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Utilizing a combination of computed tomography and 4D flow magnetic resonance imaging data, we developed patient\u2010specific fluid\u2013structure interaction models of the AV immersed in the aorta for five patients in the pre\u2010transcatheter AV replacement configuration. Our computational models are subjected to rigorous validation against 4D flow measurements. Our results demonstrate the models\u2019 capacity to accurately replicate flow dynamics. In addition, we illustrate how computational models can serve as valuable cross\u2010checks to reduce noise and erratic behaviour of\n                      <jats:italic>in vivo<\/jats:italic>\n                      data. Crucially, our validated models enable the measurement of additional critical quantities essential for a comprehensive understanding of aortic stenosis (AS) and its treatments: we compute the blood residence time, enhancing precision and personalization in assessing the probability of thrombus formation within the aorta. This study represents a significant step towards integrating\n                      <jats:italic>in silico<\/jats:italic>\n                      technologies into real clinical contexts, providing a robust framework for improving AS diagnosis and the design of next\u2010generation AV bioprostheses.\n                      <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/tjp70464-gra-0001-m.png\">\n                          <jats:alt-text>image<\/jats:alt-text>\n                        <\/jats:graphic>\n                      <\/jats:boxed-text>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Patient\u2010specific fluid\u2013structure interaction computational models of the aortic valve are developed for five patients in pre\u2010transcatheter aortic valve replacement configuration.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            A synergistic approach involving\n                            <jats:italic>in silico<\/jats:italic>\n                            models and\n                            <jats:italic>in vivo<\/jats:italic>\n                            data is utilized, including computed tomography and 4D flow magnetic resonance imaging.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            A patient\u2010specific calibration strategy is introduced to identify the aortic valve Young's modulus, leveraging\n                            <jats:italic>in vivo<\/jats:italic>\n                            flow\u2010derived metrics in combination with patient\u2010specific valve and aortic geometries.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            The computational models are validated against\n                            <jats:italic>in vivo<\/jats:italic>\n                            4D flow measurements, demonstrating their ability to replicate flow dynamics accurately.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            The potential of computational models to cross\u2010check and reduce noise in\n                            <jats:italic>in vivo<\/jats:italic>\n                            data is highlighted, providing additional critical physiological quantities for comprehensive aortic stenosis assessment, such as blood residence time, important for thrombus formation evaluation.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1113\/jp287284","type":"journal-article","created":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T01:48:41Z","timestamp":1775353721000},"page":"8237-8263","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Advancing aortic stenosis assessment: Validation of fluid\u2013structure interaction models against 4D flow MRI 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