We developed a new method for automated detection of microchannel in intravascular optical coherence tomography images. The proposed method includes three main steps including pre-processing, identification of microchannel candidates, and classification of microchannel. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. Our method has great potential to enable highly automated, objective, repeatable, and comprehensive evaluations of vulnerable plaques and treatments. We believe that this method is promising for both research and clinical applications.
Intravascular optical coherence tomography (IVOCT) images have the necessary resolution and contrast to assess atherosclerotic plaque, and our group has created machine/deep learning techniques to analyze plaques in pre-stent IVOCT images. Such software can be used to aid numerous research studies and clinical treatment planning. Here we extend our work to images obtained following stent implantation, where opaque metallic stent struts obstruct the view of portions of the plaque, making it difficult to visualize/analyze plaques. We created a generative adversarial network (GAN)-based method to fill in the missing image data. Applications include analysis of plaque progression/regression behind the stent to evaluate drugs, drug eluting stents, and the anatomical registration of pre- and post-stent image data for further post stent optimization. We used conditional GAN (cGAN) to correct the presence of the guide wire and stent struts shadows, creating full images for visualization and analyses. To train/test software, we created synthetic post-stent images by interjecting realistic stent strut shadows in images without a stent in place. Results show the capability of cGAN to generate plausible and realistic images. To assess results, we used deep learning segmentation models to segment calcifications in corrected, synthesized images and compared results to the original images with stent. DICE scores were typically above 0.79 ± 0.03 with correction and 0.71 ± 0.02 without correction. The co-registration errors improved to be ranged between 0.52 mm to 0.69 mm. Compared with images with shadows, the new method offers lower errors in both location and orientation registration.
Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary calcifications and detailed measurements of acute stent deployment following stent implantation. Since pre- and post-stent IVOCT image “pull-back” acquisitions start from different locations, registration of corresponding pullbacks is needed for assessing treatment outcomes. In particular, we are interested in assessing finite element model (FEM) prediction of lumen gain following stenting, requiring registration. We used deep learning to segment calcifications in corresponding pre- and poststent IVOCT pullbacks. We created 1D representations of calcium thickness as a function of the angle of the helical IVOCT scans. Registration of two scans was done by maximizing the cross correlation of these two 1D representations. Registration was accurate, as determined by visual comparisons of 2D image frames. We used our pre-stent calcification segmentations to create a lesion-specific FEM, which took into account balloon size, balloon pressure, and stent measurements. We then compared simulated lumen gain from FEM analysis to actual stent deployment results. Actual lumen gain across ~200 registered pre and post-stent images was 1.52 ± 0.51, while FEM prediction was 1.43 ± 0.41. Comparison between actual and FEM results showed no significant difference (p < 0.001), suggesting accurate prediction of FEM modeling. Registered image data showed good visual agreement regarding lumen gain and stent strut malapposition. Hence, we have developed a platform for evaluation of FEM prediction of lumen gain. This platform can be used to guide development of FEM prediction software, which could ultimately help physicians with stent treatment planning of calcified lesions.
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