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Bioengineering
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Advancements in Computational Modelling and Imaging Techniques for Personalised Treatment...
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Advancements in Computational Modelling and Imaging Techniques for Personalised Treatment of Aortic Aneurysms

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 2897

Special Issue Editors

Dr. José Xavier

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, Faculty of Sciences and Technology, Universidade NOVA de Lisboa, Lisbon, Portugal
Interests: biomechanical engineering; CFD; cardiovascular diseases modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Simona Celi

E-Mail Website
Guest Editor
Gabriele Monasterio Foundation, Pisa, Italy
Interests: vascular diseases; aneurysm; finite element method; computational fluid dynamics; biomechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The diagnosis and treatment of cardiovascular diseases, such as aortic aneurysms, have evolved significantly over the last few decades. Advances in surgical techniques and functional imaging have transformed modern clinical medicine, resulting in substantial social and economic impacts. This Special Issue aims to encompass the development of in silico modeling, computational fluid–structure interaction frameworks, advanced imaging techniques for monitoring tissue deformation, and the integration of clinical and imaging data to enhance the diagnosis and treatment planning of aortic aneurysms. We welcome contributions that explore novel computational models, experimental methodologies, and clinical applications to improve the understanding and management of thoracic aortic aneurysms. Additionally, submissions may cover research on uncertainty quantification, model validation, and the development of digital tools for patient-specific hemodynamic analysis. The ultimate goal of this Special Issue is to enhance clinical decision making and improve patient outcomes for this complex disease.

Dr. José Xavier
Dr. Simona Celi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiovascular diseases
  • silico modelling
  • computational fluid–structure interaction frameworks
  • advanced imaging techniques

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Published Papers (3 papers)

