Academic Journal
Precision CT-based Aortic Valve Reconstruction: Minimal Variation Geometry Invariant Parametric Reconstruction Approach for Aortic Stenosis and Bicuspid Valves.
| Title: | Precision CT-based Aortic Valve Reconstruction: Minimal Variation Geometry Invariant Parametric Reconstruction Approach for Aortic Stenosis and Bicuspid Valves. |
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| Authors: | Abdelkhalek M; School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada., Keshavarz-Motamed Z; School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada; Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada; School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada. Electronic address: motamedz@mcmaster.ca. |
| Source: | Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2026 Jan; Vol. 273, pp. 109071. Date of Electronic Publication: 2025 Sep 06. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Scientific Publishers Original Publication: Amsterdam : Elsevier Science Publishers, c1984- |
| MeSH Terms: | Aortic Valve Stenosis*/diagnostic imaging , Aortic Valve*/diagnostic imaging , Aortic Valve*/abnormalities , Image Processing, Computer-Assisted*/methods , Tomography, X-Ray Computed*, Bicuspid Aortic Valve Disease/diagnostic imaging ; Humans ; Retrospective Studies ; Phantoms, Imaging ; Algorithms ; X-Ray Microtomography ; Reproducibility of Results |
| Abstract: | Background and Objectives: Accurate reconstruction of the aortic valve complex is challenging due to its significant anatomical variability, presence of calcifications, and dynamic deformations across cardiac cycles. This study aimed to introduce and evaluate a novel, fully automated pipeline-Minimal Variation Geometry Invariant Parametric Reconstruction (MVGIPR)-for precisely reconstructing the aortic valve complex from computed tomography scans. By integrating computer-aided design, image processing, meshing, and geometry optimization into one cohesive framework, MVGIPR seeks to advance diagnostic and interventional planning in both aortic stenosis and bicuspid aortic valve cases. Methods: We retrospectively analyzed 80 patient datasets (covering aortic stenosis and bicuspid aortic valve pathologies), supplemented by ex-vivo micro-computed tomography and synthetic phantom experiments. The fully automated pipeline employed advanced computer-aided design for constructing parametric models, coupled with a semi-automated image processing workflow for segmenting and isolating the aortic valve complex. Further, a specialized meshing algorithm generated high-fidelity surface representations, while differential geometry metrics-specifically length, curvature, and torsion-were optimized to minimize noise and preserve essential anatomical details. Results: MVGIPR demonstrated strong concordance with high-resolution imaging and ground truth datasets. In clinical scans, minimal mean signed Euclidean distance errors of -0.2 ± 0.1 mm and -0.1 ± 0.08 mm were observed, whereas ex-vivo micro-computed tomography assessments yielded 233 ± 447 μm errors. Phantom data reconstructions showed negligible deviations (0.003 ± 0.04 mm) and a 2.9% surface area discrepancy, underscoring the pipeline's robustness in both planar and three-dimensional applications. Additionally, the automated approach reduced inter-observer variability by providing consistent, reproducible reconstructions across diverse imaging modalities. Conclusions: MVGIPR offers a comprehensive, fully automated pipeline that integrates computer-aided design, image processing, meshing, and geometry optimization. By preserving critical anatomical features and minimizing noise, the method addresses key challenges in aortic valve complex reconstruction, thereby enhancing diagnostic and prognostic assessments. Its proven accuracy across clinical, ex-vivo, and synthetic settings underscores its potential for broader clinical integration, including enabling personalized treatment strategies and improving outcomes in valvular heart diseases by providing a robust foundation for advanced interventional planning. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Aortic stenosis; NURBS; bicuspid aortic valve; calcification; computer aided design; contrast-enhanced CT; geometric reconstruction |
| Entry Date(s): | Date Created: 20251016 Date Completed: 20251112 Latest Revision: 20251112 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.cmpb.2025.109071 |
| PMID: | 41101036 |
| Database: | MEDLINE |
| ISSN: | 1872-7565 |
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| DOI: | 10.1016/j.cmpb.2025.109071 |