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Dong H, Leach JR, Kao E, Zhou A, Chitiboi T, Zhu C, Ballweber M, Jiang F, Lee YJ, Iannuzzi J, Gasper W, Saloner D, Hope MD, Mitsouras D. Measurement of Abdominal Aortic Aneurysm Strain Using MR Deformable Image Registration: Accuracy and Relationship to Recent Aneurysm Progression. Invest Radiol 2024; 59:425-432. [PMID: 37855728 PMCID: PMC11026303 DOI: 10.1097/rli.0000000000001035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
BACKGROUND Management of asymptomatic abdominal aortic aneurysm (AAA) based on maximum aneurysm diameter and growth rate fails to preempt many ruptures. Assessment of aortic wall biomechanical properties may improve assessment of progression and rupture risk. This study aimed to assess the accuracy of AAA wall strain measured by cine magnetic resonance imaging (MRI) deformable image registration (MR strain) and investigate its relationship with recent AAA progression. METHODS The MR strain accuracy was evaluated in silico against ground truth strain in 54 synthetic MRIs generated from a finite element model simulation of an AAA patient's abdomen for different aortic pulse pressures, tissue motions, signal intensity variations, and image noise. Evaluation included bias with 95% confidence interval (CI) and correlation analysis. Association of MR strain with AAA growth rate was assessed in 25 consecutive patients with >6 months of prior surveillance, for whom cine balanced steady-state free-precession imaging was acquired at the level of the AAA as well as the proximal, normal-caliber aorta. Univariate and multivariate regressions were used to associate growth rate with clinical variables, maximum AAA diameter (D max ), and peak circumferential MR strain through the cardiac cycle. The MR strain interoperator variability was assessed using bias with 95% CI, intraclass correlation coefficient, and coefficient of variation. RESULTS In silico experiments revealed an MR strain bias of 0.48% ± 0.42% and a slope of correlation to ground truth strain of 0.963. In vivo, AAA MR strain (1.2% ± 0.6%) was highly reproducible (bias ± 95% CI, 0.03% ± 0.31%; intraclass correlation coefficient, 97.8%; coefficient of variation, 7.14%) and was lower than in the nonaneurysmal aorta (2.4% ± 1.7%). D max ( β = 0.087) and MR strain ( β = -1.563) were both associated with AAA growth rate. The MR strain remained an independent factor associated with growth rate ( β = -0.904) after controlling for D max . CONCLUSIONS Deformable image registration analysis can accurately measure the circumferential strain of the AAA wall from standard cine MRI and may offer patient-specific insight regarding AAA progression.
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Affiliation(s)
- Huiming Dong
- From the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (H.D., J.L., E.K., A.Z., C.Z., M.B., Y.J.L., D.S., M.H., D.M.); Vascular Imaging Research Center, San Francisco Veteran Affairs Medical Center, San Francisco, CA (H.D., J.L., E.K., A.Z., C.Z., M.B., D.S., M.H., D.M.); Siemens Healthineers (T.C.); Department of Radiology, University of Washington, Seattle, WA (C.Z.); Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA (F.J.); Department of Surgery, University of California, San Francisco, San Francisco, CA (J.I., W. G.); and Department of Vascular Surgery, San Francisco Veteran Affairs Medical Center, San Francisco, CA (J.I., W.G.)
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Chung TK, Gueldner PH, Aloziem OU, Liang NL, Vorp DA. An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes. Sci Rep 2024; 14:3390. [PMID: 38336915 PMCID: PMC10858046 DOI: 10.1038/s41598-024-53459-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/31/2024] [Indexed: 02/12/2024] Open
Abstract
Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their clinically-estimated risk of rupture-an event that is the 13th leading cause of death in the US-exceeds the risk associated with repair. Yet the current clinical guideline remains a one-size-fits-all "maximum diameter criterion" whereby AAA exceeding a threshold diameter is thought to make the risk of rupture high enough to warrant intervention. However, between 7 and 23.4% of smaller-sized AAA have been reported to rupture with diameters below the threshold. In this study, we train and assess machine learning models using clinical, biomechanical, and morphological indices from 381 patients to develop an aneurysm prognosis classifier to predict one of three outcomes for a given AAA patient: their AAA will remain stable, their AAA will require repair based as currently indicated from the maximum diameter criterion, or their AAA will rupture. This study represents the largest cohort of AAA patients that utilizes the first available medical image and clinical data to classify patient outcomes. The APC model therefore represents a potential clinical tool to striate specific patient outcomes using machine learning models and patient-specific image-based (biomechanical and morphological) and clinical data as input. Such a tool could greatly assist clinicians in their management decisions for patients with AAA.
