Production exploitation of cardiac image analysis tools is hampered by the lack of proper IT infrastructure in health institutions, the non trivial integration of heterogeneous codes in coherent analysis procedures, and the need to achieve complete automation of these methods. HealthGrids are promising technologies to address these difficulties. This paper details how they can be complemented by high level problem solving environments such as workflow managers to improve the performance of applications both in terms of execution time and robustness of results. Two of the most important important cardiac image analysis tasks are considered, namely myocardium segmentation and motion estimation in a 4D sequence. Results are shown on the corresponding pipelines, using two different execution environments on the EGEE grid production infrastructure.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Feasibility of very low iodine dose aortoiliac CT angiography using dual-source photon-counting detector CT.
Eur J Radiol
January 2025
Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, USA. Electronic address:
Purpose: To evaluate the feasibility of aortoiliac CT-Angiography (CTA) using dual-source photon-counting detector (PCD)-CT with minimal iodine dose.
Methods: This IRB-approved, single-center prospective study enrolled patients with indications for aortoiliac CTA from December 2022 to March 2023. All scans were performed using a first-generation dual-source PCD-CT.
Characterizing Cardiac Adipose Tissue in Post-AMI Patients via CT Imaging: A Comparative Cross-sectional Study.
Eur Heart J Cardiovasc Imaging
January 2025
National Heart Center Singapore, Singapore, Singapore.
Aims: To identify differences in CT-derived perivascular (PVAT) and epicardial adipose tissue (EAT) characteristics that may indicate inflammatory status differences between post-treatment acute myocardial infarction (AMI) and stable coronary artery disease (CAD) patients.
Methods And Results: A cohort of 205 post-AMI patients (age 59.8±9.
Central venous pressure as a method of optimising atrio-ventricular delay after cardiac surgery.
PLoS One
January 2025
National Heart and Lung Institute, Imperial College London, London, United Kingdom.
Introduction: Haemodynamic atrioventricular delay (AVD) optimisation has primarily focussed on signals that are not easy to acquire from a pacing system itself, such as invasive left ventricular catheterisation or arterial blood pressure (ABP). In this study, standard clinical central venous pressure (CVP) signals are tested as a potential alternative.
Methods: Sixteen patients with a temporary pacemaker after cardiac surgery were studied.
H-Dots: Renal Clearable Zwitterionic Nanocarriers for Disease Diagnosis and Therapy.
Langmuir
January 2025
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, United States.
Nanocarriers have shown significant promise in the diagnosis and treatment of various diseases, utilizing a wide range of biocompatible materials such as metals, inorganic substances, and organic components. Despite diverse design strategies, key physicochemical properties, including hydrodynamic diameter, shape, surface charge, and hydrophilicity/lipophilicity, are crucial for optimizing biodistribution, pharmacokinetics, and therapeutic efficacy. However, these properties are often influenced by drug payload, presenting an ongoing challenge in developing versatile platform technologies for theranostics.
View Article and Find Full Text PDFArtificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.
PLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!