{"title":"针对时间分辨血管造影剂浓度重建的模拟知情学习","authors":"","doi":"10.1016/j.compbiomed.2024.109178","DOIUrl":null,"url":null,"abstract":"<div><div>Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been developed to enable the visualization of volumetric contrast flow dynamics through time-series of volumes. This reconstruction problem is ill-posed mainly due to vessel overlap in the projection direction and geometric vessel foreshortening, which leads to information loss in the recorded projection images. However, knowledge about the underlying fluid dynamics can be leveraged to constrain the solution space. In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations. The model predicts the spatially averaged contrast agent concentration for each centerline point of the vasculature over time, lowering the overall computational demand. The trained network enables the reconstruction of relative contrast agent concentrations with a mean absolute error of 0.02±0.02 and a mean absolute percentage error of 5.31±9.25 %. Moreover, the network is robust to varying degrees of vessel overlap and vessel foreshortening. Our approach demonstrates the potential of the integration of machine learning and blood flow simulations in time-resolved angiographic contrast agent concentration reconstruction.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010482524012630/pdfft?md5=d402d6a132f3f9ced1d8f862d270edb5&pid=1-s2.0-S0010482524012630-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Simulation-informed learning for time-resolved angiographic contrast agent concentration reconstruction\",\"authors\":\"\",\"doi\":\"10.1016/j.compbiomed.2024.109178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been developed to enable the visualization of volumetric contrast flow dynamics through time-series of volumes. This reconstruction problem is ill-posed mainly due to vessel overlap in the projection direction and geometric vessel foreshortening, which leads to information loss in the recorded projection images. However, knowledge about the underlying fluid dynamics can be leveraged to constrain the solution space. In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations. The model predicts the spatially averaged contrast agent concentration for each centerline point of the vasculature over time, lowering the overall computational demand. The trained network enables the reconstruction of relative contrast agent concentrations with a mean absolute error of 0.02±0.02 and a mean absolute percentage error of 5.31±9.25 %. Moreover, the network is robust to varying degrees of vessel overlap and vessel foreshortening. Our approach demonstrates the potential of the integration of machine learning and blood flow simulations in time-resolved angiographic contrast agent concentration reconstruction.</div></div>\",\"PeriodicalId\":10578,\"journal\":{\"name\":\"Computers in biology and medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0010482524012630/pdfft?md5=d402d6a132f3f9ced1d8f862d270edb5&pid=1-s2.0-S0010482524012630-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in biology and medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010482524012630\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524012630","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
三维数字减影血管造影术(3D-DSA)是一种成熟的基于 X 射线的血管解剖可视化技术。最近,人们开发了四维数字减影血管造影(4D-DSA)重建算法,以便通过时间序列的体积来观察体积造影剂的流动动态。由于投影方向上的血管重叠和几何上的血管前缩,导致记录的投影图像中信息丢失,因此这一重建问题存在困难。不过,可以利用有关基本流体动力学的知识来限制求解空间。在我们的工作中,我们在基于图像的血流模拟数据集上训练的神经网络模型中隐含了这一信息。该模型可预测血管每个中心线点随时间变化的空间平均造影剂浓度,从而降低整体计算需求。训练有素的网络能重建相对造影剂浓度,平均绝对误差为 0.02±0.02,平均绝对百分比误差为 5.31±9.25%。此外,该网络对不同程度的血管重叠和血管前缩具有鲁棒性。我们的方法证明了机器学习和血流模拟在时间分辨血管造影剂浓度重建中的整合潜力。
Simulation-informed learning for time-resolved angiographic contrast agent concentration reconstruction
Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been developed to enable the visualization of volumetric contrast flow dynamics through time-series of volumes. This reconstruction problem is ill-posed mainly due to vessel overlap in the projection direction and geometric vessel foreshortening, which leads to information loss in the recorded projection images. However, knowledge about the underlying fluid dynamics can be leveraged to constrain the solution space. In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations. The model predicts the spatially averaged contrast agent concentration for each centerline point of the vasculature over time, lowering the overall computational demand. The trained network enables the reconstruction of relative contrast agent concentrations with a mean absolute error of 0.02±0.02 and a mean absolute percentage error of 5.31±9.25 %. Moreover, the network is robust to varying degrees of vessel overlap and vessel foreshortening. Our approach demonstrates the potential of the integration of machine learning and blood flow simulations in time-resolved angiographic contrast agent concentration reconstruction.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.