{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:21Z","timestamp":1760146341994,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In the realm of cardiac health research, accurate fluid dynamics simulations are vital for comprehending the heart function and diagnosing conditions. Central to these simulations is the precision of ventricular wall meshes used to model heart geometry. However, segmenting the wetted surface, particularly in the right ventricle (RV) with its significantly thinner parietal thickness compared to the left ventricle, presents challenges. This study focuses on qualitatively evaluating an automated reconstruction model for the RV\u2019s outer wall using Radial Basis function (RBF) morphing. Two procedural criteria were compared, a random selection of control points and a curvature-based approach, which differ in terms of the identification of the control points of the RBF function. From these considerations, it emerges that a controlled use of the RBF function on the basis of the curvatures guarantees the greater controllability of the shape evolutions of the parietal structure of the RV, but it is more sensitive to any anomalies in the distribution of the vertices, as can be seen from the number of outliers, and its controllability is a function of the percentage of points chosen, exerting a greater impact on the required computational capacity. The definition of a strategic criterion for the selection of control points could represent a crucial aspect in the definition of an automatic reconstruction procedure of anatomical elements, which guarantees a morphological variability in line with the need to expand the pathological sample to be used for statistical formulations in the clinical field.<\/jats:p>","DOI":"10.3390\/computation12110216","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T05:07:22Z","timestamp":1730092042000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Three-Dimensional Reconstruction of the Right Ventricle from a Radial Basis Morphing of the Inner Surface"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1938-8837","authenticated-orcid":false,"given":"Carlotta","family":"Fontana","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5843-2805","authenticated-orcid":false,"given":"Nicola","family":"Cappetti","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Salerno, 84084 Fisciano, SA, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.media.2014.10.004","article-title":"Right Ventricle Segmentation from Cardiac MRI: A Collation Study","volume":"19","author":"Petitjean","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10334-015-0521-4","article-title":"A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging","volume":"29","author":"Peng","year":"2016","journal-title":"Magn. Reson. Mater. Phys. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8116","DOI":"10.16965\/ijar.2021.165","article-title":"Difference in Thickness Between Right Ventricle and Left Ventricle of Adult Human Heart: A Cadaveric Study","volume":"9","author":"Thounaojam","year":"2021","journal-title":"Int. J. Anat. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"901","DOI":"10.3348\/kjr.2020.0787","article-title":"Right Ventricular Mass Quantification Using Cardiac CT and a Semiautomatic Three-Dimensional Hybrid Segmentation Approach: A Pilot Study","volume":"22","author":"Goo","year":"2021","journal-title":"Korean J. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1093\/icvts\/ivu202","article-title":"Computational fluid dynamics of the right ventricular outflow tract and of the pulmonary artery: A bench model of flow dynamics","volume":"19","author":"Mosbahi","year":"2014","journal-title":"Interact. Cardiovasc. Thorac. Surg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8230","DOI":"10.1038\/s41598-023-34098-8","article-title":"GPU accelerated digital twins of the human heart open new routes for cardiovascular research","volume":"13","author":"Viola","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s10237-019-01268-5","article-title":"The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium","volume":"19","author":"Augustin","year":"2020","journal-title":"Biomech. Model Mechanobiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1002\/mp.16613","article-title":"Multimodal fusion workflow for target delineation in cardiac radioablation of ventricular tachycardia","volume":"51","author":"Rigal","year":"2024","journal-title":"Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pascoletti, G., Aldieri, A., Terzini, M., Bhattacharya, P., Cal\u00ec, M., and Zanetti, E.M. (2021). Stochastic PCA-based bone models from inverse transform sampling: Proof of concept for mandibles and proximal femurs. Appl. Sci., 11.","DOI":"10.3390\/app11115204"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8864","DOI":"10.1038\/s41598-023-35374-3","article-title":"A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms","volume":"13","author":"Yuan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_11","first-page":"143","article-title":"Radial Basis Functions for Multivariable Interpolation: A Review","volume":"2","author":"Powell","year":"2010","journal-title":"Front. Synaptic. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Biancolini, M.E., Chiappa, A., Cella, U., Costa, E., Groth, C., and Porziani, S. (2020). Radial Basis Functions Mesh Morphing, Springer.","DOI":"10.1007\/978-3-030-50433-5_23"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, W., Fu, Z.J., and Chen, C.S. (2014). Radial Basis Functions. Recent Advances in Radial Basis Function Collocation Methods, Springer. SpringerBriefs in Applied Sciences and Technology.","DOI":"10.1007\/978-3-642-39572-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4979","DOI":"10.1016\/j.apm.2015.03.049","article-title":"Finite integration method for solving multi-dimensional partial differential equations","volume":"39","author":"Li","year":"2015","journal-title":"Appl. Math. Model."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/72.80341","article-title":"Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks","volume":"2","author":"Chen","year":"1991","journal-title":"IEEE Trans. Neural. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104184","DOI":"10.1016\/j.compbiomed.2020.104184","article-title":"Numerical simulations of flow patterns in the human left ventricle model with a novel dynamic mesh morphing approach based on radial basis function","volume":"130","author":"Xu","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104937","DOI":"10.1016\/j.jmbbm.2021.104937","article-title":"Cardiac mesh morphing method for finite element modeling of heart failure with preserved ejection fraction","volume":"126","author":"Weissmann","year":"2022","journal-title":"J. Mech. Behav. Biomed. Mater."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1080\/10618562.2014.932352","article-title":"Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: Application to fluid\u2013structure interaction coupling problems","volume":"28","author":"Forti","year":"2014","journal-title":"Int. J. Comut. Fluid Dyn."},{"key":"ref_19","first-page":"395","article-title":"Data Assimilation in Cardiovascular Fluid\u2013Structure Interaction Problems: An Introduction","volume":"2014","author":"Bertagna","year":"2014","journal-title":"Fluid-Struct. Interact. Biomed. Appl."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/11\/216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:21:04Z","timestamp":1760113264000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/11\/216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"references-count":19,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["computation12110216"],"URL":"https:\/\/doi.org\/10.3390\/computation12110216","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2024,10,26]]}}}