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Abstract
Many cardiovascular diseases lead to local increases in relative pressure, reflecting the higher costs of driving blood flow. The utility of this biomarker for stratifying the severity of disease has thus driven the development of methods to measure these relative pressures. While intravascular catheterisation remains the most direct measure, its invasiveness limits clinical application in many instances. Non-invasive Doppler ultrasound estimates have partially addressed this gap; however only provide relative pressure estimates for a range of constricted cardiovascular conditions. Here we introduce a non-invasive method that enables arbitrary interrogation of relative pressures throughout an imaged vascular structure, leveraging modern phase contrast magnetic resonance imaging, the virtual work-energy equations, and a virtual field to provide robust and accurate estimates. The versatility and accuracy of the method is verified in a set of complex patient-specific cardiovascular models, where relative pressures into previously inaccessible flow regions are assessed. The method is further validated within a cohort of congenital heart disease patients, providing a novel tool for probing relative pressures in-vivo.
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1 KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746); Karolinska Institutet, Department of Clinical Sciences, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626)
2 King’s College London, St Thomas’ Hospital, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); Evelina Children’s Hospital, Department of Congenital Heart Disease, London, United Kingdom (GRID:grid.13097.3c)
3 King’s College London, St Thomas’ Hospital, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764)
4 Bernoulli Institute, University of Groningen, Groningen, The Netherlands (GRID:grid.4830.f) (ISNI:0000 0004 0407 1981); Universidad de Chile, Center for Mathematical Modeling, Santiago, Chile (GRID:grid.443909.3) (ISNI:0000 0004 0385 4466)
5 KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746); Intervention and Technology (CLINTEC), Karolinska Institutet, Department of Clinical Science, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626)
6 KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746)
7 King’s College London, St Thomas’ Hospital, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); University of Michigan, Departments of Surgery and Biomedical Engineering, Ann Arbor, USA (GRID:grid.214458.e) (ISNI:0000000086837370)