An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging

Narayanan, Bharath; Olender, Max L.; Marlevi, David; Edelman, Elazer R.; Nezami, Farhad Rikhtegar

doi:10.1038/s41598-021-01874-3

Cited by 19 publications

(25 citation statements)

References 44 publications

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“…Further advancements could also assist in differentiating between healthy re-endothelization or fibrin drug eluting stent coverage, improving the ability to stratify risk of late stent thrombosis [ 222 ]. Combining this ability to accurately segment pathological borders and extract molecular information, reminiscent of an advanced virtual histology IVUS/OCT [ 223 , 224 ], presents opportunities to reverse engineer tissue constitutive models and adapt structural simulations to patient-specific conditions, currently a major limitation in the field of biomechanics [ 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 ]. However, there is still a need for further evidence to determine which multi-modal imaging technique can provide the strongest incremental benefits and risk stratification to improve both clinical outcomes and simulation capability.…”

Section: Discussionmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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Section: Discussionmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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“…Inverse FEA methods with more realistic conditions are needed. Recently, to overcome the previous limitation of homogenized or simplified material representations, an inverse FEA approach was developed to derive non-linear material properties of heterogeneous coronary plaque components using OCT imaging data acquired at differing pressures by incorporating interfaces between various intra-plaque components into the objective function ( Narayanan et al, 2021 ). The importance of including multi-material plaque components has also been demonstrated by the greatly varied lesion mechanical responses ( Kadry et al, 2021 ).…”

Section: Pipeline Of Image-based Computational Biomechanical Simulationsmentioning

confidence: 99%

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Northrup

et al. 2022

Front. Bioeng. Biotechnol.

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Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

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“…Further advancements could also assist in differentiating between healthy re-endothelisation or fibrin drug eluting stent coverage, improving the ability to stratify risk of late stent thrombosis [208]. Combining this ability to accurately segment pathological borders and extract molecular information, reminiscent of an advanced virtual histology IVUS/OCT [209,210], presents opportunities to reverse engineer tissue constitutive models and adapt structural simulations to patient-specific conditions, currently a major limitation in the field of biomechanics [211][212][213][214][215][216][217][218][219][220]. However, there is still a need for further evidence to determine which multi-modal imaging technique can provide the strongest incremental benefits and risk stratification to improve both clinical outcomes and simulation capability.…”

Section: Figure 14mentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Carpenter¹,

Ghayesh²,

Zander³

et al. 2022

Preprint

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualisation of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarises advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the three-dimensional reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; plaque characteristics and subtypes; artery layers; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

show abstract

An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging

Cited by 19 publications

References 44 publications

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

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