[Key frames extraction and application in intravascular ultrasound pullback sequences based on manifold learning]

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.

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.

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.

The proposed algorithm is simple and robust, and the gating sequences can effectively reduce motion artifacts in IVUS longitudinal cuts.