Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.

Background: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 HO perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings.

Methods: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables.

Plaque volume, composition, and fraction versus ischemia and outcomes in patients with coronary artery disease.

Background: The various plaque components have been associated with ischemia and outcomes in patients with coronary artery disease (CAD). The main goal of this analysis was to test the hypothesis that, at patient level, the fraction of non-calcified plaque volume (PV) of total PV is associated with ischemia and outcomes in patients with CAD. This ratio could be a simple and clinically useful parameter, if predicting outcomes.

Incorporating coronary artery calcium scoring in the prediction of obstructive coronary artery disease with myocardial ischemia: a study with sequential use of coronary computed tomography angiography and positron emission tomography imaging.

Background: Additional strategies are needed to refine the referral for diagnostic testing of symptomatic patients with suspected coronary artery disease (CAD). We aimed to compare various models to predict hemodynamically obstructive CAD.

Methods And Results: Symptomatic patients with suspected CAD who underwent coronary artery calcium scoring (CACS) and sequential coronary computed tomography angiography (CCTA) and [O]HO positron emission tomography (PET) myocardial perfusion imaging were analyzed.

Coronary computed tomography angiography-based endothelial wall shear stress in normal coronary arteries.

Endothelial wall shear stress (ESS) is a biomechanical force which plays a role in the formation and evolution of atherosclerotic lesions. The purpose of this study is to evaluate coronary computed tomography angiography (CCTA)-based ESS in coronary arteries without atherosclerosis, and to assess factors affecting ESS values. CCTA images from patients with suspected coronary artery disease were analyzed to identify coronary arteries without atherosclerosis.

Importance of plaque volume and composition for the prediction of myocardial ischaemia using sequential coronary computed tomography angiography/positron emission tomography imaging.

Aims: Coronary atherosclerosis with a large necrotic core has been postulated to reduce the vasodilatory capacity of vascular tissue. In the present analysis, we explored whether total plaque volume and necrotic core volume on coronary computed tomography angiography (CCTA) are independently associated with myocardial ischaemia on positron emission tomography (PET).

Methods And Results: From a registry of symptomatic patients with suspected coronary artery disease and clinically indicated CCTA with sequential [15O]H2O PET myocardial perfusion imaging, we quantitatively measured diameter stenosis, total and compositional plaque volumes on CCTA.

Detailed behaviour of endothelial wall shear stress across coronary lesions from non-invasive imaging with coronary computed tomography angiography.

Aims: Evolving evidence suggests that endothelial wall shear stress (ESS) plays a crucial role in the rupture and progression of coronary plaques by triggering biological signalling pathways. We aimed to investigate the patterns of ESS across coronary lesions from non-invasive imaging with coronary computed tomography angiography (CCTA), and to define plaque-associated ESS values in patients with coronary artery disease (CAD).

Methods And Results: Symptomatic patients with CAD who underwent a clinically indicated CCTA scan were identified.

Classification of ischemia from myocardial polar maps in O-HO cardiac perfusion imaging using a convolutional neural network.

We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from O-HO stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation.

Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification.

Background: Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating.

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy.

Probing quantum state space: does one have to learn everything to learn something?

Determining the state of a quantum system is a consuming procedure. For this reason, whenever one is interested only in some particular property of a state, it would be desirable to design a measurement set-up that reveals this property with as little effort as possible. Here, we investigate whether, in order to successfully complete a given task of this kind, one needs an informationally complete measurement, or if something less demanding would suffice.

Verifying the Quantumness of Bipartite Correlations.

Entanglement is at the heart of most quantum information tasks, and therefore considerable effort has been made to find methods of deciding the entanglement content of a given bipartite quantum state. Here, we prove a fundamental limitation to deciding if an unknown state is entangled or not: we show that any quantum measurement which can answer this question for an arbitrary state necessarily gives enough information to identify the state completely. We also extend our treatment to other classes of correlated states by considering the problem of deciding if a state has negative partial transpose, is discordant, or is fully classically correlated.