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Manifold learning for image-based gating of intravascular ultrasound(IVUS) pullback sequences

İşgüder, Gözde Gül and Ünal, Gözde and Groher, Martin and Navab, Nassir and Kalkan, Ali Kemal and Değertekin, Muzaffer and Hetterich, Holger and Rieber, Johannes (2010) Manifold learning for image-based gating of intravascular ultrasound(IVUS) pullback sequences. In: 5th International Workshop on Medical Imaging and Augmented Reality (MIAR 2010), Beijing, China

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Official URL: http://dx.doi.org/10.1007/978-3-642-15699-1_15

Abstract

Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel struc- tures. The IVUS frames are acquired by pulling the catheter back with a motor running at a constant speed. However, during the pullback, some artifacts occur due to the beating heart. These artifacts cause inaccu- rate measurements for total vessel and lumen volume and limitation for further processing. Elimination of these artifacts are possible with an ECG (electrocardiogram) signal, which determines the time interval cor- responding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of impor- tant information about the vessel, and furthermore, ECG gating function may not be available in all clinical systems. To address this problem, we propose an image-based gating technique based on manifold learning. Quantitative tests are performed on 3 different patients, 6 different pull- backs and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method.

Item Type:Papers in Conference Proceedings
Uncontrolled Keywords:Manifold Learning, Classification, IVUS, Image-based gating, ECG gating
Subjects:Q Science > QA Mathematics > QA075 Electronic computers. Computer science
ID Code:15170
Deposited By:Gözde Ünal
Deposited On:23 Nov 2010 16:09
Last Modified:23 Mar 2011 09:38

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