Quantification of Myocardial Blood Flow in Absolute Terms Using (82)Rb PET Imaging: The RUBY-10 Study.

OBJECTIVES: The purpose of this study was to compare myocardial blood flow (MBF) and myocardial flow reserve (MFR) estimates from rubidium-82 positron emission tomography ((82)Rb PET) data using 10 software packages (SPs) based on 8 tracer kinetic models. BACKGROUND: It is unknown how MBF and MFR values from existing SPs agree for (82)Rb PET. METHODS: Rest and stress (82)Rb PET scans of 48 patients with suspected or known coronary artery disease were analyzed in 10 centers. Each center used 1 of 10 SPs to analyze global and regional MBF using the different kinetic models implemented. Values were considered to agree if they simultaneously had an intraclass correlation coefficient >0.75 and a difference <20% of the median across all programs. RESULTS: The most common model evaluated was the Ottawa Heart Institute 1-tissue compartment model (OHI-1-TCM). MBF values from 7 of 8 SPs implementing this model agreed best. Values from 2 other models (alternative 1-TCM and Axially distributed) also agreed well, with occasional differences. The MBF results from other models (e.g., 2-TCM and retention) were less in agreement with values from OHI-1-TCM. CONCLUSIONS: SPs using the most common kinetic model-OHI-1-TCM-provided consistent results in measuring global and regional MBF values, suggesting that they may be used interchangeably to process data acquired with a common imaging protocol

Artificial intelligence in cardiovascular CT: Current status and future implications

Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool

Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease

Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratification. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease

Myocardial infarction associates with a distinct pericoronary adipose tissue radiomic phenotype: a prospective case-control study

Abstract not available.Andrew Lin, Márton Kolossváry, Jeremy Yuvaraj, Sebastien Cadet, Priscilla A. McElhinney, Cathy Jiang, Nitesh Nerlekar, Stephen J. Nicholls, Piotr J. Slomka, Pál Maurovich-Horvat, Dennis T.L. Wong, Damini De

Registration of Free-Hand Ultrasound and MRI of Carotid Arteries through Combination of Point-Based and Intensity-Based Algorithms

We propose a methodology to register medical images of carotid arteries from tracked freehand sweep B-Mode ultrasound (US) and magnetic resonance imaging (MRI) acquisitions. Successful registration of US and MR images will allow a multimodal analysis of atherosclerotic plaque in the carotid artery. The main challenge is the difference in the positions of the patient's neck during the examinations. While in MRI the patient's neck remains in a natural position, in US the neck is slightly bent and rotated. Moreover, the image characteristics of US and MRI around the carotid artery are very different. Our technique uses the estimated centerlines of the common, internal and external carotid arteries in each modality as landmarks for registration. For US, we used an algorithm based on a rough lumen segmentation obtained by robust ellipse fitting to estimate the lumen centerline. In MRI, we extract the centerline using a minimum cost path approach in which the cost is defined by medialness and an intensity based similarity term. The two centerlines are aligned by an iterative closest point (ICP) algorithm, using rigid and thin-plate spline transformation models. The resulting point correspondences are used as a soft constraint in a subsequent intensity-based registration, optimizing a weighted sum of mutual information between the US and MRI and the Euclidean distance between corresponding points. Rigid and B-spline transformation models were used in this stage. Experiments were performed on datasets from five healthy volunteers. We compared different registration approaches, in order to evaluate the necessity of each step, and to establish the optimum algorithm configuration. For the validation, we used the Dice similarity index to measure the overlap between lumen segmentations in US and MRI.</p

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.Cardiolog