Physics-guided Variational Method for Fractional Flow Reserve Based on Coronary Angiography.

IF 9.8 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging Pub Date : 2025-10-07 DOI:10.1109/tmi.2025.3618679

Qi Zhang,Heye Zhang,Zhifan Gao,Baihong Xie,Zhihui Zhang,Dan Deng,Changnong Peng,Xiaoqing Wang,Xiujian Liu

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引用次数: 0

Abstract

As a leading global cause of mortality, coronary ischemia requires accurate diagnostics for effective management. The combining coronary angiography with fractional flow reserve (FFR) offers structural and functional assessment of coronary stenosis to guide revascularization. However, traditional FFR measurements are invasive, requiring pressure wire placement. Image-based FFR estimation methods integrate vascular morphology with biomechanics but face challenges in modelling the complex fluid-structure interaction (FSI) of coronary flow and vessel walls. Therefore, we propose a physics-guided variational domain progressing method (PVDPM) for non-invasive FFR estimation through FSI system. PVDPM employs the principle of virtual work to model FSI system. This approach can improve the modelling of interdependent physical processes, enabling accurate FFR estimation based on coronary angiography-derived vascular morphology. The PVDPM demonstrates 91% accuracy in clinical datasets and offers solution for diagnosing coronary ischemia based on coronary angiography.

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基于冠状动脉造影的分数血流储备的物理引导变分方法。

作为全球主要的死亡原因，冠状动脉缺血需要准确的诊断才能有效地治疗。冠状动脉造影联合血流储备分数（FFR）可对冠状动脉狭窄进行结构和功能评估，指导血管重建术。然而，传统的FFR测量是侵入性的，需要放置压力丝。基于图像的FFR估计方法将血管形态学与生物力学相结合，但在模拟冠状动脉血流和血管壁的复杂流固相互作用（FSI）方面面临挑战。因此，我们提出了一种物理引导的变分域递进法（PVDPM），用于FSI系统的无创FFR估计。PVDPM采用虚功原理对FSI系统进行建模。这种方法可以改善相互依赖的物理过程的建模，实现基于冠状动脉造影衍生血管形态的准确FFR估计。PVDPM在临床数据集中的准确率达到91%，为基于冠状动脉造影的冠状动脉缺血诊断提供了解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Medical Imaging 医学-成像科学与照相技术

CiteScore

21.80

自引率

5.70%

发文量

637

审稿时长

5.6 months

期刊介绍： The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.