Gharaibeh Yazan, Lee Juhwan, Prabhu David, Dong Pengfei, Zimin Vladislav N, Dallan Luis A, Bezerra Hiram, Gu Linxia, Wilson David
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA 44106.
Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA 68588.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11317. doi: 10.1117/12.2550212. Epub 2020 Feb 28.
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 post-stent 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.
血管内光学相干断层扫描（IVOCT）可提供冠状动脉钙化的高分辨率图像，并在支架植入后对急性支架展开情况进行详细测量。由于支架植入前和植入后的IVOCT图像“回撤”采集是从不同位置开始的，因此需要对相应的回撤进行配准，以评估治疗效果。特别是，我们有兴趣评估支架植入后管腔增益的有限元模型（FEM）预测，这需要进行配准。我们使用深度学习对支架植入前和植入后的IVOCT回撤中的钙化进行分割。我们创建了钙厚度的一维表示，作为螺旋IVOCT扫描角度的函数。通过最大化这两个一维表示的互相关来完成两次扫描的配准。通过二维图像帧的视觉比较确定，配准是准确的。我们使用支架植入前钙化分割创建了一个病变特异性有限元模型，该模型考虑了球囊大小、球囊压力和支架测量值。然后，我们将有限元分析模拟得到的管腔增益与实际支架展开结果进行比较。在约200对配准的支架植入前和植入后图像中，实际管腔增益为1.52±0.51，而有限元预测为1.43±0.41。实际结果与有限元结果之间的比较显示无显著差异（p<0.001），表明有限元建模预测准确。配准后的图像数据在管腔增益和支架支柱贴壁不良方面显示出良好的视觉一致性。因此，我们开发了一个评估管腔增益有限元预测的平台。该平台可用于指导有限元预测软件的开发，最终可帮助医生进行钙化病变的支架治疗规划。
