A stretching transform‐based automatic nonrigid registration system for cerebrovascular digital subtraction angiography images

Abstract

In the cerebrovascular digital subtraction angiography (DSA), patient motion is the primary cause of image quality degradation. In this article, we describe a nonrigid image registration system for motion artifact reduction in DSA which is fully automatic, effective, and computationally very efficient. In this system, the mask image is partitioned to generate the appropriate control points. The energy of histogram of differences method is adopted as similarity measurement, and the Powell algorithm is utilized for acceleration. A forward stretching transformation is used to complete the motion estimation, and an inverse stretching transformation is used to generate the target image by pixel mapping strategy. The method of this system is effective to maintain the topological relationships of the gray value before and after the image deformation. Preliminary experiments on the cerebrovascular DSA images illustrate the applicability of the system. After the deformation, the mask image remains clear and accurate contours, and the quality of the subtraction image after the registration is favorable.

AngioMoCo: Learning-Based Motion Correction in Cerebral Digital Subtraction Angiography

“…Over the last three decades, various motion correction techniques have been proposed to mitigate the impact of body motion retrospectively [18]. Registration algorithms typically employ template matching with corresponding control points or landmarks to align images [3,4,[6][7][8][9][10]16,17,19,22,[26][27][28]. These algorithms rely on features based on vessels [8], edges [9,17,19,28], corners [30], textures [20], temporal correspondence [3], and non-uniform grids [27].…”