Initial study of the radiomics of intracranial aneurysms using Angiographic Parametric Imaging (API) to evaluate contrast flow changes

Abstract

Purpose: The purpose of this study is to apply targeted Parametric Imaging on aneurysms to quantitatively investigate contrast flow changes at pre-, post-treatment and follow-up with outcome scoring. Methods: The angiograms for 50 patients were acquired, 25 treated with coil embolization and 25 treated using a flow diverter. API was performed by synthesizing the time density curve (TDC) at every pixel. Based on the TDCs, we calculated various parameters for the quantitative characterization of contrast flow through the vascular network and aneurysms and displayed them using color encoded maps. The parameters included were : Time to Peak (TTP), Mean Transit Time (MTT), Time of Arrival (TTA), Peak Height (PH) and Area Under the Curve (AUC). Two Regions of Interest (ROI) were manually marked over the aneurysm dome and the main artery. Average aneurysm parameter values were normalized to those values recorded in the main artery and recorded pre-/post-treatment and follow-up and compared to Raymond Roy scores and flow diverter stent scoring. Results: The normalized mean values were as follows (pre and post treatment): TTP (1.09+/-0.14, 1.55+/-1.36), MTT (1.07+/-0.23, 1.27+/-0.42), TTA (0.14+/-0.15, 0.26+/-0.23), PH (1.2+/-0.54, 0.95+/-0.83) and AUC (1.29+/-0.69, 1.44+/- 1.92). The neural network gave a validation accuracy of 0.8036 with a loss of 0.0927. A receiver operating characteristic curve with an AUC of 0.866 was obtained. Conclusions: API can quantitatively describe the flow in the aneurysm for initial investigation of the radiomics of intracranial aneurysms. It also shows a clear demarcation between pre and post treatment. Statistical modelling and a machine learning network is used to prove the success of our model.