AIMC Topic: Artifacts

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Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.

Diagnostic and interventional imaging
PURPOSE: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and t...

End-to-end deep learning for interior tomography with low-dose x-ray CT.

Physics in medicine and biology
There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, such as (1) sparse-view CT, (2) low-dose CT and (3) region-of-interest (ROI) CT (called interior tomography). To further reduce the dose, sparse-view ...

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and n...

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation.

IEEE transactions on medical imaging
Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion can be estimated simultaneously from respiratory gated data through a joint estimation framework. H...

Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI.

IEEE transactions on medical imaging
Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement fi...

[Modern mid-field magnetic resonance imaging in private practice : Field report].

Der Radiologe
BACKGROUND: With the 0.55 T magnetic resonance imaging (MRI) scanner "Free.Max", a new device concept in the mid-field sector is being introduced into the market. New technologies and artificial intelligence (AI) applications as well as a new coil co...

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network.

Computational intelligence and neuroscience
With the continuous development of imaging sensors, images contain more and more information, the images presented by different types of sensors are different, and the images obtained by the same type of sensors under different parameters or conditio...

Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models.

Journal of digital imaging
To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video frames inside organs in endoscopy. However, multiple artefacts exist in these video frames that cause d...

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.

NeuroImage
Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are inter...

Phase retrieval based on deep learning in grating interferometer.

Scientific reports
Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is di...