Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study.

Background And Aims: Coronary thin-cap fibroatheromas (TCFA) are associated with adverse outcome, but identification of TCFA requires expertise and is highly time-demanding. This study evaluated the utility of artificial intelligence (AI) for TCFA identification in relation to clinical outcome.

Methods: The PECTUS-AI study is a secondary analysis from the prospective observational PECTUS-obs study, in which 438 patients with myocardial infarction underwent optical coherence tomography (OCT) of all fractional flow reserve-negative non-culprit lesions (i.

Learning hemodynamic scalar fields on coronary artery meshes: A benchmark of geometric deep learning models.

Coronary artery disease involves the narrowing of coronary vessels due to atherosclerosis and is currently the leading cause of death worldwide. The gold standard for its diagnosis is the fractional flow reserve (FFR) examination, which measures the trans-stenotic pressure ratio during maximal vasodilation. However, the invasiveness and cost of this procedure have prompted the development of computer-based virtual FFR (vFFR) computation, which simulates coronary flow using computational fluid dynamics (CFD) techniques.

Comprehensive full-vessel segmentation and volumetric plaque quantification for intracoronary optical coherence tomography using deep learning.

Aims: Intracoronary optical coherence tomography (OCT) provides detailed information on coronary lesions, but interpretation of OCT images is time-consuming and subject to interobserver variability. The aim of this study was to develop and validate a deep learning-based multiclass semantic segmentation algorithm for OCT (OCT-AID).

Methods And Results: A reference standard was obtained through manual multiclass annotation (guidewire artefact, lumen, side branch, intima, media, lipid plaque, calcified plaque, thrombus, plaque rupture, and background) of OCT images from a representative subset of pullbacks from the PECTUS-obs study.

World of Forms: Deformable geometric templates for one-shot surface meshing in coronary CT angiography.

Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. This may lead to topological inconsistencies and suboptimal performance in low-data regimes. To address these challenges, we propose a data-efficient deep learning method for direct 3D anatomical object surface meshing using geometric priors.

Deployment of a digital twin using the coupled momentum method for fluid-structure interaction: A case study for aortic aneurysm.

Introduction: Dacron graft replacement is the standard therapy for ascending aorta aneurysm, involving the insertion of a prosthesis with lower compliance than native tissue, which can alter downstream hemodynamics and lead to adverse remodeling. Digital human twins (DHT), based on in-silico models, have the potential to predict biomarkers of adverse outcome and aid in designing optimal treatments tailored to the individual patient.

Objective: We propose a pipeline for deploying a digital human twin of the thoracic aorta to explore alternative solutions to traditional Dacron grafting, utilizing more compliant prostheses for reconstructing the ascending aorta.

Ascending aorta over-angulation is a risk factor for acute type A aortic dissection: evidence from advanced finite element simulations.

Objectives: To assess whether ascending aorta over-angulation, a morphological feature recently found to be associated with acute type A aortic dissection, precedes dissection and how it affects wall stress distribution.

Methods: A baseline finite element model, previously created by a neural network tool from end-diastolic computed tomography angiography measurements in 124 healthy subjects, was modified to simulate the over-angulation accompanying aortic elongation, obtaining paradigmatic models with different ascending angulations (ascending-arch angle 145°-110°). The models were discretized and embedded in a deformable continuum representing surrounding tissues, aortic wall anisotropy and nonlinearity were accounted for, pre-tensioning at diastolic pressures was applied and peak systolic stresses were computed.

Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.

Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.

An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions.

Background And Objective: Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data.

Left ventricle diastolic vortex ring characterization in ischemic cardiomyopathy: insight into atrio-ventricular interplay.

Diastolic vortex ring (VR) plays a key role in the blood-pumping function exerted by the left ventricle (LV), with altered VR structures being associated with LV dysfunction. Herein, we sought to characterize the VR diastolic alterations in ischemic cardiomyopathy (ICM) patients with systo-diastolic LV dysfunction, as compared to healthy controls, in order to provide a more comprehensive understanding of LV diastolic function. 4D Flow MRI data were acquired in ICM patients (n = 15) and healthy controls (n = 15).

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI.

Background And Objective: 4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta.

Methods: Efficient training of SIRENs in 4D was achieved by sampling voxel coordinates and enforcing the no-slip condition at the vessel wall.

A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization.

Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis.

A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning.

Accurate planning of transcatheter aortic valve implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly performed through 3D computed tomography (CT) image analysis. Currently, there is no standard automated solution for this process. Two convolutional neural networks with 3D U-Net architectures (model 1 and model 2) were trained on 310 CT scans for AR analysis.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta.

Background And Objective: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs.

Quantification of paravalvular leaks associated with TAVI implants using 4D MRI in an aortic root phantom made possible by the use of 3D printing.

