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Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning
[article]
2021
arXiv
pre-print
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To the best of our knowledge, the proposed method is the first to tackle super-resolution and motion artifact correction simultaneously in the context of MRI using unsupervised learning. ...
To overcome the limitations, here we propose a fully unsupervised quality enhancement scheme, which boosts the resolution and removes the motion artifact simultaneously. ...
Conclusion In this work, we proposed a novel unsupervised deep learning method for simultaneous super-resolution and motion artifact correction. ...
arXiv:2105.00240v1
fatcat:zpuz5ltljbehzc7lg76regaxay
Applications of Deep Learning to Neuro-Imaging Techniques
2019
Frontiers in Neurology
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Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, ...
There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing ...
(GAN-CIRCLE), for super-resolution MRI from low-resolution MRI. ...
doi:10.3389/fneur.2019.00869
pmid:31474928
pmcid:PMC6702308
fatcat:yki64mb57jhafduasd3hohfkgi
Front Matter: Volume 10133
2017
Medical Imaging 2017: Image Processing
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of the prostate on CT images using deep learning and multi-atlas fusion [10133-99] 10133 2P AWM: Adaptive Weight Matting for medical image segmentation [10133-13] 10133 2Q Pseudo CT estimation from MRI ...
[10133-46]
Simultaneous segmentation and correspondence improvement using statistical modes
[10133-47]
10133 1C
Improved automatic optic nerve radius estimation from high resolution MRI [10133-48 ...
doi:10.1117/12.2270368
dblp:conf/miip/X17
fatcat:resfpzholvbtbalfkbg3pj64gu
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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denoising and artifact removal in arterial spin labelling MRI 680 Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences 681 3D Segmentation with Exponential Logarithmic Loss ...
Synthetic CT Generation Using Multi-view Deep Convolutional Neural Networks 567 Deep learning with synthetic diffusion MRI data for free-water elimination in glioblastoma cases 568 3D Deep Convolutional ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Deep Learning in MR Image Processing
2019
Investigative Magnetic Resonance Imaging
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Deep Learning: a Brief Overview Deep learning is a branch of machine learning based on the use of multiple layers to learn data representations, and can be applied to both supervised and unsupervised learning ...
In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications. 82 Deep Learning in MRI | Doohee Lee, et al. directions of deep ...
In this section, we review recent deep learning studies on MR image denoising, artifact correction, super-resolution, and other quality enhancement methods. ...
doi:10.13104/imri.2019.23.2.81
fatcat:txjrlwhklbh47nxbwiq55xkhva
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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Nazir, A., Brain Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors. ...
., +, TIP 2020 2380-2394 Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Table of contents
2020
IEEE Transactions on Image Processing
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Chen 4027 Learning a Deep Dual Attention Network for Video Super-Resolution ...................... F. Li, H. Bai, and Y. ...
Chen, and Z. Gao 1725 Deep Coupled ISTA Network for Multi-Modal Image Super-Resolution ............... X. Deng and P. ...
Lin, and Zhang, Y. Tian, K. Wang, W. Zhang, and F.- ...
doi:10.1109/tip.2019.2940373
fatcat:i7hktzn4wrfz5dhq7hj75u6esa
Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow—Editorial for the 2nd Special Issue "Machine Learning for Image Reconstruction"
2021
IEEE Transactions on Medical Imaging
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, and report our verification of the shared deep learning codes. ...
In this editorial, we provide a brief background illustrating the motivation for the development of network-based, data-driven, and learning-oriented reconstruction methods, summarize the included papers ...
Deep PET In [A16], In[A14],Oh et al.propose an unpaired deep learning method for MR motion artifact removal that does not require matched motion-free and motion artifact images. ...
doi:10.1109/tmi.2021.3115547
fatcat:udpvw2kkzneexkiyvvjc2oxpoe
An overview of deep learning in medical imaging focusing on MRI
2018
Zeitschrift für Medizinische Physik
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As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. ...
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition ...
Our work was financially supported by the Bergen Research Foundation through the project "Computational medical imaging and machine learning -methods, infrastructure and applications". ...
doi:10.1016/j.zemedi.2018.11.002
fatcat:kkimovnwcrhmth7mg6h6cpomjm
Table of contents
2020
IEEE Transactions on Image Processing
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Huang 4461 Learning a Deep Dual Attention Network for Video Super-Resolution ...................... F. Li, H. Bai, and Y. ...
Zhang, and F. Nie 2139 Face Hallucination Using Cascaded Super-Resolution and Identity Priors ....... K. Grm, W. J. Scheirer, and V. ...
doi:10.1109/tip.2019.2940372
fatcat:h23ul2rqazbstcho46uv3lunku
A comprehensive review of deep learning-based single image super-resolution
2021
PeerJ Computer Science
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This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used ...
In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. ...
Since the diffusion MRI has high image acquisition time and low resolution, Super-resolution Reconstruction Diffusion Tensor Imaging (SRR-DTI) reconstructed HR diffusion parameters from LR diffusion-weighted ...
doi:10.7717/peerj-cs.621
fatcat:jsd6fw3ewjeudgnypdam7xy5oy
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
[article]
2022
arXiv
pre-print
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Empirically, we observe comparable or better performance to supervised learning techniques in several medical imaging tasks in CT and MRI, while demonstrating significantly better generalization to unknown ...
Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). ...
ACKNOWLEDGMENTS YS is supported by the Apple PhD Fellowship in AI/ML. LS is supported by the Stanford Bio-X Graduate Student Fellowship. ...
arXiv:2111.08005v2
fatcat:j5v4d2auafexlitjllhq32on3e
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
Note that the item title is found only under the primary entry in the Author Index. ...
., +, TIP 2021 7964-7979 Deep Shearlet Residual Learning Network for Single Image Super-Resolu-TIP 2021 9030-9042 Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
3D Deep Learning on Medical Images: A Review
[article]
2020
arXiv
pre-print
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We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field. ...
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical ...
and MRI scans [14] using 3D deep learning. ...
arXiv:2004.00218v4
fatcat:iucszcjffnbwbbzc4zzqpbvahy
3D Deep Learning on Medical Images: A Review
2020
Sensors
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We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field. ...
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical ...
and MRI scans [14] using 3D deep learning. ...
doi:10.3390/s20185097
pmid:32906819
pmcid:PMC7570704
fatcat:top2ambpizdzdpsqamz2xm643u
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