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Heart

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Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure.

Journal of medical imaging and radiation sciences
BACKGROUND: Our previous work with iodine meta-iodobenzylguanidine (I-mIBG) radionuclide imaging among patients with cardiomyopathy reported limitations associated with the prognostic power of global parameters derived from planar imaging [1]. Employ...

Memory-efficient 2.5D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans.

International journal of computer assisted radiology and surgery
PURPOSE : Despite its potential for improvements through supervision, deep learning-based registration approaches are difficult to train for large deformations in 3D scans due to excessive memory requirements. METHODS : We propose a new 2.5D convolut...

Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network.

IEEE journal of biomedical and health informatics
Recently, portable electrocardiogram (ECG) hardware devices have been developed using limb-lead measurements. However, portable ECGs provide insufficient ECG information because of limitations in the number of leads and measurement positions. Therefo...

Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data.

IEEE transactions on medical imaging
In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image...

Cine MRI analysis by deep learning of optical flow: Adding the temporal dimension.

Computers in biology and medicine
Accurate segmentation of the left ventricle (LV) from cine magnetic resonance imaging (MRI) is an important step in the reliable assessment of cardiac function in cardiovascular disease patients. Several deep learning convolutional neural network (CN...

Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking.

Sensors (Basel, Switzerland)
Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of releva...

Parallel imaging and convolutional neural network combined fast MR image reconstruction: Applications in low-latency accelerated real-time imaging.

Medical physics
PURPOSE: To develop and evaluate a parallel imaging and convolutional neural network combined image reconstruction framework for low-latency and high-quality accelerated real-time MR imaging.

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation.

Computers in biology and medicine
BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNe...

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning.

Medical image analysis
Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual id...

A Remotely Controlled Transformable Soft Robot Based on Engineered Cardiac Tissue Construct.

Small (Weinheim an der Bergstrasse, Germany)
Many living organisms undergo conspicuous or abrupt changes in body structure, which is often accompanied by a behavioral change. Inspired by the natural metamorphosis, robotic systems can be designed as reconfigurable to be multifunctional. Here, a ...