Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

Aims: Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicentre international study compared an automated deep learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS).

Long-term prognostic implications of hemodynamic and plaque assessment using coronary CT angiography.

Background And Aims: Hemodynamic and plaque characteristics can be analyzed using coronary CT angiography (CTA). We aimed to explore long-term prognostic implications of hemodynamic and plaque characteristics using coronary CT angiography (CTA).

Methods: Invasive fractional flow reserve (FFR) and CTA-derived FFR (FFR) were undertaken for 136 lesions in 78 vessels and followed-up to 10 years until December 2020.

Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks.

Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible topologies. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts of training data, and have not widely been benchmarked against pixel-wise segmentation models. We propose the Atlas Image-and-Spatial Transformer Network (Atlas-ISTN), a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process.

Myocardial Perfusion Simulation for Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model.

Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data.

Precise measurement of coronary stenosis diameter with CCTA using CT number calibration.

Purpose: Coronary x-ray computed tomography angiography (CCTA) continues to develop as a noninvasive method for the assessment of coronary vessel geometry and the identification of physiologically significant lesions. The uncertainty of quantitative lesion diameter measurement due to limited spatial resolution and vessel motion reduces the accuracy of CCTA diagnoses. In this paper, we introduce a new technique called computed tomography (CT)-number-Calibrated Diameter to improve the accuracy of the vessel and stenosis diameter measurements with CCTA.

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors.

In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation.

Attention gated networks: Learning to leverage salient regions in medical images.

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs).

Generation of Patient-Specific Cardiac Vascular Networks: A Hybrid Image-Based and Synthetic Geometric Model.

Objective: In this paper, we propose an algorithm for the generation of a patient-specific cardiac vascular network starting from segmented epicardial vessels down to the arterioles.

Method: We extend a tree generation method based on satisfaction of functional principles, named constrained constructive optimization, to account for multiple, competing vascular trees. The algorithm simulates angiogenesis under vascular volume minimization with flow-related and geometrical constraints adapting the simultaneous tree growths to patient priors.

Lumen boundaries extracted from coronary computed tomography angiography on computed fractional flow reserve (FFRCT): validation with optical coherence tomography.

Aims: The aim of this study was to evaluate the accuracy of minimum lumen area (MLA) by coronary computed tomography angiography (cCTA) and its impact on fractional flow reserve (FFRCT).

Methods And Results: Fifty-seven patients (118 lesions, 72 vessels) who underwent cCTA and optical coherence tomography (OCT) were enrolled. OCT and cCTA were co-registered and MLAs were measured with both modalities.

3D reconstruction techniques of human coronary bifurcations for shear stress computations.

Background: Heterogeneity in plaque composition in human coronary artery bifurcations is associated with blood flow induced shear stress. Shear stress is generally determined by combing 3D lumen data and computational fluid dynamics (CFD). We investigated two new procedures to generate 3D lumen reconstructions of coronary artery bifurcations for shear stress computations.

Automatic segmentation, detection and quantification of coronary artery stenoses on CTA.

Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step.

Registration of 3D+t coronary CTA and monoplane 2D+t X-ray angiography.

A method for registering preoperative 3D+t coronary CTA with intraoperative monoplane 2D+t X-ray angiography images is proposed to improve image guidance during minimally invasive coronary interventions. The method uses a patient-specific dynamic coronary model, which is derived from the CTA scan by centerline extraction and motion estimation. The dynamic coronary model is registered with the 2D+t X-ray sequence, considering multiple X-ray time points concurrently, while taking breathing induced motion into account.

Vessel specific coronary artery calcium scoring: an automatic system.

Rationale And Objectives: The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images.

Materials And Methods: Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.

Semiautomatic carotid lumen segmentation for quantification of lumen geometry in multispectral MRI.

Quantitative information about the geometry of the carotid artery bifurcation is relevant for investigating the onset and progression of atherosclerotic disease. This paper proposes an automatic approach for quantifying the carotid bifurcation angle, carotid area ratio, carotid bulb size and the vessel tortuosity from multispectral MRI. First, the internal and external carotid centerlines are determined by finding a minimum cost path between user-defined seed points where the local costs are based on medialness and intensity.

Regression-based cardiac motion prediction from single-phase CTA.

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions.

Automated bone removal in CT angiography: comparison of methods based on single energy and dual energy scans.

Purpose: To evaluate dual energy based methods for bone removal in computed tomography angiography (CTA) images and compare these with single energy based methods that use an additional, nonenhanced, CT scan.

Methods: Four different bone removal methods were applied to CT scans of an anthropomorphic thorax phantom, acquired with a second generation dual source CT scanner. The methods differed by the way information on the presence of bone was obtained (either by using an additional, nonenhanced scan or by scanning with two tube voltages at the same time) and by the way the bone was removed from the CTA images (either by masking or subtracting the bone).

Comparison of shape regression methods under landmark position uncertainty.

Despite the growing interest in regression based shape estimation, no study has yet systematically compared different regression methods for shape estimation. We aimed to fill this gap by comparing linear regression methods with a special focus on shapes with landmark position uncertainties. We investigate two scenarios: In the first, the uncertainty of the landmark positions was similar in the training and test dataset, whereas in the second the uncertainty of the training and test data were different.

Detection and quantification of coronary atherosclerotic plaque by 64-slice multidetector CT: a systematic head-to-head comparison with intravascular ultrasound.

Objective: We evaluated the ability of 64-slice multidetector computed tomography (MDCT)-derived plaque parameters to detect and quantify coronary atherosclerosis, using intravascular ultrasound (IVUS) as the reference standard.

Methods: In 32 patients, IVUS and 64-MDCT was performed. The MDCT and IVUS datasets of 44 coronary arteries were co-registered using a newly developed fusion technique and quantitative parameters were derived from both imaging modalities.

Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA.

This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve.

Statistical analysis of minimum cost path based structural brain connectivity.

Diffusion MRI can be used to study the structural connectivity within the brain. Brain connectivity is often represented by a binary network whose topology can be studied using graph theory. We present a framework for the construction of weighted structural brain networks, containing information about connectivity, which can be effectively analyzed using statistical methods.

A semi-automatic method for segmentation of the carotid bifurcation and bifurcation angle quantification on black blood MRA.

Quantitative information about the geometry of the carotid artery bifurcation may help in predicting the development of atherosclerosis. A geodesic active contours based segmentation method combining both gradient and intensity information was developed for semi-automatic, accurate and robust quantification of the carotid bifurcation angle in Black Blood MRA data. The segmentation method was evaluated by comparing its accuracy to inter and intra observer variability on a large dataset that has been acquired as part of a longitudinal population study which investigates the natural progression of carotid atherosclerosis.

Statistical analysis of structural brain connectivity.

We present a framework for statistical analysis in large cohorts of structural brain connectivity, derived from diffusion weighted MRI. A brain network is defined between subcortical gray matter structures and a cortical parcellation obtained with FreeSurfer. Connectivity is established through minimum cost paths with an anisotropic local cost function and is quantified per connection.

Conditional shape models for cardiac motion estimation.

We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic alignment of pre-operative CTA data with intra-operative X-ray imaging.