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Affiliation(s)
- Timothy K Chung
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Pete H Gueldner
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Okechukwu U Aloziem
- School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nathan L Liang
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Vascular Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Vorp
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical & Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, USA.
- Bioengineering, Cardiothoracic Surgery, Surgery, Chemical and Petroleum Engineering and the Clinical and Translational Sciences Institute, Center for Bioengineering, University of Pittsburgh, 300 Technology Drive, Suite 300, Pittsburgh, PA, 15219, USA.
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Singh TP, Moxon JV, Gasser TC, Jenkins J, Bourke M, Bourke B, Golledge J. Association between aortic peak wall stress and rupture index with abdominal aortic aneurysm-related events. Eur Radiol 2023; 33:5698-5706. [PMID: 36897345 PMCID: PMC10326087 DOI: 10.1007/s00330-023-09488-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 01/23/2023] [Accepted: 01/27/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVE The aim of this study was to assess whether aortic peak wall stress (PWS) and peak wall rupture index (PWRI) were associated with the risk of abdominal aortic aneurysm (AAA) rupture or repair (defined as AAA events) among participants with small AAAs. METHODS PWS and PWRI were estimated from computed tomography angiography (CTA) scans of 210 participants with small AAAs (≥ 30 and ≤ 50 mm) prospectively recruited between 2002 and 2016 from two existing databases. Participants were followed for a median of 2.0 (inter-quartile range 1.9, 2.8) years to record the incidence of AAA events. The associations between PWS and PWRI with AAA events were assessed using Cox proportional hazard analyses. The ability of PWS and PWRI to reclassify the risk of AAA events compared to the initial AAA diameter was examined using net reclassification index (NRI) and classification and regression tree (CART) analysis. RESULTS After adjusting for other risk factors, one standard deviation increase in PWS (hazard ratio, HR, 1.56, 95% confidence intervals, CI 1.19, 2.06; p = 0.001) and PWRI (HR 1.74, 95% CI 1.29, 2.34; p < 0.001) were associated with significantly higher risks of AAA events. In the CART analysis, PWRI was identified as the best single predictor of AAA events at a cut-off value of > 0.562. PWRI, but not PWS, significantly improved the classification of risk of AAA events compared to the initial AAA diameter alone. CONCLUSION PWS and PWRI predicted the risk of AAA events but only PWRI significantly improved the risk stratification compared to aortic diameter alone. KEY POINTS • Aortic diameter is an imperfect measure of abdominal aortic aneurysm (AAA) rupture risk. • This observational study of 210 participants found that peak wall stress (PWS) and peak wall rupture index (PWRI) predicted the risk of aortic rupture or AAA repair. • PWRI, but not PWS, significantly improved the risk stratification for AAA events compared to aortic diameter alone.
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Affiliation(s)
- Tejas P Singh
- Queensland Research Centre for Peripheral Vascular Disease, College of Medicine and Dentistry, James Cook University, Townsville, Queensland, 4811, Australia
- The Department of Vascular and Endovascular Surgery, The Townsville University Hospital, Townsville, Queensland, Australia
| | - Joseph V Moxon
- Queensland Research Centre for Peripheral Vascular Disease, College of Medicine and Dentistry, James Cook University, Townsville, Queensland, 4811, Australia
- The Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
| | - T Christian Gasser
- Department of Engineering Mechanics, KTH Solid Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jason Jenkins
- Department of Vascular and Endovascular Surgery, Royal Brisbane and Women's Hospital Brisbane, Herston, Queensland, Australia
| | - Michael Bourke
- Gosford Vascular Services Gosford New South Wales Australia, Gosford, Australia
- The School of Biomedical Sciences & Pharmacy, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Benard Bourke
- Gosford Vascular Services Gosford New South Wales Australia, Gosford, Australia
| | - Jonathan Golledge
- Queensland Research Centre for Peripheral Vascular Disease, College of Medicine and Dentistry, James Cook University, Townsville, Queensland, 4811, Australia.
- The Department of Vascular and Endovascular Surgery, The Townsville University Hospital, Townsville, Queensland, Australia.
- The Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia.
