AIMC Topic: Artifacts

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A Deep Learning-Based Model That Reduces Speed of Sound Aberrations for Improved In Vivo Photoacoustic Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Photoacoustic imaging (PAI) has attracted great attention as a medical imaging method. Typically, photoacoustic (PA) images are reconstructed via beamforming, but many factors still hinder the beamforming techniques in reconstructing optimal images i...

A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections.

Physics in medicine and biology
Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to ...

Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors.

BMC medical imaging
BACKGROUND: Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential fa...

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction.

BMC medical imaging
BACKGROUND: Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT im...

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network.

Journal of healthcare engineering
Low-dose computed tomography (CT) has proved effective in lowering radiation risk for the patients, but the resultant noise and bar artifacts in CT images can be a disturbance for medical diagnosis. The difficulty of modeling statistical features in ...

Artificial intelligence for quality control of oscillometry measures.

Computers in biology and medicine
BACKGROUND: The forced oscillation technique (FOT) allows non-invasive lung function testing during quiet breathing even without expert guidance. However, it still relies on an operator for excluding breaths with artefacts such as swallowing, glottis...

Learning Dynamic Textures for Neural Rendering of Human Actors.

IEEE transactions on visualization and computer graphics
Synthesizing realistic videos of humans using neural networks has been a popular alternative to the conventional graphics-based rendering pipeline due to its high efficiency. Existing works typically formulate this as an image-to-image translation pr...

Deep learning enabled ultra-fast-pitch acquisition in clinical X-ray computed tomography.

Medical physics
OBJECTIVE: In X-raycomputed tomography (CT), many important clinical applications may benefit from a fast acquisition speed. The helical scan is the most widely used acquisition mode in clinical CT, where a fast helical pitch can improve the acquisit...

Deep learning-based forward and cross-scatter correction in dual-source CT.

Medical physics
PURPOSE: Dual-source computed tomography (DSCT) uses two source-detector pairs offset by about 90°. In addition to the well-known forward scatter, a special issue in DSCT is cross-scattered radiation from X-ray tube A detected in the detector of syst...

Deep-learning-based motion correction in optical coherence tomography angiography.

Journal of biophotonics
Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visuali...