Reconstruction of 3D vascular flow patterns from sparse angiographic data using a 3D Convolutional Neural Network (CNN)

Cerebral aneurysms (CAs) can have adverse consequences if ruptured, including severe disability or death. Minimally invasive endovascular procedures to treat cerebral aneurysms involve the intricate placement of coiling or flow diverters guided by 2D angiography; however, retreatment is often necessary. However, cerebral vasculature and the corresponding hemodynamics are highly three-dimensional, with complex 3D curvature and tortuosity. Current imaging techniques used during procedures are 2D projections that ‘flatten’ the complex flow characteristics. In this study, we developed a novel method for using three orthogonal angiographic image projections to reconstruct the 3D volumetric angiographic contrast transport in cerebral angiography from sparse angiographic data using a convolutional neural network. We utilize a large dataset of aneurysm geometries of patient-specific cerebral aneurysms and surrounding vasculature with complex morphologies and sizes. For each aneurysm, angiography was simulated using a transient computational fluid dynamic model quantifying blood and contrast flow characteristics. Virtual angiographic images were obtained using 2D projections of the gridded data in 3 orthogonal directions for 2,639 unique time frames. The 2D projected virtual angiograms and an anatomical mask were inputs to a custom-built model architecture. We developed the 3D Angiographic Reconstruction Neural Network as a 3D convolutional neural network that reconstructs contrast in 3D in a frame-by-frame approach. For the tested patient-specific models, the heterogeneous 3D nature of the contrast was identified, and the contrast transport through the vasculature was successfully reconstructed across successive time frames. The average mean square error across all the unseen test patients was 5.27E-04. Providing clinicians with additional information regarding 3D CA hemodynamics can improve the assessment and treatment of CAs.