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Arslan AC, Salman HE. Effect of Intraluminal Thrombus Burden on the Risk of Abdominal Aortic Aneurysm Rupture. J Cardiovasc Dev Dis 2023; 10:233. [PMID: 37367398 DOI: 10.3390/jcdd10060233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/19/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
Abdominal aortic aneurysm (AAA) is a critical health disorder, where the abdominal aorta dilates more than 50% of its normal diameter. Enlargement in abdominal aorta alters the hemodynamics and flow-induced forces on the AAA wall. Depending on the flow conditions, the hemodynamic forces on the wall may result in excessive mechanical stresses that lead to AAA rupture. The risk of rupture can be predicted using advanced computational techniques such as computational fluid dynamics (CFD) and fluid-structure interaction (FSI). For a reliable rupture risk assessment, formation of intraluminal thrombus (ILT) and uncertainty in arterial material properties should be taken into account, mainly due to the patient-specific differences and unknowns in AAAs. In this study, AAA models are computationally investigated by performing CFD simulations combined with FSI analysis. Various levels of ILT burdens are artificially generated in a realistic AAA geometry, and the peak effective stresses are evaluated to elucidate the effect of material models and ILT formation. The results indicate that increasing the ILT burden leads to lowered effective stresses on the AAA wall. The material properties of the artery and ILT are also effective on the stresses; however, these effects are limited compared to the effect of ILT volume in the AAA sac.
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Affiliation(s)
- Aykut Can Arslan
- Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara 06530, Turkey
| | - Huseyin Enes Salman
- Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara 06530, Turkey
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Liang L, Liu M, Elefteriades J, Sun W. Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications for Biomechanical Analysis of Human Aorta. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535423. [PMID: 37066215 PMCID: PMC10104001 DOI: 10.1101/2023.04.03.535423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Motivation: Patient-specific finite element analysis (FEA) has the potential to aid in the prognosis of cardiovascular diseases by providing accurate stress and deformation analysis in various scenarios. It is known that patient-specific FEA is time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) techniques, including deep neural networks (DNNs), have been developed to construct fast FEA surrogates. However, due to the data-driven nature of these ML models, they may not generalize well on new data, leading to unacceptable errors. Methods We propose a synergistic integration of DNNs and finite element method (FEM) to overcome each other’s limitations. We demonstrated this novel integrative strategy in forward and inverse problems. For the forward problem, we developed DNNs using state-of-the-art architectures, and DNN outputs were then refined by FEM to ensure accuracy. For the inverse problem of heterogeneous material parameter identification, our method employs a DNN as regularization for the inverse analysis process to avoid erroneous material parameter distribution. Results We tested our methods on biomechanical analysis of the human aorta. For the forward problem, the DNN-only models yielded acceptable stress errors in majority of test cases; yet, for some test cases that could be out of the training distribution (OOD), the peak stress errors were larger than 50%. The DNN-FEM integration eliminated the large errors for these OOD cases. Moreover, the DNN-FEM integration was magnitudes faster than the FEM-only approach. For the inverse problem, the FEM-only inverse method led to errors larger than 50%, and our DNN-FEM integration significantly improved performance on the inverse problem with errors less than 1%.
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Liang L, Liu M, Elefteriades J, Sun W. PyTorch-FEA: Autograd-enabled Finite Element Analysis Methods with Applications for Biomechanical Analysis of Human Aorta. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.533816. [PMID: 37034587 PMCID: PMC10081215 DOI: 10.1101/2023.03.27.533816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Motivation Finite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed. Methods In this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance. Results We applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs.
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Dynamic Loading-A New Marker for Abdominal Aneurysm Growth? MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59020404. [PMID: 36837605 PMCID: PMC9967562 DOI: 10.3390/medicina59020404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/07/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023]
Abstract
The growing possibilities of non-invasive heart rate and blood pressure measurement with mobile devices allow vital data to be continuously collected and used to assess patients' health status. When it comes to the risk assessment of abdominal aortic aneurysms (AAA), the continuous tracking of blood pressure and heart rate could enable a more patient-specific approach. The use of a load function and an energy function, with continuous blood pressure, heart rate, and aneurysm stiffness as input parameters, can quantify dynamic load on AAA. We hypothesise that these load functions correlate with aneurysm growth and outline a possible study procedure in which the hypothesis could be tested for validity. Subsequently, uncertainty quantification of input quantities and derived quantities is performed.
