{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:15:40Z","timestamp":1790709340167,"version":"4.1.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p>Statistical Shape Models (SSMs) are well-established tools for assessing the variability of 3D geometry and for broadening a limited set of shapes. They are widely used in medical imaging due to their ability to model complex geometries and their high efficiency as generative models. The principal step behind these techniques is a registration phase, which, in the case of complex geometries, can be a critical issue due to the correspondence problem, as it necessitates the development of correspondence mapping between shapes. The thoracic aorta, with its high level of morphological complexity, poses a multi-scale deformation problem due to the presence of several branch vessels with varying diameters. Moreover, branch vessels exhibit significant variability in shape, making the correspondence optimization even more challenging. Consequently, existing studies have focused on developing SSMs based only on the main body of the aorta, excluding the supra-aortic vessels from the analysis. In this work, we present a novel non-rigid registration algorithm based on optimizing a differentiable distance function through a modified gradient descent approach. This strategy enables the inclusion of custom, domain-specific constraints in the objective function, which act as landmarks during the registration phase. The algorithm\u2019s registration performance was tested and compared to an alternative Statistical Shape modeling framework, and subsequently used for the development of a comprehensive SSM of the thoracic aorta, including the supra-aortic vessels. The developed SSM was further evaluated against the alternative framework in terms of generalisation, specificity, and compactness to assess its effectiveness.<\/jats:p>","DOI":"10.3389\/fphys.2023.1211461","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:58:32Z","timestamp":1691744312000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Enabling supra-aortic vessels inclusion in statistical shape models of the aorta: a novel non-rigid registration method"],"prefix":"10.3389","volume":"14","author":[{"given":"Martino Andrea","family":"Scarpolini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marilena","family":"Mazzoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona","family":"Celi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"B1","unstructured":"Patient-specific modeling of geometry and blood flow in large arteries\n            AntigaL.\n          Milano, ItalyPolitecnico di Milano2002"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1007\/s11517-008-0420-1","article-title":"An image-based modeling framework for patient-specific computational hemodynamics","volume":"46","author":"Antiga","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"B3","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.1007\/s10439-021-02841-9","article-title":"Effects of uncertainty of outlet boundary conditions in a patient-specific case of aortic coarctation","volume":"49","author":"Antonuccio","year":"2021","journal-title":"Ann. Biomed. Eng."},{"key":"B4","volume-title":"Meshmixer","year":"2018"},{"key":"B5","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/B978-044482537-7\/50007-3","article-title":"Mesh generation","volume-title":"Handbook of computational geometry","author":"Bern","year":"2000"},{"key":"B6","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Analysis Mach. Intell."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1007\/s12008-020-00694-5","article-title":"Fast interactive cfd evaluation of hemodynamics assisted by rbf mesh morphing and reduced order models: the case of ataa modelling","volume":"14","author":"Biancolini","year":"2020","journal-title":"Int. J. Interact. Des. Manuf. (IJIDeM)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1007\/s13239-018-00387-x","article-title":"Validation of numerical simulations of thoracic aorta hemodynamics: comparison with in vivo measurements and stochastic sensitivity analysis","volume":"9","author":"Boccadifuoco","year":"2018","journal-title":"Cardiovasc. Eng. Technol."},{"key":"B9","first-page":"6226","article-title":"Uncertainty quantification in numerical simulations of the flow in thoracic aortic aneurysms","author":"Boccadifuoco","year":"2016"},{"key":"B10","first-page":"185","article-title":"A remeshing approach to multiresolution modeling","author":"Botsch","year":"2004"},{"key":"B11","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s12880-016-0142-z","article-title":"A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta","volume":"16","author":"Bruse","year":"2016","journal-title":"BMC Med. imaging"},{"key":"B12","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.1109\/TBME.2017.2655364","article-title":"Detecting clinically meaningful shape clusters in medical image data: metrics analysis for hierarchical clustering applied to healthy and pathological aortic arches","volume":"64","author":"Bruse","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B13","doi-asserted-by":"publisher","first-page":"111620","DOI":"10.1016\/j.jbiomech.2023.111620","article-title":"Impact of wall displacements on the large-scale flow coherence in ascending aorta","volume":"154","author":"Cal\u00f2","year":"2023","journal-title":"J. Biomechanics"},{"key":"B14","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.medengphy.2020.09.005","article-title":"A novel formulation for the study of the ascending aortic fluid dynamics with in vivo data","volume":"91","author":"Capellini","year":"2021","journal-title":"Med. Eng. Phys."