Spatiotemporally constrained 3D reconstruction from biplanar digital subtraction angiography

Our goal is to reconstruct 3D cerebral vessels from two 2D digital subtraction angiography (DSA) images acquired using a biplane scanner. This could provide intraoperative 3D imaging with 2-5 × spatial and 20 × temporal resolution of 3D magnetic resonance angiography, computed tomography angiography (CTA), or rotational DSA. Because many interventional radiology suites have biplane scanners, our method could be easily integrated into clinical workflows.

We present a constrained 3D reconstruction method that utilizes vessel centerlines, radii, and the flow of contrast agent through vessels from DSA. The reconstructed volume samples 'vesselness' at each voxel, i.e., its probability of containing a vessel. We present evaluation metrics which we used to optimize reconstruction parameters and evaluate our method on synthetic data. We provide preliminary results on clinical data. To handle clinical data, we developed a software tool for extracting vessel centerlines, radii, and contrast arrival times from clinical DSA. We provide an automated method for registering DSA to CTA which allows us to compare reconstructed vessels with vessels extracted from CTA.

Our method reduced reconstruction artifacts in vesselness volumes for both synthetic and clinical data. In synthetic DSA, where 3D ground-truth vessel centerlines are available, our constrained reconstruction method improved accuracy, selectivity, and Dice scores with two views compared to existing sparse reconstruction methods with up to 16 views.

Incorporating additional constraints into 3D reconstruction can successfully reduce artifacts introduced when a complex 3D structure like the brain vasculature is reconstructed from a small number of 2D views.