AIMC Topic: Artifacts

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Automated OCT angiography image quality assessment using a deep learning algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.

NeuroImage
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue ...

Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging.

IEEE transactions on medical imaging
The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgment accura...

Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like re...

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model.

Magnetic resonance in medicine
PURPOSE: We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model-based motion minimization.

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...

Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net.

Ultrasonics
A Fully Convolutional Network (FCN) based deep architecture called Dual Path U-Net (DPU-Net) is proposed for automatic segmentation of the lumen and media-adventitia in IntraVascular UltraSound (IVUS) frames, which is crucial for diagnosis of many ca...

Data-driven synthetic MRI FLAIR artifact correction via deep neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow,...

Human electrocortical dynamics while stepping over obstacles.

Scientific reports
To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We ...

Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI.

Magnetic resonance imaging
For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via ...