AIMC Topic: Artifacts
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PSAR-SR: Patches separation and artifacts removal for improving super-resolution networks.
Neural networks : the official journal of the International Neural Network Society
Jul 24, 2024
The success of the ClassSR has led to a strategy of decomposing images being used for large image SR. The decomposed image patches have different recovery difficulties. Therefore, in ClassSR, image patches are reconstructed by different networks to g...
Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.
Investigative radiology
Jul 23, 2024
OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerate...
Dual-stage feedback network for lightweight color image compression artifact reduction.
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration o...
Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: In-hospital setting.
Resuscitation
Jul 17, 2024
AIM OF THE STUDY: This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Addit...
Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.
Medical physics
Jul 16, 2024
BACKGROUND: Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray ...
A machine learning artefact detection method for single-channel infant event-related potential studies.
Journal of neural engineering
Jul 16, 2024
. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use...
Deep learning reconstructed T2-weighted Dixon imaging of the spine: Impact on acquisition time and image quality.
European journal of radiology
Jul 15, 2024
PURPOSE: To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2) of the spine.
Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.
International journal of neural systems
Jul 12, 2024
Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the person...
Image quality of whole-body diffusion MR images comparing deep-learning accelerated and conventional sequences.
European radiology
Jul 4, 2024
OBJECTIVES: To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences.
Sliding Window Optimal Transport for Open World Artifact Detection in Histopathology.
IEEE journal of biomedical and health informatics
Jul 2, 2024
Histological images are frequently impaired by local artifacts from scanner malfunctions or iatrogenic processes - caused by preparation - impacting the performance of Deep Learning models. Models often struggle with the slightest out-of-distribution...
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