Abstract: Objective We propose an image-based key frames gating method for intravascular ultrasound (IVUS) sequence
based on manifold learning to reduce motion artifacts in IVUS longitudinal cuts. Methods We achieved the gating with
Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the
high-dimensional image space. A distance function was constructed by the low-dimensional feature vectors to reflect the heart
movement. The IVUS images were classified as end-diastolic and non-end-diastolic based on the distance function, and the
IVUS images collected in end-diastolic stage constitutes the key frames gating sequences. Result We tested the algorithm on 13
in vivo clinical IVUS sequences (images 915 ± 142 frames, coronary segments length 15.24 ± 2.37 mm) to calculate the vessel
volume, lumen volume, and the mean plaque burden of the original and gated sequences. Statistical results showed that both
the vessel volume and lumen volume measured from the gated sequences were significantly smaller than the original ones,
indicating that the gated sequences were more stable; the mean plaque burden was comparable between the original and gated
sequences to meet the need in clinical diagnosis and treatment. In the longitudinal views, the gated sequences had less saw
tooth shape than the original ones with a similar trend and a good continuity. We also compared our method with an existing
gating method. Conclusion The proposed algorithm is simple and robust, and the gating sequences can effectively reduce
motion artifacts in IVUS longitudinal cuts.