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

Abstract

PurposeIntraoperative 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.MethodsWe 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.ResultsThis 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 (), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.Conclusionswe 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.

Unsupervised Learning for Robust Respiratory Signal Estimation From X-Ray Fluoroscopy

“…The most basic method is the application of PCA to the images. This is related to the approach of Ma et al, except that we do not apply morphological closing before PCA because our images do not contain coronaries [19]. It is also similar to the methods of Panayiotou et al, but without focus on tubular structures [18].…”

“…For an image of size D u × D v , the dimension of the vectorized representation is D y = D u · D v . Potentially, the images are processed [19] or masks are applied [18] before vectorization. A mapping, specific to the C-arm angulation, the table position, and the patient, is learned from the image data itself in an unsupervised manner.…”

“…Due to the sign ambiguity of dimensionality reduction, this is not guaranteed automatically. The issue of global sign is commonly neglected in the literature [14], [15], [17], [18], [19], [25], [26], with the exception of [16], because it does not affect the commonly used correlation measures for evaluating algorithm performance. To determine the sign, we use a heuristic that is based on the intuition that more time is spent in exhalation than in inhalation [16].…”

“…The above methods have some built-in assumptions about the X-ray images that are often violated. In recent years, manifold learning has been the dominating paradigm for respiratory signal extraction from medical images, for example in MR [14], ultrasound [14], SPECT [15], [16], and X-ray [17], [18], [19]. In the domain of X-ray fluoroscopy, Fischer et al applied incremental manifold learning to extract the respiratory signal in realtime [17].…”

“…However, it is designed and evaluated only for retrospective gating, i.e., its performance is unclear for realtime motion compensation. In contrast to [18], Ma et al suppressed tubular structures before applying principal component analysis (PCA) [19], and showed that it gives better results.…”