AIMC Topic: Artifacts

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Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Physics in medicine and biology
Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, ...

Deep learning based correction of low performing pixel in computed tomography.

Biomedical physics & engineering express
Low Performing Pixel (LPP)/bad pixel in CT detectors cause ring and streaks artifacts, structured non-uniformities and deterioration of the image quality. These artifacts make the image unusable for diagnostic purposes. A missing/defective detector p...

Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

PloS one
Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifac...

Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach.

Computers in biology and medicine
Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). Access to LUS is challenged by user dependence and shortage of training. Image c...

MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.

Magnetic resonance imaging
PURPOSE: To compare capabilities of compressed sensing (CS) with and without deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) with and without DLR for improving examination time and image quality of shoulder MRI for...

A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification.

Scientific reports
We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem o...

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity.

PloS one
The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermos...

Virtual computed-tomography system for deep-learning-based material decomposition.

Physics in medicine and biology
Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental informati...

DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion.

Sensors (Basel, Switzerland)
Infrared images are robust against illumination variation and disguises, containing the sharp edge contours of objects. Visible images are enriched with texture details. Infrared and visible image fusion seeks to obtain high-quality images, keeping t...

Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interp...