AIMC Topic: Artifacts

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Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Sensors (Basel, Switzerland)
Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified ...

An anomaly detection approach to identify chronic brain infarcts on MRI.

Scientific reports
The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by le...

Volume Reduction Techniques for the Classification of Independent Components of rs-fMRI Data: a Study with Convolutional Neural Networks.

Neuroinformatics
In the last decade, neurosciences have had an increasing interest in resting state functional magnetic resonance imaging (rs-fMRI) as a result of its advantages, such as high spatial resolution, compared to other brain exploration techniques. To impr...

Deep learning-enhanced T mapping with spatial-temporal and physical constraint.

Magnetic resonance in medicine
PURPOSE: To propose a reconstruction framework to generate accurate T maps for a fast MR T mapping sequence.

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Radiology
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose T...

Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach.

Sensors (Basel, Switzerland)
Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. T...

Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels.

Journal of neuroscience methods
BACKGROUND: In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpola...

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Journal of the American Heart Association
Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimiz...

SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning.

IEEE transactions on medical imaging
High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more c...

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Dedicated cardiac SPECT scanners with cadmium-zinc-telluride cameras have shown capabilities for shortened scan times or reduced radiation doses, as well as improved image quality. Since most dedicated scanners do not have integrated CT, image quanti...