Introduction: Transcatheter aortic valve implantation (TAVI) has become an alternative to surgical replacement of the aortic valve elderly patients. However, TAVI patients may suffer from paravalvular leaks (PVL). Detecting and grading is usually done by echocardiography, but is limited by resolution, 2D visualization and operator dependency.

The Role of Multiple Re-Entry Tears in Type B Aortic Dissection Progression: A Longitudinal Study Using a Controlled Swine Model.

Purpose: False lumen (FL) expansion often occurs in type B aortic dissection (TBAD) and has been associated with the presence of re-entry tears. This longitudinal study aims to elucidate the role of re-entry tears in the progression of TBAD using a controlled swine model, by assessing aortic hemodynamics through combined imaging and computational modeling.

Materials And Methods: A TBAD swine model with a primary entry tear at 7 cm distal to the left subclavian artery was created in a previous study.

A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography.

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning.

Comparison of Four-Dimensional Magnetic Resonance Imaging Analysis of Left Ventricular Fluid Dynamics and Energetics in Ischemic and Restrictive Cardiomyopathies.

Background: Time-resolved three-directional velocity-encoded (4D flow) magnetic resonance imaging (MRI) enables the quantification of left ventricular (LV) intracavitary fluid dynamics and energetics, providing mechanistic insight into LV dysfunctions. Before becoming a support to diagnosis and patient stratification, this analysis should prove capable of discriminating between clearly different LV derangements.

Purpose: To investigate the potential of 4D flow in identifying fluid dynamic and energetics derangements in ischemic and restrictive LV cardiomyopathies.

Evaluation and verification of patient-specific modelling of type B aortic dissection.

Quantitative assessment of the complex hemodynamic environment in type B aortic dissection (TBAD) through computational fluid dynamics (CFD) simulations can provide detailed insights into the disease and its progression. As imaging and computational technologies have advanced, methodologies have been developed to increase the accuracy and physiological relevance of CFD simulations. This study presents a patient-specific workflow to simulate blood flow in TBAD, utilising the maximum amount of in vivo data available in the form of CT images, 4D-flow MRI and invasive Doppler-wire pressure measurements, to implement the recommended current best practice methodologies in terms of patient-specific geometry and boundary conditions.

Qualitative and Quantitative Assessments of Blood Flow on Tears in Type B Aortic Dissection With Different Morphologies.

The interactions between aortic morphology and hemodynamics play a key role in determining type B aortic dissection (TBAD) progression and remodeling. The study aimed to provide qualitative and quantitative hemodynamic assessment in four different TBAD morphologies based on 4D flow MRI analysis. Four patients with different TBAD morphologies underwent CT and 4D flow MRI scans.

Aortic hemodynamics assessment prior and after valve sparing reconstruction: A patient-specific 4D flow-based FSI model.

Introduction: Valve-sparing root replacement (VSRR) of the ascending aorta is a life-saving procedure for the treatment of aortic aneurysms, but patients remain at risk for post-operative events involving the downstream native aorta, the mechanism for which is uncertain. It is possible that proximal graft replacement of the ascending aorta induces hemodynamics alterations in the descending aorta, which could trigger adverse events. Herein, we present a fluid-structure interaction (FSI) protocol, based on patient-specific geometry and boundary conditions, to assess impact of proximal aortic grafts on downstream aortic hemodynamics and distensibility.

The influence of inlet velocity profile on predicted flow in type B aortic dissection.

In order for computational fluid dynamics to provide quantitative parameters to aid in the clinical assessment of type B aortic dissection, the results must accurately mimic the hemodynamic environment within the aorta. The choice of inlet velocity profile (IVP) therefore is crucial; however, idealised profiles are often adopted, and the effect of IVP on hemodynamics in a dissected aorta is unclear. This study examined two scenarios with respect to the influence of IVP-using (a) patient-specific data in the form of a three-directional (3D), through-plane (TP) or flat IVP; and (b) non-patient-specific flow waveform.

Evaluation of 4D flow MRI-based non-invasive pressure assessment in aortic coarctations.

Severity of aortic coarctation (CoA) is currently assessed by estimating trans-coarctation pressure drops through cardiac catheterization or echocardiography. In principle, more detailed information could be obtained non-invasively based on space- and time-resolved magnetic resonance imaging (4D flow) data. Yet the limitations of this imaging technique require testing the accuracy of 4D flow-derived hemodynamic quantities against other methodologies.

4-D Flow MRI-Based Computational Analysis of Blood Flow in Patient-Specific Aortic Dissection.

Objective: Computational hemodynamic studies of aortic dissections usually combine patient-specific geometries with idealized or generic boundary conditions. In this study, we present a comprehensive methodology for the simulation of hemodynamics in type B aortic dissection (TBAD), based on fully patient-specific boundary conditions.

Methods: Pre-operative four-dimensional (4-D) flow magnetic resonance imaging (MRI) and Doppler-wire pressure measurements (pre- and post-operative) were acquired from a TBAD patient.