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Sjoerdsma M, Verstraeten SCFPM, Maas EJ, van de Vosse FN, van Sambeek MRHM, Lopata RGP. Spatiotemporal Registration of 3-D Multi-perspective Ultrasound Images of Abdominal Aortic Aneurysms. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:318-332. [PMID: 36441033 DOI: 10.1016/j.ultrasmedbio.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/02/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Methods for patient-specific abdominal aortic aneurysm (AAA) progression monitoring and rupture risk assessment are widely investigated. Three-dimensional ultrasound can visualize the AAA's complex geometry and displacement fields. However, ultrasound has a limited field of view and low frame rate (i.e., 3-8 Hz). This article describes an approach to enhance the temporal resolution and the field of view. First, the frame rate was increased for each data set by sequencing multiple blood pulse cycles into one cycle. The sequencing method uses the original frame rate and the estimated pulse wave rate obtained from AAA distension curves. Second, the temporal registration was applied to multi-perspective acquisitions of the same AAA. Third, the field of view was increased through spatial registration and fusion using an image feature-based phase-only correlation method and a wavelet transform, respectively. Temporal sequencing was fully correct in aortic phantoms and was successful in 51 of 62 AAA patients, yielding a factor 5 frame rate increase. Spatial registration of proximal and distal ultrasound acquisitions was successful in 32 of 37 different AAA patients, based on the comparison between the fused ultrasound and computed tomography segmentation (95th percentile Haussdorf distances and similarity indices of 4.2 ± 1.7 mm and 0.92 ± 0.02 mm, respectively). Furthermore, the field of view was enlarged by 9%-49%.
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Affiliation(s)
- Marloes Sjoerdsma
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.
| | - Sabine C F P M Verstraeten
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Cardiovascular Biomechanics Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Esther J Maas
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Frans N van de Vosse
- Cardiovascular Biomechanics Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marc R H M van Sambeek
- Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Richard G P Lopata
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Lorandon F, Rinckenbach S, Settembre N, Steinmetz E, Mont LSD, Avril S. Stress Analysis in AAA does not Predict Rupture Location Correctly in Patients with Intraluminal Thrombus. Ann Vasc Surg 2021; 79:279-289. [PMID: 34648863 DOI: 10.1016/j.avsg.2021.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 08/21/2021] [Accepted: 08/31/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND A biomechanical approach to the rupture risk of an abdominal aortic aneurysm could be a solution to ensure a personalized estimate of this risk. It is still difficult to know in what conditions, the assumptions made by biomechanics, are valid. The objective of this work was to determine the individual biomechanical rupture threshold and to assess the correlation between their rupture sites and the locations of their maximum stress comparing two computed tomography scan (CT) before and at time of rupture. METHODS We included 5 patients who had undergone two CT; one within the last 6 months period before rupture and a second CT scan just before the surgical procedure for the rupture. All DICOM data, both pre- and rupture, were processed following the same following steps: generation of a 3D geometry of the abdominal aortic aneurysm, meshing and computational stress analysis using the finite element method. We used two different modelling scenarios to study the distribution of the stresses, a "wall" model without intraluminal thrombus (ILT) and a "thrombus" model with ILT. RESULTS The average time between the pre-rupture and rupture CT scans was 44 days (22-97). The median of the maximum stresses applied to the wall between the pre-rupture and rupture states were 0.817 MPa (0.555-1.295) and 1.160 MPa (0.633-1.625) for the "wall" model; and 0.365 MPa (0.291-0.753) and 0.390 MPa (0.343-0.819) for the "thrombus" model. There was an agreement between the site of rupture and the location of maximum stress for only 1 patient, who was the only patient without ILT. CONCLUSIONS We observed a large variability of stress values at rupture sites between patients. The rupture threshold strongly varied between individuals depending on the intraluminal thrombus. The site of rupture did not correlate with the maximum stress except for 1 patient.
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Affiliation(s)
- Fanny Lorandon
- Department of Vascular and Endovascular Surgery, University Hospital of Besançon, Besançon, Saint Etienne, France..
| | - Simon Rinckenbach
- Department of Vascular and Endovascular Surgery, University Hospital of Besançon, Besançon, Saint Etienne, France.; EA3920, University Hospital of Besançon, Besançon, France
| | - Nicla Settembre
- Department of Vascular Surgery, University Hospital of Nancy, Nancy, France
| | - Eric Steinmetz
- Department of Vascular Surgery, University Hospital of Dijon, Dijon, France
| | - Lucie Salomon Du Mont
- Department of Vascular and Endovascular Surgery, University Hospital of Besançon, Besançon, Saint Etienne, France.; EA3920, University Hospital of Besançon, Besançon, France
| | - Stephane Avril
- Mines Saint-Etienne, Univ Lyon, INSERM, U 1059 Sainbiose, Centre CIS, F - 42023 Saint-Etienne, France..
