Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics
Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone...
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| Published in: | Computers in biology and medicine Vol. 100; pp. 209 - 220 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
Elsevier Ltd
01.09.2018
Elsevier Limited |
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| ISSN: | 0010-4825, 1879-0534, 1879-0534 |
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| Abstract | Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.
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•Automatic parametric mesh generation method has been validated using statistical analysis and modified Hausdorff distance.•We reconstructed arterial trees meshes and validated the centerline and diameter accuracy of them against raw images.•Accurate computation is essential for high-fidelity CFD simulation, hemodynamic risk, and morphological analysis. |
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| AbstractList | Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.
[Display omitted]
•Automatic parametric mesh generation method has been validated using statistical analysis and modified Hausdorff distance.•We reconstructed arterial trees meshes and validated the centerline and diameter accuracy of them against raw images.•Accurate computation is essential for high-fidelity CFD simulation, hemodynamic risk, and morphological analysis. Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation. Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation. |
| Author | Linninger, Andreas A. Xu, Guoren Charbel, Fady T. Ghaffari, Mahsa Zhou, Xiaohong Joe Sanchez, Lea Alaraj, Ali |
| AuthorAffiliation | 1 department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA 3 Department of Radiology and Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA 2 Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA |
| AuthorAffiliation_xml | – name: 3 Department of Radiology and Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA – name: 2 Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA – name: 1 department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA |
| Author_xml | – sequence: 1 givenname: Mahsa surname: Ghaffari fullname: Ghaffari, Mahsa organization: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA – sequence: 2 givenname: Lea surname: Sanchez fullname: Sanchez, Lea organization: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA – sequence: 3 givenname: Guoren surname: Xu fullname: Xu, Guoren organization: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA – sequence: 4 givenname: Ali surname: Alaraj fullname: Alaraj, Ali email: alaraj@uic.edu organization: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA – sequence: 5 givenname: Xiaohong Joe surname: Zhou fullname: Zhou, Xiaohong Joe organization: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA – sequence: 6 givenname: Fady T. surname: Charbel fullname: Charbel, Fady T. organization: Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA – sequence: 7 givenname: Andreas A. surname: Linninger fullname: Linninger, Andreas A. email: linninge@uic.edu, linninge@gmail.com organization: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30048917$$D View this record in MEDLINE/PubMed |
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| Keywords | Pointwise surface distance Shape similarity index Morphological analysis Mesh validation Hausdorff distance Cerebral arterial tree Parametric structured mesh |
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| SubjectTerms | Angiography Arteries Bifurcations Blood vessels Cerebral arterial tree Cerebral Arteries - diagnostic imaging Cerebral Arteries - physiopathology Cerebral blood flow Cerebrovascular Circulation Cerebrovascular system Computational grids Computer applications Computer Simulation Confidence Correlation analysis Data banks Finite element method Hausdorff distance Hemodynamics Human subjects Humans Image processing Image reconstruction Image segmentation Magnetic fields Magnetic resonance Magnetic Resonance Angiography Mathematical models Mesh generation Mesh validation Model accuracy Models, Cardiovascular Morphological analysis Morphology Parametric structured mesh Pointwise surface distance Shape similarity index Similarity measures Statistical analysis Time dependence Trees |
| Title | Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics |
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