Camilla Giulia Calastra, Marika Bono, Aloma Blanch Granada, Aleksandra Tuleja, Sarah Maike Bernhard, Vanessa Diaz-Zuccarini, Stavroula Balabani, Dominik Obrist, Hendrik von Tengg-Kobligk, Bernd Jung
{"title":"外周动静脉畸形的血流动力学特征使用快速对比增强磁共振成像：体外和体内研究。","authors":"Camilla Giulia Calastra, Marika Bono, Aloma Blanch Granada, Aleksandra Tuleja, Sarah Maike Bernhard, Vanessa Diaz-Zuccarini, Stavroula Balabani, Dominik Obrist, Hendrik von Tengg-Kobligk, Bernd Jung","doi":"10.1007/s10439-025-03766-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Peripheral arterio-venous malformations (pAVMs) are vascular defects often requiring extensive medical treatment. To improve disease management, hemodynamic markers based on 2D Digital Subtraction Angiography (DSA) data were previously defined to classify pAVMs. However, DSA offers only 2D information, involves ionizing radiation, and requires intra-arterial intervention. We hypothesized that pAVMs could be classified with the same approach with 3D dynamic contrast-enhanced MR-based data. To this end, the present work aims to develop a computational classification system for pAVMs using 3D dynamic contrast-enhanced MR-based data.</p><p><strong>Methods: </strong>A pAVM phantom was imaged using both DSA and MRI to validate the methodology, which was then applied to 10 MR-based in vivo datasets. A semi-automated vessel detection algorithm, based on the standard deviation of each voxel or pixel in time, was used. Classification was performed by identifying the time of arrival (CA<sub>ToA</sub>) of contrast agent (CA) and the maximum time derivative of the CA transport in each pixel or voxel (CA<sub>si</sub>).</p><p><strong>Results: </strong>Normalized CA<sub>ToA</sub> and CA<sub>si</sub> histograms showed no significant difference between in vitro DSA and MRI (respectively χ<sup>2</sup> = 0.20, p = 0.65 and χ<sup>2</sup> = 0.21, p = 0.65), validating the methodology to classify pAVMs. CA<sub>ToA</sub> histograms for type II-IV AVMs derived from in vivo MR-based data aligned with DSA patterns and known hemodynamics. CA<sub>ToA</sub> histograms of capillary-venulous AVMs were distinct, with non-zero values at later times than other AVM types, representing late venous drainage. Type IV AVMs histograms for CA<sub>si</sub> were more right-skewed than those derived from types II and III pAVMs.</p><p><strong>Conclusions: </strong>MR image quality and temporal resolution are sufficient to allow a classification of pAVMs. This classification method has the potential to become a diagnostic tool for the surgical navigation of pAVMs for clinicians.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hemodynamic Characterization of Peripheral Arterio-Venous Malformations Using Rapid Contrast-Enhanced MR Imaging: An In Vitro and In Vivo Study.\",\"authors\":\"Camilla Giulia Calastra, Marika Bono, Aloma Blanch Granada, Aleksandra Tuleja, Sarah Maike Bernhard, Vanessa Diaz-Zuccarini, Stavroula Balabani, Dominik Obrist, Hendrik von Tengg-Kobligk, Bernd Jung\",\"doi\":\"10.1007/s10439-025-03766-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Peripheral arterio-venous malformations (pAVMs) are vascular defects often requiring extensive medical treatment. To improve disease management, hemodynamic markers based on 2D Digital Subtraction Angiography (DSA) data were previously defined to classify pAVMs. However, DSA offers only 2D information, involves ionizing radiation, and requires intra-arterial intervention. We hypothesized that pAVMs could be classified with the same approach with 3D dynamic contrast-enhanced MR-based data. To this end, the present work aims to develop a computational classification system for pAVMs using 3D dynamic contrast-enhanced MR-based data.</p><p><strong>Methods: </strong>A pAVM phantom was imaged using both DSA and MRI to validate the methodology, which was then applied to 10 MR-based in vivo datasets. A semi-automated vessel detection algorithm, based on the standard deviation of each voxel or pixel in time, was used. Classification was performed by identifying the time of arrival (CA<sub>ToA</sub>) of contrast agent (CA) and the maximum time derivative of the CA transport in each pixel or voxel (CA<sub>si</sub>).