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Lindquist Liljeqvist M, Bogdanovic M, Siika A, Gasser TC, Hultgren R, Roy J. Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms. Sci Rep 2021; 11:18040. [PMID: 34508118 PMCID: PMC8433325 DOI: 10.1038/s41598-021-96512-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/02/2021] [Indexed: 12/17/2022] Open
Abstract
It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomatic aneurysm. 189 patients with AAAs of diameters 40–50 mm were included, 161 had undergone two CTAs. Geometric and biomechanical variables were used in prediction modelling. Classifications were evaluated with area under receiver operating characteristic curve (AUC) and regressions with correlation between observed and predicted growth rates. Compared with the baseline clinical diameter, geometric-biomechanical analysis improved prediction of reaching surgical threshold within four years (AUC 0.80 vs 0.85, p = 0.031) and prediction of diameter growth rate (r = 0.17 vs r = 0.38, p = 0.0031), mainly due to the addition of semiautomatic diameter measurements. There was a trend towards increased precision of volume growth rate prediction (r = 0.37 vs r = 0.45, p = 0.081). Lumen diameter and biomechanical indices were the only variables that could predict future rupture or symptomatic AAA (AUCs 0.65–0.67). Enhanced precision of diameter measurements improves the prediction of reaching the surgical threshold and diameter growth rate, while lumen diameter and biomechanical analysis predicts rupture or symptomatic AAA.
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Affiliation(s)
- Moritz Lindquist Liljeqvist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden. .,Department of Vascular Surgery, Karolinska University Hospital, Stockholm, Sweden.
| | - Marko Bogdanovic
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Antti Siika
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - T Christian Gasser
- Department of Engineering Mechanics, Royal Institute of Technology, Stockholm, Sweden
| | - Rebecka Hultgren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Vascular Surgery, Karolinska University Hospital, Stockholm, Sweden
| | - Joy Roy
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Vascular Surgery, Karolinska University Hospital, Stockholm, Sweden
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Petterson N, Sjoerdsma M, van Sambeek M, van de Vosse F, Lopata R. Mechanical characterization of abdominal aortas using multi-perspective ultrasound imaging. J Mech Behav Biomed Mater 2021; 119:104509. [PMID: 33865067 DOI: 10.1016/j.jmbbm.2021.104509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/13/2021] [Accepted: 03/30/2021] [Indexed: 11/17/2022]
Abstract
Mechanical characterization of abdominal aortic aneurysms using personalized biomechanical models is being widely investigated as an alternative criterion to assess risk of rupture. These methods rely on accurate wall motion detection and appropriate model boundary conditions. In this study, multi-perspective ultrasound is combined with finite element models to perform mechanical characterization of abdominal aortas in volunteers. Multi-perspective biplane radio frequency ultrasound recordings were made under seven angles (-45° to 45°) in one phantom set-up and eight volunteers, which were merged using automatic image registration. 2-D displacement fields were estimated in the seven longitudinal ultrasound views, creating a sparse, high resolution 3-D map of the wall motion at relatively high frame rates (20-27 Hz). The displacements were used to personalize the subject-specific finite element model of which the geometry of the aorta, spine, and surrounding tissue were determined from a single 3-D ultrasound acquisition. Automatic registration of the multi-perspective images was successful in six out of eight cases with an average error of 5.4° compared to the ground truth. Displacements of the aortic wall were measured and cyclic strain of the aortic diameter was found ranging from 4.2% to 8.6%. The subject-specific mesh and inverse FE analysis was performed yielding shear moduli estimates for the wall between 104 and 215 kPa. Comparative results from a single-perspective workflow revealed very low aortic wall motion signal, which resulted in relatively high modulus estimates, between 230 and 754 kPa. Multi-perspective biplane ultrasound imaging was used to personalize finite element models of the abdominal aorta and its surroundings, and performing mechanical characterization of the aortic shear modulus. The method was found to be a more robust method compared to a single-perspective 3-D ultrasound approach. Future research will focus on investigating the use of multiple 3-D ultrasound acquisitions, the feasibility of free-hand scanning, the creation of a full 3-D automatic registration process, and with that, enable a clinical continuation of this study.
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Affiliation(s)
- Niels Petterson
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, the Netherlands
| | - Marloes Sjoerdsma
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, the Netherlands.
