Results 71 to 80 of about 7,799,574 (379)
Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians
Journal of Magnetic Resonance Imaging, 2020 Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep‐learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an
Dana J. Lin +3 more
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Deep learning for biomedical image reconstruction: a survey [PDF]
Artificial Intelligence Review, 2020 Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of information indispensable for understanding, modelling, diagnosis, and treatment of diseases ...
Hanene Ben Yedder +2 more
semanticscholar +1 more source
Simulation of 3D Image Reconstruction in Rigid body Motion
MATEC Web of Conferences, 2018 3D image reconstruction under rigid body motion is affected by rigid body motion and visual displacement factors, which leads to low quality of 3D image reconstruction and more noise, in order to improve the quality of 3D image reconstruction of rigid ...
Chen Huihong, Li Shiming
doaj +1 more source
DUG-RECON: A Framework for Direct Image Reconstruction using Convolutional Generative Networks [PDF]
, 2020 This paper explores convolutional generative networks as an alternative to iterative reconstruction algorithms in medical image reconstruction. The task of medical image reconstruction involves mapping of projection main data collected from the detector to the image domain.
arxiv +1 more source
Deep Learning for PET Image Reconstruction
IEEE Transactions on Radiation and Plasma Medical Sciences, 2020 This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET).
A. Reader +5 more
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Scene Graph Generation With External Knowledge and Image Reconstruction [PDF]
Computer Vision and Pattern Recognition, 2019 Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction, etc.
Jiuxiang Gu +5 more
semanticscholar +1 more source
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
Remote Sensing, 2023 High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution ...
Xuan Wang +3 more
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Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
, 2012 Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging ...
Alexander A Oraevsky +9 more
core +1 more source
IEEE Signal Processing Magazine, 2020 The development of compressed-sensing (CS) methods for magnetic resonance (MR) image reconstruction led to an explosion of research on models and optimization algorithms for MR imaging (MRI).
J. Fessler
semanticscholar +1 more source
EURASIP Journal on Image and Video Processing, 2018 Considering the non-uniqueness and instability of concrete defect detection in construction engineering, ultrasonic time of flight data and maximum likelihood expectation maximization algorithm were proposed to improve the readability of the ultrasonic ...
Honghui Fan, Hongjin Zhu
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