Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent
  Concentration Reconstruction
Noah Maul Annette Birkhold Fabian Wagner Mareike Thies Maximilian Rohleder Philipp Berg Markus Kowarschik Andreas Maier
Three-dimensional Digital Subtraction Angiography (3D-DSA) is a
well-established X-ray-based technique for visualizing vascular anatomy.
Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been
developed to enable the visualization of volumetric contrast flow dynamics
through time-series of volumes. . This reconstruction problem is ill-posed
mainly due to vessel overlap in the projection direction and geometric vessel
foreshortening, which leads to information loss in the recorded projection
images. However, knowledge about the underlying fluid dynamics can be leveraged
to constrain the solution space. In our work, we implicitly include this
information in a neural network-based model that is trained on a dataset of
image-based blood flow simulations. The model predicts the spatially averaged
contrast agent concentration for each centerline point of the vasculature over
time, lowering the overall computational demand. The trained network enables
the reconstruction of relative contrast agent concentrations with a mean
absolute error of 0.02 $\pm$ 0.02 and a mean absolute percentage error of 5.31
% $\pm$ 9.25 %. Moreover, the network is robust to varying degrees of vessel
overlap and vessel foreshortening. Our approach demonstrates the potential of
the integration of machine learning and blood flow simulations in time-resolved
angiographic flow reconstruction.