},{"key":"B15","doi-asserted-by":"publisher","first-page":"111007","DOI":"10.1115\/1.4040940","article-title":"Computational fluid dynamic study for aTAA hemodynamics: an integrated image-based and radial basis functions mesh morphing approach","volume":"140","author":"Capellini","year":"2018","journal-title":"J. biomechanical Eng."},{"key":"B16","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/B978-0-12-810493-4.00012-2","article-title":"Shapeworks: particle-based shape correspondence and visualization software","volume-title":"Statistical shape and deformation analysis","author":"Cates","year":"2017"},{"key":"B17","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1093\/ejcts\/ezt400","article-title":"Three-dimensional sensitivity assessment of thoracic aortic aneurysm wall stress: A probabilistic finite-element study","volume":"45","author":"Celi","year":"2014","journal-title":"Eur. J. Cardio-thoracic Surg."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1096196","DOI":"10.3389\/fbioe.2023.1096196","article-title":"An image-based approach for the estimation of arterial local stiffness in vivo","volume":"11","author":"Celi","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B19","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.2174\/1381612823666170704171702","article-title":"Multimodality imaging for interventional cardiology","volume":"23","author":"Celi","year":"2017","journal-title":"Curr. Pharm. Des."},{"key":"B20","volume-title":"Learning shape: Optimal models for analysing natural variability","author":"Davies","year":"2002"},{"key":"B21","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.medengphy.2020.07.003","article-title":"Deciphering ascending thoracic aortic aneurysm hemodynamics in relation to biomechanical properties","volume":"82","author":"De Nisco","year":"2020","journal-title":"Med. Eng. Phys."},{"key":"B22","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1177\/2150135114551028","article-title":"Abnormalities of aortic arch shape, central aortic flow dynamics, and distensibility predispose to hypertension after successful repair of aortic coarctation","volume":"5","author":"Donazzan","year":"2014","journal-title":"World J. Pediatr. Congenit. Heart Surg."},{"key":"B23","doi-asserted-by":"publisher","first-page":"081906","DOI":"10.1063\/5.0101128","article-title":"Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics","volume":"34","author":"Du","year":"2022","journal-title":"Phys. Fluids"},{"key":"B24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neuroimage.2014.06.043","article-title":"Morphometry of anatomical shape complexes with dense deformations and sparse parameters","volume":"101","author":"Durrleman","year":"2014","journal-title":"NeuroImage"},{"key":"B25","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.1093\/eurheartj\/ehu281","article-title":"2014 ESC Guidelines on the diagnosis and treatment of aortic diseases: document covering acute and chronic aortic diseases of the thoracic and abdominal aorta of the adult. The Task Force for the Diagnosis and Treatment of Aortic Diseases of the European Society of Cardiology (ESC)","volume":"35","author":"Erbel","year":"2014","journal-title":"Eur. Heart J."},{"key":"B26","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10851-007-0018-5","article-title":"Measures for benchmarking of automatic correspondence algorithms","volume":"28","author":"Ericsson","year":"2007","journal-title":"J. Math. Imaging Vis."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D slicer as an image computing platform for the quantitative imaging network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. imaging"},{"key":"B28","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1111\/j.2517-6161.1991.tb01825.x","article-title":"Procrustes methods in the statistical analysis of shape","volume":"53","author":"Goodall","year":"1991","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"B29","doi-asserted-by":"publisher","first-page":"102271","DOI":"10.1016\/j.media.2021.102271","article-title":"Benchmarking off-the-shelf statistical shape modeling tools in clinical applications","volume":"76","author":"Goparaju","year":"2022","journal-title":"Med. Image Anal."},{"key":"B30","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.media.2009.05.004","article-title":"Statistical shape models for 3d medical image segmentation: A review","volume":"13","author":"Heimann","year":"2009","journal-title":"Med. image Anal."},{"key":"B31","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"B32","doi-asserted-by":"publisher","first-page":"e3103","DOI":"10.1002\/cnm.3103","article-title":"A machine learning approach as a surrogate of finite element analysis\u2013based inverse method to estimate the zero-pressure geometry of human thoracic aorta","volume":"34","author":"Liang","year":"2018","journal-title":"Int. J. Numer. methods Biomed. Eng."},{"key":"B33","doi-asserted-by":"publisher","first-page":"3482","DOI":"10.21037\/jtd.2018.05.188","article-title":"Hemodynamic changes lead to alterations in aortic diameters and may challenge further stent graft sizing in acute aortic syndrome","volume":"10","author":"Lortz","year":"2018","journal-title":"J. Thorac. Dis."