{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:06:01Z","timestamp":1780675561394,"version":"3.54.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":139,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Morphometric analysis of the thoracic aorta (TA) and left ventricle (LV) plays a fundamental role in detecting anatomical abnormalities and functional alterations to support pre-operative planning, predict disease risk and inform device design. However, conventional approaches to morphometric evaluation are typically performed manually using visualization software, thus resulting in time-consuming, operator-dependent processes that are usually limited to static imaging. This work presents an automated three-dimensional image-based methodological framework for dynamic morphometric analysis, from ECG-gated CT datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>A multi-label 3D U-Net was trained for the automatic segmentation of the TA and LV using a dataset of 50 single-phase CT scans, with ground-truth label maps validated under expert radiological supervision. Model performance was tested on an independent multi-phase cohort of 10 patients. The network achieved high segmentation accuracy, with Dice scores of 97.77 \u00b1 0.31% for the TA and 91.45 \u00b1 1.26% for the LV on the multi-phase test set. The resulting 3D surface models enabled the computation of geometric descriptors, including volumetric indices, displacement fields, and centreline-based diameters, across cardiac phases on 42 patients. Overall, the framework demonstrated robustness to variations in contrast intensity, cardiac motion, and inter-patient anatomical variability, providing a reliable and reproducible pipeline for comprehensive, three-dimensional, and time-resolved morphometric analysis of the ventriculo-arterial complex with physiological or mildly altered anatomy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This approach has strong potential for future clinical translation, supporting quantitative assessment of cardiac function, and aortic pathophysiology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjimp\/qyag090","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T11:43:53Z","timestamp":1778845433000},"source":"Crossref","is-referenced-by-count":0,"title":["Advancing morphometric assessment of the aorta and left ventricle from dynamic CT: a deep learning-based study"],"prefix":"10.1093","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7344-4402","authenticated-orcid":false,"given":"Francesca","family":"Dell\u2019Agnello","sequence":"first","affiliation":[{"name":"BioCardioLab, Bioengineering Unit, Fondazione Monasterio , Massa ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4938-9569","authenticated-orcid":false,"given":"Katia","family":"Capellini","sequence":"additional","affiliation":[{"name":"BioCardioLab, Bioengineering Unit, Fondazione Monasterio , Massa ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-3279","authenticated-orcid":false,"given":"Filippo","family":"Cademartiri","sequence":"additional","affiliation":[{"name":"Multimodal Cardiovascular and Neuroradiological Imaging, Department of Radiology, Fondazione Monasterio , Pisa ,","place":["Italy"]},{"name":"IRCCS SYNLAB SDN , Napoli ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelo","family":"Monteleone","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging, Department of Radiology, Fondazione Monasterio , Massa ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3378-1723","authenticated-orcid":false,"given":"Alessia","family":"Gimelli","sequence":"additional","affiliation":[{"name":"Multimodal Cardiovascular and Neuroradiological Imaging, Department of Radiology, Fondazione Monasterio , Pisa ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7832-0122","authenticated-orcid":false,"given":"Simona","family":"Celi","sequence":"additional","affiliation":[{"name":"BioCardioLab, Bioengineering Unit, Fondazione Monasterio , Massa 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