Intravascular ultrasound (IVUS) is widely used in coronary artery examination. Ultrasonic elastography combined with IVUS is very conspicuous in identifying plaque component and in detecting plaque vulnerability degree. In this study, a simulation model of the blood vessel based on finite element analysis (FEA) was established. The vessel walls generally have radial changes caused by different intravascular pressure. The signals at lower pressures were used as the pre-deformation data and the signals at higher pressure were used as the post-deformation data. Displacement distribution was constructed using the time-domain cross-correlation method, and then strain images. By comparison of elastograms under different pressures, we obtained the optimal pressure step. Furthermore, on the basis of the obtained optimize pressure step, the simulation results showed that this method could effectively distinguish characteristics between different component plaques, and could guide the later experiments and clinical applications.
In this paper, we propose an image-based key frame gating method to reduce motion artifacts in intravascular ultrasound (IVUS) longitudinal cuts. The artifacts are mainly caused by the periodic relative displacement between blood vessels and the IVUS catheter due to cardiac motion. The method is achieved in four steps as following. Firstly, we convert IVUS image sequences to polar coordinates to cut down the amount of calculation. Secondly, we extracted a one-dimensional signal cluster reflecting cardiac motion by spectral analysis and filtering techniques. Thirdly, we designed a Butterworth band-pass filter for filtering the one-dimensional signal clusters. Fourthly, we retrieved the extremes of the filtered signal clusters to seek key frames to compose key-frames gated sequences. Experimental results showed that our algorithm was fast and the average frame processing time was 17ms. Observing the longitudinal viewpictures, we found that comparing to the original ones, the gated sequences had similar trend, less saw tooth shape, and good continuity. We selected 12 groups of clinical IVUS sequences[images (876±65 frames), coronary segments length (14.61±1.08 mm)] to calculate vessel volume, lumen volume, mean plaque burden of the original and gated sequences. Statistical results showed that, on one hand, both vessel volume and lumen volume measured of the gated sequences were significantly smaller than those of the original ones, and there was no significant difference on mean plaque burden between original and gated sequences, which met the need of the clinical diagnosis and treatment. On the other hand, variances of vessel area and lumen area of the gated sequences were significantly smaller than those of the original sequences, indicating that the gated sequences would be more stable than the original ones.
Quantitative measurement of strain distribution of arterial vessel walls due to pulsatile blood flow within the vascular lumen is valuable for evaluating the elasticity of arterial wall and predicting the evolution of plaques. The present paper shows that the three-dimensional (3D) strain distribution are estimated through uni-directional coupling for 3D vessel and blood models reconstructed from intravascular ultrasound (IVUS) images with the computational fluid dynamics (CFD) numerical simulation technique. The morphology of vessel wall and plaques as well as strain distribution can be visually displayed with pseudo-color coding.
Automated characterization of different vessel wall tissues including atherosclerotic plaques, branchings and stents from intravascular ultrasound (IVUS) gray-scale images was addressed. The texture features of each frame were firstly detected with local binary pattern (LBP), Haar-like and Gabor filter in the present study. Then, a Gentle Adaboost classifier was designed to classify tissue features. The methods were validated with clinically acquired image data. The manual characterization results obtained by experienced physicians were adopted as the golden standard to evaluate the accuracy. Results indicated that the recognition accuracy of lipidic plaques reached 94.54%, while classification precision of fibrous and calcified plaques reached 93.08%. High recognition accuracy can be reached up to branchings 93.20% and stents 93.50%, respectively.