Angiographic analysis for phantom simulations of endovascular aneurysm treatments with a new fully retrievable asymmetric flow diverter

Recovery of complete time density curves from incomplete angiographic data using recurrent neural networks

Quantitative angiography is a 2D/3D x-ray imaging modality that summarizes hemodynamic information using time density curve (TDC) based parameters. Estimation of the TDC parameters are susceptible to errors due to various factors including, patient motion, incomplete temporal data, imaging trigger errors etc. In this study, we tested the feasibility of using recurrent neural networks (RNN) to recover complete TDC temporal information from incomplete sequences and evaluate quantitative parameters generated from the corrected TDCs. Digital subtraction angiograms (DSAs) were collected from patients undergoing endovascular treatments and angiographic parametric imaging (API) parameters were calculated from each DSA. Each set of API parameters was used to simulate a TDC resulting in a dataset of 760 TDCs. One-third of each TDC was continuously masked from pseudo-random points past the peak height (PH) point to simulate missing/artifact information. An RNN was developed, trained and tested to generate completed/corrected TDCs. The RNN recovered complete TDC temporal information with an average mean squared error of 0.0086±0.002. Average mean absolute errors were calculated between each API parameter generated from the ground truth TDCs and RNN corrected TDCs, these were 11.02%±0.91 for time to peak, 10.97%±0.69 for mean transit time, 5.65%±0.76 for PH, and 15.08%±0.98 for area under the TDC. The change in API parameters was not clinically significant and the predictive power of the API parameters was retained. This study proved the feasibility of using RNNs to mitigate motion artifacts and incomplete angiographic acquisitions to extract accurate quantitative parameters.

In vitro angiographic comparison of the flow-diversion performance of five neurovascular stents

Background and purpose Data differentiating flow diversion properties of commercially available low- and high-porosity stents are limited. This in vitro study applies angiographic analysis of intra-aneurysmal flow to compare the flow-diversion performance of five neurovascular devices in idealized sidewall and bifurcation aneurysm models. Methods Five commercial devices (Enterprise, Neuroform, LVIS, FRED, and Pipeline) were implanted in silicone sidewall and bifurcation aneurysm models under physiological average flow of blood analog fluid. High-speed angiographic images were acquired pre- and post-device implantation and contrast concentration-time curves within the aneurysm were recorded. The curves were quantified with five parameters to assess changes in contrast transport, and thus aneurysm hemodynamics, due to each device. Results Inter-device flow-diversion performance was more easily distinguished in the sidewall model than the bifurcation model. There were no obvious overall statistical trends in the bifurcation parameters but the Pipeline performed marginally better than the other devices. In the sidewall geometry, overall evidence suggests that the LVIS performed better than the Neuroform and Enterprise. The Pipeline and FRED devices were statistically superior to the three stents and Pipeline was superior to FRED in all sidewall parameters evaluated. Conclusions Based on this specific set of experiments, lower-porosity flow diverters perform significantly better in reducing intra-aneurysmal flow activity than higher-porosity stents in sidewall-type geometries. The LVIS device is potentially a better flow diverter than the Neuroform and Enterprise devices, while the Pipeline is potentially better than the FRED.

Sensitivity evaluation of DSA-based parametric imaging using Doppler ultrasound in neurovascular phantoms

An evaluation of the relation between parametric imaging results obtained from Digital Subtraction Angiography (DSA) images and blood-flow velocity measured using Doppler ultrasound in patient-specific neurovascular phantoms is provided. A silicone neurovascular phantom containing internal carotid artery, middle cerebral artery and anterior communicating artery was embedded in a tissue equivalent gel. The gel prevented movement of the vessels when blood mimicking fluid was pumped through it to obtain Colour Doppler images. The phantom was connected to a peristaltic pump, simulating physiological flow conditions. To obtain the parametric images, water was pumped through the phantom at various flow rates (100, 120 and 160 ml/min) and 10 ml contrast boluses were injected. DSA images were obtained at 10 frames/sec from the Toshiba C-arm and DSA image sequences were input into LabVIEW software to get parametric maps from time-density curves. The parametric maps were compared with velocities determined by Doppler ultrasound at the internal carotid artery. The velocities measured by the Doppler ultrasound were 38, 48 and 65 cm/s for flow rates of 100, 120 and 160 ml/min, respectively. For the 20% increase in flow rate, the percentage change of blood velocity measured by Doppler ultrasound was 26.3%. Correspondingly, there was a 20% decrease of Bolus Arrival Time (BAT) and 14.3% decrease of Mean Transit Time (MTT), showing strong inverse correlation with Doppler measured velocity. The parametric imaging parameters are quite sensitive to velocity changes and are well correlated to the velocities measured by Doppler ultrasound.