Xinyu Li, Danni Ai, Hong Song, Jingfan Fan, Tianyu Fu, Deqiang Xiao, Yining Wang, Jian Yang
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引用次数: 0
摘要
在 X 射线血管造影(XRA)中准确检测冠状动脉狭窄对于诊断和治疗冠状动脉疾病(CAD)非常重要。然而,由于呼吸和心脏运动、成像质量差以及血管结构复杂等因素,很难快速准确地识别狭窄。在这项研究中,我们提出了一种具有时空特征共享功能的量子扩散模型来实时检测血管狭窄(STQD-Det)。我们的框架由两个模块组成:序列量子噪声盒模块和时空特征模块。为了评估该方法的有效性,我们使用由 233 个 XRA 序列组成的数据集进行了 4 倍交叉验证。我们的方法获得了 92.39% 的 F1 分数,实时处理速度为每秒 25.08 帧。这些结果优于 17 种最先进的方法。实验结果表明,所提出的方法可以快速、准确地完成血管狭窄检测。
STQD-Det: Spatio-Temporal Quantum Diffusion Model for Real-time Coronary Stenosis Detection in X-ray Angiography.
Detecting coronary stenosis accurately in X-ray angiography (XRA) is important for diagnosing and treating coronary artery disease (CAD). However, challenges arise from factors like breathing and heart motion, poor imaging quality, and the complex vascular structures, making it difficult to identify stenosis fast and precisely. In this study, we proposed a Quantum Diffusion Model with Spatio-Temporal Feature Sharing to Real-time detect Stenosis (STQD-Det). Our framework consists of two modules: Sequential Quantum Noise Boxes module and spatio-temporal feature module. To evaluate the effectiveness of the method, we conducted a 4-fold cross-validation using a dataset consisting of 233 XRA sequences. Our approach achieved the F1 score of 92.39% with a real-time processing speed of 25.08 frames per second. These results outperform 17 state-of-the-art methods. The experimental results show that the proposed method can accomplish the stenosis detection quickly and accurately.