| | - Marc van Sambeek
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, the Netherlands; Department of Vascular Surgery, Catharina Hospital Eindhoven, Michelangelolaan 2, 5623 EJ, Eindhoven, the Netherlands
| | - Frans van de Vosse
- Cardiovascular Biomechanics Group, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, the Netherlands
| | - Richard Lopata
- Photoacoustics & Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, the Netherlands
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12
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Zhang D, Guan L, Li X. Bioinformatics analysis identifies potential diagnostic signatures for coronary artery disease. J Int Med Res 2020; 48:300060520979856. [PMID: 33356708 PMCID: PMC7840986 DOI: 10.1177/0300060520979856] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background Coronary artery disease (CAD) is the leading cause of mortality worldwide. We
aimed to screen out potential gene signatures and construct a diagnostic
model for CAD. Method We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed
analyses of differential expression, gene ontology terms, and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was
used to identify protein–protein interactions (PPI). PPI network
visualization and screening out of key genes were performed using Cytoscape
software. Finally, a diagnostic model was constructed. Results A total of 2127 differentially expressed genes (DEGs) were identified in
GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that
showed differential expression between CAD patients and controls, 471
biological process terms, 35 cellular component terms, 17 molecular function
terms, and 49 KEGG pathways were significantly enriched. The top 20 key
genes in the PPI network were identified, and a diagnostic model constructed
from five optimal genes that could efficiently separate CAD patients from
controls. Conclusion We identified several potential biomarkers for CAD and built a logistic
regression model that will provide a valuable reference for future clinical
diagnoses and guide therapeutic strategies.
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Affiliation(s)
- Dong Zhang
- The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Department of Cardiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Liying Guan
- Health Management Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xiaoming Li
- Health Management Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
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13
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Bruder L, Pelisek J, Eckstein HH, Gee MW. Biomechanical rupture risk assessment of abdominal aortic aneurysms using clinical data: A patient-specific, probabilistic framework and comparative case-control study. PLoS One 2020; 15:e0242097. [PMID: 33211767 PMCID: PMC7676745 DOI: 10.1371/journal.pone.0242097] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/26/2020] [Indexed: 11/18/2022] Open
Abstract
We present a data-informed, highly personalized, probabilistic approach for the quantification of abdominal aortic aneurysm (AAA) rupture risk. Our novel framework builds upon a comprehensive database of tensile test results that were carried out on 305 AAA tissue samples from 139 patients, as well as corresponding non-invasively and clinically accessible patient-specific data. Based on this, a multivariate regression model is created to obtain a probabilistic description of personalized vessel wall properties associated with a prospective AAA patient. We formulate a probabilistic rupture risk index that consistently incorporates the available statistical information and generalizes existing approaches. For the efficient evaluation of this index, a flexible Kriging-based surrogate model with an active training process is proposed. In a case-control study, the methodology is applied on a total of 36 retrospective, diameter matched asymptomatic (group 1, n = 18) and known symptomatic/ruptured (group 2, n = 18) cohort of AAA patients. Finally, we show its efficacy to discriminate between the two groups and demonstrate competitive performance in comparison to existing deterministic and probabilistic biomechanical indices.
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Affiliation(s)
- Lukas Bruder
- Mechanics & High Performance Computing Group, Technical University of Munich, Garching, Germany
| | - Jaroslav Pelisek
- Department of Vascular Surgery, University Hospital Zurich, Zurich, Switzerland
- Clinic for Vascular and Endovascular Surgery, Technical University of Munich, Munich, Germany
| | - Hans-Henning Eckstein
- Clinic for Vascular and Endovascular Surgery, Technical University of Munich, Munich, Germany
| | - Michael W. Gee
- Mechanics & High Performance Computing Group, Technical University of Munich, Garching, Germany
- * E-mail:
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14
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Biomechanical indices are more sensitive than diameter in predicting rupture of asymptomatic abdominal aortic aneurysms. J Vasc Surg 2020; 71:617-626.e6. [DOI: 10.1016/j.jvs.2019.03.051] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/07/2019] [Indexed: 11/23/2022]
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15
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Rengarajan B, Wu W, Wiedner C, Ko D, Muluk SC, Eskandari MK, Menon PG, Finol EA. A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms. Ann Biomed Eng 2020; 48:1419-1429. [PMID: 31980998 DOI: 10.1007/s10439-020-02461-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/20/2020] [Indexed: 12/14/2022]
Abstract
The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1-6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.
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Affiliation(s)
- Balaji Rengarajan
- Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA
| | - Wei Wu
- Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA
| | - Crystal Wiedner
- Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, TX, USA
| | - Daijin Ko
- Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, TX, USA
| | - Satish C Muluk
- Department of Thoracic & Cardiovascular Surgery, Allegheny Health Network, Allegheny General Hospital, Pittsburgh, PA, USA
| | - Mark K Eskandari
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Prahlad G Menon
- Department of Mathematics and Data Analytics, Carlow University, Pittsburgh, PA, USA
| | - Ender A Finol
- Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA.
- UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, San Antonio, TX, USA.
