TY - GEN
T1 - Manifold learning for image-based gating of Intravascular Ultrasound(IVUS) pullback sequences
AU - Isguder, Gozde Gul
AU - Unal, Gozde
AU - Groher, Martin
AU - Navab, Nassir
AU - Kalkan, Ali Kemal
AU - Degertekin, Muzaffer
AU - Hetterich, Holger
AU - Rieber, Johannes
PY - 2010
Y1 - 2010
N2 - Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel structures. 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 inaccurate 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 corresponding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of important 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 pullbacks and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method.
AB - Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel structures. 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 inaccurate 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 corresponding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of important 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 pullbacks and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method.
KW - Classification
KW - ECG gating
KW - IVUS
KW - Image-based gating
KW - Manifold Learning
UR - http://www.scopus.com/inward/record.url?scp=78049420689&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15699-1_15
DO - 10.1007/978-3-642-15699-1_15
M3 - Conference contribution
AN - SCOPUS:78049420689
SN - 3642156983
SN - 9783642156984
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 139
EP - 148
BT - Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
T2 - 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Y2 - 19 September 2010 through 20 September 2010
ER -