Similarity metric learning for 2D to 3D registration of brain vasculature

Abstract

2D to 3D image registration techniques are useful in the treatment of neurological diseases such as stroke. Image registration can aid physicians and neurosurgeons in the visualization of the brain for treatment planning, provide 3D information during treatment, and enable serial comparisons. In the context of stroke, image registration is challenged by the occluded vessels and deformed anatomy due to the ischemic process. In this paper, we present an algorithm to register 2D digital subtraction angiography (DSA) with 3D magnetic resonance angiography (MRA) based upon local point cloud descriptors. The similarity between these local descriptors is learned using a machine learning algorithm, allowing flexibility in the matching process. In our experiments, the error rate of 2D/3D registration using our machine learning similarity metric (52.29) shows significant improvement when compared to a Euclidean metric (152.54). The proposed similarity metric is versatile and could be applied to a wide range of 2D/3D registration.