PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions

Abstract

Purpose: Intraoperative coronary motion modeling with motion surrogates enables prospective motion prediction in X-ray angiograms (XA) for percutaneous coronary interventions. The motion of coronary arteries is mainly affected by patients breathing and heartbeat. Purpose of our work is therefore to extract coronary motion surrogates that are related to respiratory and cardiac motion. In particular, we focus on respiratory motion surrogates extraction in this paper.

Methods: We propose a fast automatic method for extracting patient-specific respiratory motion surrogate from cardiac XA. The method starts with an image preprocessing step to remove all tubular and curvilinear structures from XA images, such as vessels and guiding catheters, followed by principal component analysis on pixel intensities. The respiratory motion surrogate of an XA image is then obtained by projecting its vessel-removed image onto the first principal component.

Results: This breathing motion surrogate was demonstrated to get high correlation with ground truth diaphragm motion (correlation coefficient over 0.9 on average). In comparison with other related methods, the method we developed did not show significant difference (p > 0.05), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.

Conclusions: we developed and evaluated a method in extraction of respiratory motion surrogate from interventional X-ray images that is easy to implement and runs in real time and thus allows extracting respiratory motion surrogates during interventions.

Fig. 1

Morphological closing operation on an XA image. a Original XA. b Image processed with morphological closing: guiding catheter and coronary arteries are removed

Fig. 2

Ground truth data. a Drawing a rectangular ROI on diaphragm border. b Sagittal view of ROI. c Manual labeling of diaphragm border

Fig. 3

Retrospective projection on the first principal component for one patient, in comparison with diaphragm position in ROI image. a, b PCA was done on one sequence of the biplane data; c–f principal components were derived from the concatenated sequence of both planes. c, d show the projection in comparison with plane A; e, f illustrate the comparison with plane B

Fig. 4

Boxplot of correlation coefficients calculated using various methods on monoplane and biplane data for retrospective evaluation

Fig. 5

Frequency distribution of correlation coefficients for various methods in retrospective analysis

Fig. 6

Prospective projection on the first principal component for one patient, in comparison with diaphragm position in ROI image. a, b PCA was implemented on monoplane data; c–f principal components were derived from the concatenated sequence of biplane. c, d show the projection in comparison with plane A; e, f illustrate the comparison with plane B

Fig. 7

Boxplot of correlation coefficients calculated using various methods on monoplane and biplane data for prospective evaluation

Fig. 8

Frequency distribution of correlation coefficients for various methods in prospective analysis

Fig. 9

The first four principal components (eigenimages) of images with and without diaphragm being present. The first two rows of images have diaphragm and the last two rows do not. The first and third row show the original images and their eigenimages, and the second and fourth row are the same images in the first and third row after morphological closing operation