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16
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Cavinato C, Molimard J, Curt N, Campisi S, Orgéas L, Badel P. Does the Knowledge of the Local Thickness of Human Ascending Thoracic Aneurysm Walls Improve Their Mechanical Analysis? Front Bioeng Biotechnol 2019; 7:169. [PMID: 31380360 PMCID: PMC6646470 DOI: 10.3389/fbioe.2019.00169] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/02/2019] [Indexed: 12/22/2022] Open
Abstract
Ascending thoracic aortic aneurysm (ATAA) ruptures are life threatening phenomena which occur in local weaker regions of the diseased aortic wall. As ATAAs are evolving pathologies, their growth represents a significant local remodeling and degradation of the microstructural architecture and thus their mechanical properties. To address the need for deeper study of ATAAs and their failure, it is required to analyze the mechanical behavior at the sub-millimeter scale by making use of accurate geometrical and kinematical measurements during their deformation. For this purpose, we propose a novel methodology that combined an accurate tool for thickness distribution measurement of the arterial wall, digital image correlation to assess local strain fields and bulge inflation to characterize the physiological and failure response of flat unruptured human ATAA specimens. The analysis of the heterogeneity of the local thickness and local physiological stress and strain was carried out for each investigated subject. At the subject level, our results state the presence of a non-consistent relationship between the local wall thickness and the local physiological strain field and high heterogeneity of the variables. At the inter-subject level, thicknesses were studied in relation to physiological strain and stress and load at rupture. The rupture pressure was correlated with neither the average thickness nor the lowest thickness of the specimens. Our results confirm that intrinsic material strength (hence structure) differs a lot from a subject to another and even within the same subject.
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Affiliation(s)
- Cristina Cavinato
- Mines Saint-Etienne, Centre CIS, INSERM, U 1059 Sainbiose, Univ Lyon, Univ Jean Monnet, Saint-Etienne, France
| | - Jerome Molimard
- Mines Saint-Etienne, Centre CIS, INSERM, U 1059 Sainbiose, Univ Lyon, Univ Jean Monnet, Saint-Etienne, France
| | - Nicolas Curt
- Mines Saint-Etienne, Centre CIS, INSERM, U 1059 Sainbiose, Univ Lyon, Univ Jean Monnet, Saint-Etienne, France
| | - Salvatore Campisi
- Department of CardioVascular Surgery, CHU Hôpital Nord Saint-Etienne, Saint-Etienne, France
| | - Laurent Orgéas
- UMR 5521, Univ. Grenoble Alpes, CNRS, Grenoble INP, 3SR Lab, Grenoble, France
| | - Pierre Badel
- Mines Saint-Etienne, Centre CIS, INSERM, U 1059 Sainbiose, Univ Lyon, Univ Jean Monnet, Saint-Etienne, France
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17
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Petterson NJ, van Disseldorp EM, van Sambeek MR, van de Vosse FN, Lopata RG. Including surrounding tissue improves ultrasound-based 3D mechanical characterization of abdominal aortic aneurysms. J Biomech 2019; 85:126-133. [DOI: 10.1016/j.jbiomech.2019.01.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 12/14/2018] [Accepted: 01/10/2019] [Indexed: 01/05/2023]
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18
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Trachet B, Lovric G, Villanueva-Perez P, Aslanidou L, Ferraro M, Logghe G, Stergiopulos N, Segers P. Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation? Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aaeb65] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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19
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Farotto D, Segers P, Meuris B, Vander Sloten J, Famaey N. The role of biomechanics in aortic aneurysm management: requirements, open problems and future prospects. J Mech Behav Biomed Mater 2018; 77:295-307. [DOI: 10.1016/j.jmbbm.2017.08.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 08/09/2017] [Accepted: 08/15/2017] [Indexed: 12/18/2022]
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20
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Stevens RRF, Grytsan A, Biasetti J, Roy J, Lindquist Liljeqvist M, Gasser TC. Biomechanical changes during abdominal aortic aneurysm growth. PLoS One 2017; 12:e0187421. [PMID: 29112945 PMCID: PMC5675455 DOI: 10.1371/journal.pone.0187421] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 10/19/2017] [Indexed: 12/11/2022] Open
Abstract
The biomechanics-based Abdominal Aortic Aneurysm (AAA) rupture risk assessment has gained considerable scientific and clinical momentum. However, such studies have mainly focused on information at a single time point, and little is known about how AAA properties change over time. Consequently, the present study explored how geometry, wall stress-related and blood flow-related biomechanical properties change during AAA expansion. Four patients with a total of 23 Computed Tomography-Angiography (CT-A) scans at different time points were analyzed. At each time point, patient-specific properties were extracted from (i) the reconstructed geometry, (ii) the computed wall stress at Mean Arterial Pressure (MAP), and (iii) the computed blood flow velocity at standardized inflow and outflow conditions. Testing correlations between these parameters identified several nonintuitive dependencies. Most interestingly, the Peak Wall Rupture Index (PWRI) and the maximum Wall Shear Stress (WSS) independently predicted AAA volume growth. Similarly, Intra-luminal Thrombus (ILT) volume growth depended on both the maximum WSS and the ILT volume itself. In addition, ILT volume, ILT volume growth, and maximum ILT layer thickness correlated with PWRI as well as AAA volume growth. Consequently, a large ILT volume as well as fast increase of ILT volume over time may be a risk factor for AAA rupture. However, tailored clinical studies would be required to test this hypothesis and to clarify whether monitoring ILT development has any clinical benefit.
