Angiographic Parametric Imaging (API) is a tool based on the parametrization of Time-Density Curves (TDCs) from Digital Subtraction Angiography (DSA). Parameters derived from the TDCs correlate moderately with hemodynamics, yet underuse the hemodynamic information encoded in a TDC. To determine whether better diagnoses can be made through a more complete utilization of the information in the TDCs, we implemented an analysis using Recurrent Neural Networks (RNNs). These are a class of neural networks that analyze and make predictions using time sequences such as the TDCs. We investigated the feasibility of using RNNs to make treatment outcome predictions using TDCs obtained from angiograms of Intracranial Aneurysms (IAs) treated with Pipeline Embolization Devices (PED). Six-month follow-up angiograms were collected to create binary labels regarding treatment outcome (occluded/un-occluded). API parameters obtained were Mean Transit Time, Time to Peak, Time to Arrival, and Peak Height. Parameters were used to simulate TDCs which were normalized to account for variability between interventions. An RNN was trained and tested to predict IA treatment outcome. A 20-fold Monte Carlo Cross Validation was conducted to evaluate robustness of the RNN. The RNN predicted occlusion outcome of IAs with an average accuracy of 74.4% (95% CI, 72.6%-76.1%) and 65.6% (63.4%- 67.2%) and average area under the receiver operating characteristic curve of 0.73 (0.70-0.76) and 0.56 (0.51-0.61) for normalized and un-normalized sub-groups respectively. This study proves the feasibility of using RNNs to predict treatment outcome of IAs treated with a PED using TDCs simulated from temporal features obtained through API.
Purpose: Intracranial arteriovenous malformations (AVMs) are severe neurovascular diseases in which the arterial branches of an area of the brain communicate directly with venous circulation through a network of dilated vasculature (nidus) which significantly increases the risk of hemorrhage. Treatment plans typically incorporate direct embolization with liquid materials delivered via micro-catheters under fluoroscopy. Currently, the progression and success of this procedure are qualitatively evaluated using digitally subtracted angiographic (DSA) sequences. This study sought to validate the use of Angiographic Parametric Imaging (API) for quantitative analysis of the hemodynamic changes caused by embolization treatment using imaging biomarkers. Materials and Methods: 36 patients with AVMs were selected randomly from a list of patients with known symptoms at presentation. For each, at least one set of frontal and lateral angiograms were analyzed using API. Parametric maps were calculated for five imaging biomarkers, including time to peak (TTP), mean transit time (MTT), time to arrival (TTA), peak height (PH), and area under the curve (AUC). Regions of interest (ROIs) were selected over the feeding arteries, AVM nidus, and draining veins. Average ROI parameters were calculated and changes in flow due to embolization were quantified through a percent change analysis. Results were verified using correlation coefficients across AVM vasculature at multiple sites following normalization. Results: Frontal to lateral correlation coefficients; TTP, 0.54±0.07; MTT, 0.24±0.09; TTA 0.60±0.06; PH, 0.33±0.08; AUC, 0.22±0.09. Nidus to principle draining vein (PDV) correlation coefficients; TTP, 0.75±0.03; MTT, 0.64±0.04; TTA, 0.80±0.02; PH, 0.32±0.06; AUC, 0.68±0.04. PH and AUC values affected by DSA inversion. Conclusions: The study concludes that the API software is reliable in determining the flow parameters throughout the AVM, provided that the selected ROI is consistent between frontal and lateral scans and DSA selection is optimal.