Virtual Single-frame Subtraction Imaging

We outline a generic framework for single-frame, detector-domain material decomposition. The method involves a segmentation and a background estimation step yielding a virtual mask image that can be used for subtraction. In many cases, material decomposition yields non-truncated difference images enabling the use of novel motion estimation methods that exploit epipolar consistency conditions. In this work, a pipeline for virtual digital subtraction coronary angiography is presented and evaluated on a phantom data set. The pipeline consists of Hessian-based vessel segmentation followed by background estimation in Fourier domain. Center of mass tracking and a metric based on epipolar consistency conditions is then used to estimate vertical detector translations that serve as a surrogate for respiratory and cardiac motion. When assessing the heart phase, we achieved a correlation of 0.91 between the ground truth ECG and the image-based surrogates. The results encourage further experiments on real data as well as the application for intra-scan motion compensation.