Prabhu David, Mehanna Emile, Gargesha Madhusudhana, Brandt Eric, Wen Di, van Ditzhuijzen Nienke S, Chamie Daniel, Yamamoto Hirosada, Fujino Yusuke, Alian Ali, Patel Jaymin, Costa Marco, Bezerra Hiram G, Wilson David L
Case Western Reserve University , Department of Biomedical Engineering, Cleveland, 10900 Euclid Ave, Cleveland, Ohio 44106, United States.
University Hospitals Case Medical Center , Harrington Heart and Vascular Institute, Cardiovascular Imaging Core Laboratory, 11100 Euclid Avenue, Cleveland, Ohio 44106, United States.
J Med Imaging (Bellingham). 2016 Apr;3(2):026004. doi: 10.1117/1.JMI.3.2.026004. Epub 2016 Jun 28.
Evidence suggests high-resolution, high-contrast, [Formula: see text] intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and three-dimensional (3-D) registration methods to provide validation of IVOCT pullback volumes using microscopic, color, and fluorescent cryo-image volumes with optional registered cryo-histology. A specialized registration method matched IVOCT pullback images acquired in the catheter reference frame to a true 3-D cryo-image volume. Briefly, an 11-parameter registration model including a polynomial virtual catheter was initialized within the cryo-image volume, and perpendicular images were extracted, mimicking IVOCT image acquisition. Virtual catheter parameters were optimized to maximize cryo and IVOCT lumen overlap. Multiple assessments suggested that the registration error was better than the [Formula: see text] spacing between IVOCT image frames. Tests on a digital synthetic phantom gave a registration error of only [Formula: see text] (signed distance). Visual assessment of randomly presented nearby frames suggested registration accuracy within 1 IVOCT frame interval ([Formula: see text]). This would eliminate potential misinterpretations confronted by the typical histological approaches to validation, with estimated 1-mm errors. The method can be used to create annotated datasets and automated plaque classification methods and can be extended to other intravascular imaging modalities.
有证据表明，高分辨率、高对比度的[公式：见正文]血管内光学相干断层扫描（IVOCT）能够区分斑块类型，但仍需进一步验证，尤其是在自动斑块特征识别方面。我们开发了实验和三维（3-D）配准方法，以使用微观、彩色和荧光冷冻图像体积以及可选的配准冷冻组织学来验证IVOCT回撤体积。一种专门的配准方法将在导管参考系中获取的IVOCT回撤图像与真实的3-D冷冻图像体积进行匹配。简而言之，在冷冻图像体积内初始化一个包含多项式虚拟导管的11参数配准模型，并提取垂直图像，模拟IVOCT图像采集。优化虚拟导管参数以最大化冷冻图像和IVOCT管腔的重叠。多次评估表明，配准误差优于IVOCT图像帧之间的[公式：见正文]间距。在数字合成模型上的测试给出的配准误差仅为[公式：见正文]（有符号距离）。对随机呈现的相邻帧进行视觉评估表明，配准精度在1个IVOCT帧间隔（[公式：见正文]）内。这将消除典型组织学验证方法所面临的潜在误解，估计误差为1毫米。该方法可用于创建带注释的数据集和自动斑块分类方法，并可扩展到其他血管内成像模式。
