Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916497091198976 |
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| author | Kim, Seungyeon Lee, Wheesung Ahn, Sung-Ho Lee, Do-Eun Lee, Tae-Rin |
| author_facet | Kim, Seungyeon Lee, Wheesung Ahn, Sung-Ho Lee, Do-Eun Lee, Tae-Rin |
| contents | Accurate prediction of cerebral blood flow is essential for the diagnosis and treatment of cerebrovascular diseases. Traditional computational methods, however, often incur significant computational costs, limiting their practicality in real-time clinical applications. This paper proposes a graph neural network (GNN) to predict blood flow and pressure in previously unseen cerebral vascular network structures that were not included in training data. The GNN was developed using clinical datasets from patients with stenosis, featuring complex and abnormal vascular geometries. Additionally, the GNN model was trained on data incorporating a wide range of inflow conditions, vessel topologies, and network connectivities to enhance its generalization capability. The approach achieved Pearson's correlation coefficients of 0.727 for pressure and 0.824 for flow rate, with sufficient training data. These findings demonstrate the potential of the GNN for real-time cerebrovascular diagnostics, particularly in handling intricate and pathological vascular networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17971 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets Kim, Seungyeon Lee, Wheesung Ahn, Sung-Ho Lee, Do-Eun Lee, Tae-Rin Image and Video Processing Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Accurate prediction of cerebral blood flow is essential for the diagnosis and treatment of cerebrovascular diseases. Traditional computational methods, however, often incur significant computational costs, limiting their practicality in real-time clinical applications. This paper proposes a graph neural network (GNN) to predict blood flow and pressure in previously unseen cerebral vascular network structures that were not included in training data. The GNN was developed using clinical datasets from patients with stenosis, featuring complex and abnormal vascular geometries. Additionally, the GNN model was trained on data incorporating a wide range of inflow conditions, vessel topologies, and network connectivities to enhance its generalization capability. The approach achieved Pearson's correlation coefficients of 0.727 for pressure and 0.824 for flow rate, with sufficient training data. These findings demonstrate the potential of the GNN for real-time cerebrovascular diagnostics, particularly in handling intricate and pathological vascular networks. |
| title | Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets |
| topic | Image and Video Processing Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2411.17971 |