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.