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Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images.

التفاصيل البيبلوغرافية
العنوان: Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images.
المؤلفون: Bartololo MA; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA., Taylor-LaPole AM; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA., Gandhi D; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA.; Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA., Johnson A; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA.; Department of Mathematics and Statistics, University of South Florida, Tampa, FL, USA., Li Y; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA.; North Carolina School of Science and Mathematics, Durham, NC, USA., Slack E; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA.; Department of Mathematics, Colorado State University, Fort Collins, CO, USA., Stevens I; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA., Turner Z; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA.; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA., Weigand JD; Division of Cardiology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA., Puelz C; Division of Cardiology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA., Husmeier D; School of Mathematics and Statistics, University of Glasgow, Glasgow, UK., Olufsen MS; Department of Mathematics, North Carolina State University, Raleigh, North Carolina, USA.
المصدر: ArXiv [ArXiv] 2024 May 09. Date of Electronic Publication: 2024 May 09.
نوع المنشور: Preprint
اللغة: English
بيانات الدورية: Country of Publication: United States NLM ID: 101759493 Publication Model: Electronic Cited Medium: Internet ISSN: 2331-8422 (Electronic) Linking ISSN: 23318422 NLM ISO Abbreviation: ArXiv Subsets: PubMed not MEDLINE
مستخلص: One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance, and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in-vivo imaging introduces variability in network size and vessel dimensions, affecting hemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centerlines. Still, there is no exact way to generate vascular trees from the centerlines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labeled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D hemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore hemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analyzing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific hemodynamics models.
Competing Interests: Competing Interests The authors declare that they have no competing interests.
التعليقات: Update in: J Physiol. 2024 Aug;602(16):3929-3954. doi: 10.1113/JP286193. (PMID: 39075725)
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معلومات مُعتمدة: R01 HL147590 United States HL NHLBI NIH HHS
تواريخ الأحداث: Date Created: 20240205 Latest Revision: 20240815
رمز التحديث: 20240815
مُعرف محوري في PubMed: PMC10836077
PMID: 38313199
قاعدة البيانات: MEDLINE
الوصف
تدمد:2331-8422