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Simultaneous Registration of Location and Orientation in Intravascular Ultrasound Pullbacks Pairs Via 3D Graph-Based Optimization

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Date 2015 Jun 17
PMID 26080381
Citations 6
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Abstract

A novel method is reported for simultaneous registration of location (axial direction) and orientation (circumferential direction) of two intravascular ultrasound (IVUS) pullbacks of the same vessel taken at different times. Monitoring plaque progression or regression (e.g., during lipid treatment) is of high clinical relevance. Our method uses a 3D graph optimization approach, in which the cost function jointly reflects similarity of plaque morphology and plaque/perivascular image appearance. Graph arcs incorporate prior information about temporal correspondence of the two IVUS sequences and limited angular twisting between consecutive IVUS images. Additionally, our approach automatically identifies starting and ending frame pairs in the two IVUS pullbacks. Validation of our method was performed in 29 pairs of IVUS baseline/follow-up pullback sequences consisting of 8 622 IVUS image frames in total. In comparison to manual registration by three experts, the average location and orientation registration errors ranged from 0.72 mm to 0.79 mm and from 7.3(°) to 9.3(°), respectively, all close to the inter-observer variability with no difference being statistically significant (p = NS). Rotation angles determined by our automated approach and expert observers showed high correlation (r(2) of 0.97 to 0.98) and agreed closely (mutual bias between the automated method and expert observers ranged from -1.57(°) to 0.15(°)). Compared with state-of-the-art approaches, the new method offers lower errors in both location and orientation registration. Our method offers highly automated and accurate IVUS pullback registration and can be employed in IVUS-based studies of coronary disease progression, enabling more focal studies of coronary plaque development and transition of vulnerability.

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References
1.
Bland J, Altman D . Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1(8476):307-10. View

2.
Asakura T, Karino T . Flow patterns and spatial distribution of atherosclerotic lesions in human coronary arteries. Circ Res. 1990; 66(4):1045-66. DOI: 10.1161/01.res.66.4.1045. View

3.
Felzenszwalb P, Zabih R . Dynamic programming and graph algorithms in computer vision. IEEE Trans Pattern Anal Mach Intell. 2010; 33(4):721-40. PMC: 3717380. DOI: 10.1109/TPAMI.2010.135. View

4.
Zikic D, Glocker B, Kutter O, Groher M, Komodakis N, Kamen A . Linear intensity-based image registration by Markov random fields and discrete optimization. Med Image Anal. 2010; 14(4):550-62. DOI: 10.1016/j.media.2010.04.003. View

5.
Sun S, Sonka M, Beichel R . Graph-based IVUS segmentation with efficient computer-aided refinement. IEEE Trans Med Imaging. 2013; 32(8):1536-49. PMC: 3883441. DOI: 10.1109/TMI.2013.2260763. View