AIMC Topic: Artifacts

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Deep Convolutional Neural Networks for Feature-Less Automatic Classification of Independent Components in Multi-Channel Electrophysiological Brain Recordings.

IEEE transactions on bio-medical engineering
OBJECTIVE: Interpretation of the electroencephalographic (EEG) and magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts share frequencies with brain activity, filtering is insufficient. Blind source separation, m...

Learning-based CBCT correction using alternating random forest based on auto-context model.

Medical physics
PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and p...

Improvement of image quality at CT and MRI using deep learning.

Japanese journal of radiology
Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for impr...

Metal artifact reduction on cervical CT images by deep residual learning.

Biomedical engineering online
BACKGROUND: Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (C...

Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

Human brain mapping
Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated iden...

Deep learning in medical imaging and radiation therapy.

Medical physics
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these ...

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.

Medical image analysis
Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions mor...

CT sinogram-consistency learning for metal-induced beam hardening correction.

Medical physics
PURPOSE: This paper proposes a sinogram-consistency learning method to deal with beam hardening-related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sin...

A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.

Physiological measurement
OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our...

Iterative quality enhancement via residual-artifact learning networks for low-dose CT.

Physics in medicine and biology
Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy ...