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UnknownComparison of fast acquisition strategies in whole-heart four-dimensional flow cardiac MR: Two-center, 1.5 Tesla, phantom and in vivo validation study.

Purpose

To validate three widely-used acceleration methods in four-dimensional (4D) flow cardiac MR; segmented 4D-spoiled-gradient-echo (4D-SPGR), 4D-echo-planar-imaging (4D-EPI), and 4D-k-t Broad-use Linear Acquisition Speed-up Technique (4D-k-t BLAST).

Materials and methods

Accelera...
S-EPMC5801550 | biostudies-literature
UnknownMean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model.

Background

Pulmonary hypertension (PH) is a heterogeneous condition and regardless of etiology impacts negatively on survival. Diagnosis of PH is based on hemodynamic parameters measured invasively at right heart catheterization (RHC); however, a non-invasive alternative would be clinically ...
S-EPMC11782807 | biostudies-literature
UnknownSignal intensity and volume of carotid intraplaque hemorrhage on magnetic resonance imaging and the risk of ipsilateral cerebrovascular events: The Plaque At RISK (PARISK) study.

Background

The Plaque At RISK (PARISK) study demonstrated that patients with a carotid plaque with intraplaque hemorrhage (IPH) have an increased risk of recurrent ipsilateral ischemic cerebrovascular events. It was previously reported that symptomatic carotid plaques with IPH showed higher ...
S-EPMC11282977 | biostudies-literature
UnknownUtility of ripple mapping for identification of slow conduction channels during ventricular tachycardia ablation in the setting of arrhythmogenic right ventricular cardiomyopathy.
BACKGROUND:Ripple mapping displays every deflection of a bipolar electrogram and enables the visualization of conduction channels (RMCC) within postinfarction ventricular scar to guide ventricular tachycardia (VT) ablation. The utility of RMCC identification for facilitation of VT ablation in the se...
S-EPMC6422721 | biostudies-literature
UnknownMachine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension.

Aims

Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning.

Methods and results

Seven hundred and twenty-thr...
S-EPMC9708011 | biostudies-literature
UnknownCardiovascular magnetic resonance imaging markers of ageing: a multi-centre, cross-sectional cohort study.

Aims

Cardiac ageing involves a series of anatomical and physiological changes contributing to a decline in overall performance. Cardiac magnetic resonance (CMR) provides comprehensive structural and functional assessment for detecting age-related cardiovascular remodelling. We aimed to devel...
S-EPMC12045662 | biostudies-literature
UnknownRisk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

Background

The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, an...
S-EPMC8236316 | biostudies-literature
UnknownA MT-TL1 variant identified by whole exome sequencing in an individual with intellectual disability, epilepsy, and spastic tetraparesis.
The genetic etiology of intellectual disability remains elusive in almost half of all affected individuals. Within the Solve-RD consortium, systematic re-analysis of whole exome sequencing (WES) data from unresolved cases with (syndromic) intellectual disability (n = 1,472 probands) was performed. T...
S-EPMC8440635 | biostudies-literature
UnknownPredictors for extubation failure in COVID-19 patients using a machine learning approach.

Introduction

Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.

Methods

We used highly granular data from 3464 adult critically ill COVID ...
S-EPMC8711075 | biostudies-literature
UnknownLarge-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse.
Not available
S-EPMC7887418 | biostudies-literature
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