</p><p><strong>Results: </strong>Normalized CA<sub>ToA</sub> and CA<sub>si</sub> histograms showed no significant difference between in vitro DSA and MRI (respectively χ<sup>2</sup> = 0.20, p = 0.65 and χ<sup>2</sup> = 0.21, p = 0.65), validating the methodology to classify pAVMs. CA<sub>ToA</sub> histograms for type II-IV AVMs derived from in vivo MR-based data aligned with DSA patterns and known hemodynamics. CA<sub>ToA</sub> histograms of capillary-venulous AVMs were distinct, with non-zero values at later times than other AVM types, representing late venous drainage. Type IV AVMs histograms for CA<sub>si</sub> were more right-skewed than those derived from types II and III pAVMs.</p><p><strong>Conclusions: </strong>MR image quality and temporal resolution are sufficient to allow a classification of pAVMs. This classification method has the potential to become a diagnostic tool for the surgical navigation of pAVMs for clinicians.</p>\",\"PeriodicalId\":7986,\"journal\":{\"name\":\"Annals of Biomedical Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10439-025-03766-3\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10439-025-03766-3","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
目的：外周动静脉畸形（pAVMs）是一种血管缺陷，通常需要广泛的药物治疗。为了改善疾病管理，以前定义了基于二维数字减影血管造影（DSA）数据的血流动力学标记物来分类pavm。然而，DSA仅提供二维信息，涉及电离辐射，需要动脉内介入。我们假设可以使用基于3D动态增强磁共振数据的相同方法对pavm进行分类。为此，本研究旨在开发一种基于三维动态对比度增强核磁共振数据的pavm计算分类系统。方法：使用DSA和MRI对pAVM幻体进行成像以验证方法，然后将其应用于10个基于mr的体内数据集。采用基于每个体素或像素在时间上的标准差的半自动化血管检测算法。通过确定造影剂（CA）的到达时间（CAToA）和CA在每个像素或体素（CAsi）中的最大时间导数进行分类。结果：归一化后的CAToA和CAsi直方图显示，体外DSA与MRI的差异无统计学意义（χ2 = 0.20, p = 0.65; χ2 = 0.21, p = 0.65），验证了pavm分类方法的有效性。II-IV型AVMs的CAToA直方图来源于基于DSA模式和已知血流动力学的体内mr数据。毛细血管-静脉静脉型AVM的CAToA直方图明显，比其他AVM晚出现非零值，说明静脉引流较晚。CAsi的IV型avm直方图比II型和III型pavm直方图更右偏。结论：MR图像质量和时间分辨率足以对pavm进行分类。这种分类方法有可能成为临床医生手术导航pavm的诊断工具。
Hemodynamic Characterization of Peripheral Arterio-Venous Malformations Using Rapid Contrast-Enhanced MR Imaging: An In Vitro and In Vivo Study.
Purpose: Peripheral arterio-venous malformations (pAVMs) are vascular defects often requiring extensive medical treatment. To improve disease management, hemodynamic markers based on 2D Digital Subtraction Angiography (DSA) data were previously defined to classify pAVMs. However, DSA offers only 2D information, involves ionizing radiation, and requires intra-arterial intervention. We hypothesized that pAVMs could be classified with the same approach with 3D dynamic contrast-enhanced MR-based data. To this end, the present work aims to develop a computational classification system for pAVMs using 3D dynamic contrast-enhanced MR-based data.
Methods: A pAVM phantom was imaged using both DSA and MRI to validate the methodology, which was then applied to 10 MR-based in vivo datasets. A semi-automated vessel detection algorithm, based on the standard deviation of each voxel or pixel in time, was used. Classification was performed by identifying the time of arrival (CAToA) of contrast agent (CA) and the maximum time derivative of the CA transport in each pixel or voxel (CAsi).
Results: Normalized CAToA and CAsi histograms showed no significant difference between in vitro DSA and MRI (respectively χ2 = 0.20, p = 0.65 and χ2 = 0.21, p = 0.65), validating the methodology to classify pAVMs. CAToA histograms for type II-IV AVMs derived from in vivo MR-based data aligned with DSA patterns and known hemodynamics. CAToA histograms of capillary-venulous AVMs were distinct, with non-zero values at later times than other AVM types, representing late venous drainage. Type IV AVMs histograms for CAsi were more right-skewed than those derived from types II and III pAVMs.
Conclusions: MR image quality and temporal resolution are sufficient to allow a classification of pAVMs. This classification method has the potential to become a diagnostic tool for the surgical navigation of pAVMs for clinicians.
期刊介绍：
Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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