},{"key":"B34","doi-asserted-by":"publisher","first-page":"61631","DOI":"10.1109\/access.2022.3179109","article-title":"Oriented and directional Chamfer distance losses for 3D object reconstruction from a single image","volume":"10","author":"Lu","year":"2022","journal-title":"IEEE Access"},{"key":"B35","doi-asserted-by":"publisher","first-page":"106107","DOI":"10.1016\/j.cmpb.2021.106107","article-title":"Coupling hemodynamics with mechanobiology in patient-specific computational models of ascending thoracic aortic aneurysms","volume":"205","author":"Mousavi","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"B36","first-page":"381","article-title":"Laplacian mesh optimization","author":"Nealen","year":"2006"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478513.3480501","article-title":"Large steps in inverse rendering of geometry","volume":"40","author":"Nicolet","year":"2021","journal-title":"ACM Trans. Graph."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1093\/ejcts\/ezv459","article-title":"Blood flow analysis of the aortic arch using computational fluid dynamics","volume":"49","author":"Numata","year":"2016","journal-title":"Eur. J. Cardio-Thoracic Surg."},{"key":"B39","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1007\/s11548-015-1319-6","article-title":"Entropy-based particle correspondence for shape populations","volume":"11","author":"Oguz","year":"2016","journal-title":"Int. J. Comput. assisted radiology Surg."},{"key":"B40","article-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"B41","doi-asserted-by":"publisher","first-page":"S465","DOI":"10.21037\/jtd.2017.03.87","article-title":"Debranching aortic surgery","volume":"9","author":"P\u00e9rez","year":"2017","journal-title":"J. Thorac. Dis."},{"key":"B42","article-title":"svMorph: interactive geometry-editing tools for virtual patient-specific vascular anatomies","author":"Pham","year":"2022"},{"key":"B43","article-title":"Accelerating 3D deep learning with PyTorch3D","author":"Ravi","year":"2020"},{"key":"B44","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1016\/j.jaccas.2021.04.036","article-title":"Transcatheter treatment of \u201ccomplex\u201d aortic coarctation guided by printed 3D model","volume":"3","author":"Santoro","year":"2021","journal-title":"Case Rep."},{"key":"B45","doi-asserted-by":"publisher","first-page":"573","DOI":"10.3390\/bioengineering9100573","article-title":"Evaluating the haemodynamic performance of endografts for complex aortic arch repair","volume":"9","author":"Sengupta","year":"2022","journal-title":"Bioengineering"},{"key":"B46","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1093\/ejcts\/ezy339","article-title":"Aortic morphological variability in patients with bicuspid aortic valve and aortic coarctation","volume":"55","author":"Sophocleous","year":"2019","journal-title":"Eur. J. Cardio-Thoracic Surg."},{"key":"B47","doi-asserted-by":"publisher","first-page":"105326","DOI":"10.1016\/j.compbiomed.2022.105326","article-title":"Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease","volume":"144","author":"Sophocleous","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B48","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1109\/TMI.2021.3057496","article-title":"Synthetic database of aortic morphometry and hemodynamics: overcoming medical imaging data availability","volume":"40","author":"Thamsen","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B49","first-page":"545","article-title":"Dpdist: comparing point clouds using deep point cloud distance","volume-title":"European conference on computer vision","author":"Urbach","year":"2020"},{"key":"B50","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1109\/TVCG.2007.70430","article-title":"Generic remeshing of 3D triangular meshes with metric-dependent discrete Voronoi diagrams","volume":"14","author":"Valette","year":"2008","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"B51","doi-asserted-by":"publisher","first-page":"732561","DOI":"10.3389\/fphys.2021.732561","article-title":"Fully-coupled FSI computational analyses in the ascending thoracic aorta using patient-specific conditions and anisotropic material properties","volume":"12","author":"Vignali","year":"2021","journal-title":"Front. Physiology"},{"key":"B52","article-title":"Kernel principal component analysis and its applications in face recognition and active shape models","author":"Wang","year":"2012"},{"key":"B53","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1080\/10255842.2018.1484914","article-title":"Point-cloud registration using adaptive radial basis functions","volume":"21","author":"Zhang","year":"2018","journal-title":"Comput. methods biomechanics Biomed. Eng."},{"key":"B54","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/bf01427149","article-title":"Iterative point matching for registration of free-form curves and surfaces","volume":"13","author":"Zhang","year":"1994","journal-title":"Int. J. Comput. Vis."}],"container-title":["Frontiers in Physiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2023.1211461\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:58:43Z","timestamp":1691744323000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2023.1211461\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":54,"alternative-id":["10.3389\/fphys.2023.1211461"],"URL":"https:\/\/doi.org\/10.3389\/fphys.2023.1211461","relation":{},"ISSN":["1664-042X"],"issn-type":[{"value":"1664-042X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]},"article-number":"1211461"}}