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16 pages, 79617 KB  
Article
An Integrated Framework for Automated Image Segmentation and Personalized Wall Stress Estimation of Abdominal Aortic Aneurysms
by Merjulah Roby, Juan C. Restrepo, Deepak K. Shan, Satish C. Muluk, Mark K. Eskandari, Vikram S. Kashyap and Ender A. Finol
Bioengineering 2026, 13(2), 191; https://doi.org/10.3390/bioengineering13020191 - 7 Feb 2026
Viewed by 243
Abstract
Abdominal Aortic Aneurysm (AAA) remains a significant public health challenge, with an 82.1% increase in related fatalities from 1990 to 2019. In the United States alone, AAA complications resulted in an estimated 13,640 deaths between 2018 and 2021. In clinical practice, computed tomography [...] Read more.
Abdominal Aortic Aneurysm (AAA) remains a significant public health challenge, with an 82.1% increase in related fatalities from 1990 to 2019. In the United States alone, AAA complications resulted in an estimated 13,640 deaths between 2018 and 2021. In clinical practice, computed tomography angiography (CTA) is the primary imaging modality for monitoring and pre-surgical planning of AAA patients. CTA provides high-resolution vascular imaging, enabling detailed assessments of aneurysm morphology and informing critical clinical decisions. However, manual segmentation of CTA images is labor-intensive and time consuming, underscoring the need for automated segmentation algorithms, particularly when feature extraction from clinical images can inform treatment decisions. We propose a framework to automatically segment the outer wall of the abdominal aorta from CTA images and estimate AAA wall stress. Our approach employs a patch-based dilated modified U-Net model to accurately delineate the outer wall boundary of AAAs and Nonlinear Elastic Membrane Analysis (NEMA) to estimate their wall stress. We further integrate Non-Uniform Rational B-Splines (NURBS) to refine the segmentation. During prediction, our deep learning architecture requires 17±0.02 milliseconds per frame to generate the final segmented output. The latter is used to provide critical insight into the biomechanical state of stress of an AAA. This modeling strategy merges advanced deep learning architecture, the precision of NURBS, and the advantages of NEMA to deliver a robust and efficient method for computational analysis of AAAs. Full article
(This article belongs to the Special Issue Advancements in Computational Modelling and Imaging Techniques for Personalised Treatment of Aortic Aneurysms)
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11 pages, 1085 KB  
Article
Can the Novel Photon-Counting CT Scan Accurately Predict Aortic Wall Thickness? Preliminary Results
by Alessandra Sala, Carlo de Vincentiis, Francesco Grimaldi, Barbara Rubino, Manuela Cirami, Noemi Perillo, Renato Vitale, Rosanna Cardani, Sara Boveri, Michele Conti and Pietro Spagnolo
Bioengineering 2025, 12(3), 306; https://doi.org/10.3390/bioengineering12030306 - 18 Mar 2025
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Abstract
Background: Surgical indication of ascending thoracic aortic aneurysms (ATAA) is generally performed in prevention. Guidelines use aortic diameter as a predictor of rupture and dissection; however, this single parameter alone has a limited value in predicting the real-world risk of acute aortic syndromes. [...] Read more.
Background: Surgical indication of ascending thoracic aortic aneurysms (ATAA) is generally performed in prevention. Guidelines use aortic diameter as a predictor of rupture and dissection; however, this single parameter alone has a limited value in predicting the real-world risk of acute aortic syndromes. The novel photon-counting CT scan(pc-CT) is capable of better-analyzing tissue composition and aortic characterization. The aim of the study is to assess whether the correlation between aortic wall thickness measured with a pc-CT scan and histology exists. Methods: 14 Patients, with a mean age of 47 years, undergoing cardiac surgery for ATAA, who had preoperatively undergone a pc-CT scan, were retrospectively analyzed. Histology analyses of the resected aortic wall aneurysm were reviewed, and minimum/maximum measurements of intima+media of the aortic wall were performed. Radiology images were also examined, and aortic wall thickness measures were taken. Bland-Altman plots and Passing-Bablock regression analyses were conducted to evaluate the correlation between the values. Results: pc-CT scan mean measurements were 1.05 and 1.69 mm, minimum/maximum, respectively. Mean minimum/maximum histology measurements were 1.66 and 2.82 mm, respectively. Bland Altman plots and Passing-Bablock regression analyses showed the absence of systematic bias and confirmed that measurement values were sufficiently similar (minimum −0.61 [CI 95% 0.16–1.38]; maximum −1.1 [0.73–2.99]). Conclusions: Despite results being merely preliminary, our study shows encouraging sufficiently similar results between aortic wall thickness measurements made with pc-CT scan and histology analyses. Full article
(This article belongs to the Special Issue Advancements in Computational Modelling and Imaging Techniques for Personalised Treatment of Aortic Aneurysms)
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16 pages, 321 KB  
Systematic Review
Quantifying In Vivo Arterial Deformation from CT and MRI: A Systematic Review of Segmentation, Motion Tracking, and Kinematic Metrics
by Rodrigo Valente, Bernardo Henriques, André Mourato, José Xavier, Moisés Brito, Stéphane Avril, António Tomás and José Fragata
Bioengineering 2026, 13(1), 121; https://doi.org/10.3390/bioengineering13010121 - 20 Jan 2026
Viewed by 312
Abstract
This article presents a systematic review on methods for quantifying three-dimensional, time-resolved (3D+t) deformation and motion of human arteries from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Scopus, Web [...] Read more.
This article presents a systematic review on methods for quantifying three-dimensional, time-resolved (3D+t) deformation and motion of human arteries from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Scopus, Web of Science, IEEE Xplore, Google Scholar, and PubMed on 19 December 2025 for in vivo, patient-specific CT or MRI studies reporting motion or deformation of large human arteries. We included studies that quantified arterial deformation or motion tracking and excluded non-vascular tissues, in vitro or purely computational work. Thirty-five studies were included in the qualitative synthesis; most were small, single-centre observational cohorts. Articles were analysed qualitatively, and results were synthesised narratively. Across the 35 studies, the most common segmentation approaches are active contours and threshold, while temporal motion is tracked using either voxel registration or surface methods. These kinematic data are used to compute metrics such as circumferential and longitudinal strain, distensibility, and curvature. Several studies also employ inverse methods to estimate wall stiffness. The findings consistently show that arterial strain decreases with age (on the order of 20% per decade in some cases) and in the presence of disease, that stiffness correlates with geometric remodelling, and that deformation is spatially heterogeneous. However, insufficient data prevents meaningful comparison across methods. Full article
(This article belongs to the Special Issue Advancements in Computational Modelling and Imaging Techniques for Personalised Treatment of Aortic Aneurysms)
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