a control group (wild-type VE-Cadherin CreERT2-WT) were subjected to hindlimb ischemia or aortic crush to evaluate perfusion/arteriogenesis and endothelial barrier permeability, respectively.

RESULTS

Our data confirmed the lower gene expression of c-Kit and SCF in the ECs of c-Kit and SCF mice, respectively. In addition, we confirmed the lower percentage of ECs positive for c-Kit in c-Kit mice. Further, we found that c-Kit and SCF mice had better limb perfusion and arteriogenesis compared to WT mice. We also demonstrated that c-Kit and SCF mice had a preserved endothelial barrier after aortic crush compared to WT.

CONCLUSIONS

Our data demonstrate the deleterious effects of endothelial SCF/c-Kit signaling on arteriogenesis and endothelial barrier integrity.

Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images

Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. We present and discuss two example applications, modeling the lower extremities of artery-vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA). Beyond applications in medical diagnosis, blood-flow simulation and vessel-phantom design, the method can serve as a tool for automated annotation of image datasets to train machine-learning algorithms.