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Affiliation(s)
- Raoul R. F. Stevens
- Department of Biomedical Engineering, University of Technology, Eindhoven, The Netherlands
- Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands
- KTH Solid Mechanics, School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Andrii Grytsan
- KTH Solid Mechanics, School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jacopo Biasetti
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, United States of America
| | - Joy Roy
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | | | - T. Christian Gasser
- KTH Solid Mechanics, School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden
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21
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Chung TK, da Silva ES, Raghavan SM. Does elevated wall tension cause aortic aneurysm rupture? Investigation using a subject-specific heterogeneous model. J Biomech 2017; 64:164-171. [DOI: 10.1016/j.jbiomech.2017.09.041] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 07/30/2017] [Accepted: 09/25/2017] [Indexed: 11/24/2022]
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22
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Liang L, Liu M, Martin C, Elefteriades JA, Sun W. A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm. Biomech Model Mechanobiol 2017; 16:1519-1533. [PMID: 28386685 PMCID: PMC5630492 DOI: 10.1007/s10237-017-0903-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 03/27/2017] [Indexed: 02/07/2023]
Abstract
Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g., diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA-predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA-predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross-validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.
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Affiliation(s)
- Liang Liang
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206 387 Technology Circle, Atlanta, GA, 30313-2412, USA
| | - Minliang Liu
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206 387 Technology Circle, Atlanta, GA, 30313-2412, USA
| | - Caitlin Martin
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206 387 Technology Circle, Atlanta, GA, 30313-2412, USA
| | - John A Elefteriades
- Aortic Institute of Yale-New Haven Hospital, Yale University, New Haven, CT, USA
| | - Wei Sun
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206 387 Technology Circle, Atlanta, GA, 30313-2412, USA.
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23
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Growth Description for Vessel Wall Adaptation: A Thick-Walled Mixture Model of Abdominal Aortic Aneurysm Evolution. MATERIALS 2017; 10:ma10090994. [PMID: 28841196 PMCID: PMC5615649 DOI: 10.3390/ma10090994] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 08/21/2017] [Accepted: 08/23/2017] [Indexed: 12/20/2022]
Abstract
(1) Background: Vascular tissue seems to adapt towards stable homeostatic mechanical conditions, however, failure of reaching homeostasis may result in pathologies. Current vascular tissue adaptation models use many ad hoc assumptions, the implications of which are far from being fully understood; (2) Methods: The present study investigates the plausibility of different growth kinematics in modeling Abdominal Aortic Aneurysm (AAA) evolution in time. A structurally motivated constitutive description for the vessel wall is coupled to multi-constituent tissue growth descriptions; Constituent deposition preserved either the constituent’s density or its volume, and Isotropic Volume Growth (IVG), in-Plane Volume Growth (PVG), in-Thickness Volume Growth (TVG) and No Volume Growth (NVG) describe the kinematics of the growing vessel wall. The sensitivity of key modeling parameters is explored, and predictions are assessed for their plausibility; (3) Results: AAA development based on TVG and NVG kinematics provided not only quantitatively, but also qualitatively different results compared to IVG and PVG kinematics. Specifically, for IVG and PVG kinematics, increasing collagen mass production accelerated AAA expansion which seems counterintuitive. In addition, TVG and NVG kinematics showed less sensitivity to the initial constituent volume fractions, than predictions based on IVG and PVG; (4) Conclusions: The choice of tissue growth kinematics is of crucial importance when modeling AAA growth. Much more interdisciplinary experimental work is required to develop and validate vascular tissue adaption models, before such models can be of any practical use.
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Peirlinck M, Debusschere N, Iannaccone F, Siersema PD, Verhegghe B, Segers P, De Beule M. An in silico biomechanical analysis of the stent–esophagus interaction. Biomech Model Mechanobiol 2017; 17:111-131. [DOI: 10.1007/s10237-017-0948-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 08/03/2017] [Indexed: 12/15/2022]
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