Can we assess brain perfusion on a regional basis from digital subtraction angiography images with motion artifacts?

A method called IPEN (for Intra-operative PErfusion assessment with No gantry rotation) has been developed that uses x-ray angiography images and assesses volumetric soft tissue perfusion on a regional basis. It has the potential to enable quantitative cross-sectional perfusion assessment before, during, and after interventional procedures, such that physicians can assess the risk and outcome of the procedure. However, the current IPEN will not function if there is no native (non-subtracted) x-ray angiography images available and digital subtraction angiography (DSA) images are corrupted by motion artifacts.

To demonstrate that region-based tissue enhancement can be extracted from DSA images even if they are contaminated by motion artifacts.

We developed a motion-compensating IPEN framework using DSA images (called MCI-D) and validated its core formulation. A key breakthrough enabling MCI-D was the finding that, even when DSA images contain motion artifacts, subtracting two DSA images can isolate the change in tissue enhancement between the images if motion is absent between the two times.

A qualitative assessment using computer simulated XCAT perfusion phantom data validated the finding, and clinical data supported the feasibility. An extensive, systematic, and quantitative assessment showed that MCI-D was highly correlated to the ground truth (e.g., the correlation coefficients > 0.99 for both the time-enhancement curve and the blood flow perfusion index, maximum slope of the rising part of the time-enhancement curve).

MCI-D enables accurate motion-compensated perfusion assessment from DSA images, even in the presence of severe motion artifacts, provided that the motion is not continuous and occurs within a short period of time.