标题： 基于超像素粒子群优化的新型冠状动脉配准方法 显示英文标题

标题： A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization

摘要： 经皮冠状动脉介入治疗（PCI）是一种微创手术，可改善冠状动脉血流并治疗冠状动脉疾病。 尽管PCI通常需要实时的二维X射线血管造影（XRA）来引导导管放置，但计算机断层扫描血管造影（CTA）可以通过提供精确的三维血管解剖和状态信息，显著提高PCI的效果。 为了利用实时XRA和详细的三维CTA解剖结构进行PCI，需要准确的多模态图像配准（XRA和CTA），以指导手术并避免并发症。 这是一个具有挑战性的过程，因为它需要从不同几何模态（2D->3D和反之亦然）进行图像配准，并且对比度和噪声水平存在变化。 本文提出了一种基于群体优化算法的新颖多模态冠状动脉图像配准方法，有效解决了这些成像模态中的大变形、低对比度和噪声等问题。 我们的算法包括两个主要模块：1）分别对XRA和CTA图像进行预处理，以及2）基于使用Steger和Superpixel粒子群优化算法特征提取的配准模块。 我们的技术在来自10名接受PCI患者的28组XRA和CTA图像的试点数据集上进行了评估。 在配准准确性、鲁棒性和效率方面，该算法与四种最先进的（SOTA）方法进行了比较。 在所有方面，我们的方法都优于所选的SOTA基线。 实验结果表明了我们算法的有效性，超过了先前的基准，并提出了一种可能有助于改善冠状动脉疾病患者预后的新型临床方法。 显示英文摘要

摘要： Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure that improves coronary blood flow and treats coronary artery disease. Although PCI typically requires 2D X-ray angiography (XRA) to guide catheter placement at real-time, computed tomography angiography (CTA) may substantially improve PCI by providing precise information of 3D vascular anatomy and status. To leverage real-time XRA and detailed 3D CTA anatomy for PCI, accurate multimodal image registration of XRA and CTA is required, to guide the procedure and avoid complications. This is a challenging process as it requires registration of images from different geometrical modalities (2D -> 3D and vice versa), with variations in contrast and noise levels. In this paper, we propose a novel multimodal coronary artery image registration method based on a swarm optimization algorithm, which effectively addresses challenges such as large deformations, low contrast, and noise across these imaging modalities. Our algorithm consists of two main modules: 1) preprocessing of XRA and CTA images separately, and 2) a registration module based on feature extraction using the Steger and Superpixel Particle Swarm Optimization algorithms. Our technique was evaluated on a pilot dataset of 28 pairs of XRA and CTA images from 10 patients who underwent PCI. The algorithm was compared with four state-of-the-art (SOTA) methods in terms of registration accuracy, robustness, and efficiency. Our method outperformed the selected SOTA baselines in all aspects. Experimental results demonstrate the significant effectiveness of our algorithm, surpassing the previous benchmarks and proposes a novel clinical approach that can potentially have merit for improving patient outcomes in coronary artery disease.