PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventionsReportar como inadecuado




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International Journal of Computer Assisted Radiology and Surgery

, Volume 10, Issue 6, pp 695–705

First Online: 07 April 2015Received: 20 February 2015Accepted: 20 March 2015

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 \p>0.05\, 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.

KeywordsRespiratory motion X-ray angiograms Principal component analysis Percutaneous coronary intervention  Download fulltext PDF



Autor: Hua Ma - Gerardo Dibildox - Carl Schultz - Evelyn Regar - Theo van Walsum

Fuente: https://link.springer.com/







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