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Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising
2021
Quantitative Imaging in Medicine and Surgery
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This study aims to apply and evaluate the use of Pix2Pix generative adversarial network (Pix2Pix GAN) in denoising low dose MP SPECT images. ...
were evaluated along with a reference convolutional auto encoder (CAE) network and postreconstruction filters. ...
Acknowledgments The authors would like to thank Mr. Qi Zhang and Ms. Yingqing Lyu for assisting data analysis. The authors also would like to thank Dr. ...
doi:10.21037/qims-21-1042
fatcat:57anq2k3orfkxo6siksvvxmiky
Front Matter: Volume 10574
2018
Medical Imaging 2018: Image Processing
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SPIE uses a seven-digit CID article numbering system structured as follows: The first five digits correspond to the SPIE volume number. The last two digits indicate publication order within the volume ...
using a Base 36 numbering system employing both numerals and letters. ...
and conditional generative adversarial networks 10574 0A Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional ...
doi:10.1117/12.2315755
fatcat:jdfbaent6vhu5dwlrqrqt66vce
Front Matter: Volume 10573
2018
Medical Imaging 2018: Physics of Medical Imaging
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Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ...
SPIE uses a seven-digit CID article numbering system structured as follows: The first five digits correspond to the SPIE volume number. The last two digits indicate publication order within the volume ...
1N
Deep learning angiography (DLA): three-dimensional C-arm cone beam CT angiography
generated from deep learning method using a convolutional neural network [10573-58]
vi
Proc. of SPIE Vol. 10573 ...
doi:10.1117/12.2323748
fatcat:mn5csad2mjezljnvzxem3rhk5i
Narrative review of generative adversarial networks in medical and molecular imaging
2021
Annals of Translational Medicine
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In regards to EKG-gated cardiac CT scans without contrast enhancement for coronary calcium scoring, denoising in low dose CT images is useful to avoid excessive radiation dose for the patient (40) . ...
General techniques of deep learning with GANs
Convolutional neural network (CNN) Based on Huber-Wiesel's hierarchical hypothesis of visual information processing, a CNN consists of convolutional layers ...
The authors have no other conflicts of interest to declare. ...
doi:10.21037/atm-20-6325
fatcat:6dfpalmijjcrnmlb7e6ppycloq
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
[article]
2020
arXiv
pre-print
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Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant ...
We first provide a comprehensive overview of deep learning techniques and popular network architectures, by introducing various types of deep neural networks and recent developments. ...
networks
ANN
Cerebrospinal fluid
CSF
Back-propagation
BP
Computed tomography
CT
Convolutional neural networks
CNN
Diffusion tensor imaging
DTI
Denoising auto-encoders
DAE
Electroencephalogram ...
arXiv:2005.04573v1
fatcat:64ze55onzfemhgpebvsewe3fki
Applications of artificial intelligence in nuclear medicine image generation
2021
Quantitative Imaging in Medicine and Surgery
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It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. ...
This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical ...
(111) used 3D OSEM PET and 3D structure MRI as input to train a residual network (a purely convolutional shift-invariant neural network). ...
doi:10.21037/qims-20-1078
pmid:34079744
pmcid:PMC8107336
fatcat:36vdnuatljbmzayjw3azmrtdee
LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks
2021
Quantitative Imaging in Medicine and Surgery
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In this process, we merge a convolutional neural network (CNN) and a residual network for feature extraction and image reconstruction. ...
Therefore, we propose a deep learning method called LCPR-Net, which is used for directly reconstructing full-count (FC) PET images from corresponding LC sinogram data. ...
Acknowledgments The authors would like to thank the editor and anonymous reviewers for their constructive comments and suggestions. ...
doi:10.21037/qims-20-66
pmid:33532274
pmcid:PMC7779905
fatcat:d26gj4qsrbcd3jtcifsuepo3jq
Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation
[article]
2021
arXiv
pre-print
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State-of-the-art PET image segmentation methods are based on convolutional neural networks (CNNs) given their ability to leverage annotated datasets to derive high-level features about the disease process ...
In this study, we propose a spatio-temporal 'dual-stream' neural network (ST-DSNN) to segment sequential whole-body PET scans. ...
State-of-the-art PET image segmentation is based on convolutional neural networks (CNNs) [11, 12] . ...
arXiv:2106.04961v1
fatcat:47x2hs4ygbbylnqxaqyzlqkvwe
2020 Index IEEE Journal of Selected Topics in Signal Processing Vol. 14
2020
IEEE Journal on Selected Topics in Signal Processing
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., +, JSTSP Oct. 2020 1280-1291 PET Image Reconstruction Using a Cascading Back-Projection Neural Network. ...
Verdoliva, L., JSTSP Aug. 2020 910-932 PET Image Reconstruction Using a Cascading Back-Projection Neural Network. ...
doi:10.1109/jstsp.2020.3029672
fatcat:6twwzcqpwzg4ddcu2et75po77u
A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis
2020
Frontiers in Neuroscience
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Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant ...
We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. ...
neural networks, graph convolutional networks, and recurrent neural networks. ...
doi:10.3389/fnins.2020.00779
pmid:33117114
pmcid:PMC7578242
fatcat:tzdcq3kyyrefvn7vxgdj5lnhju
U-Net and its variants for medical image segmentation: theory and applications
[article]
2020
arXiv
pre-print
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The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. ...
U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. ...
Recurrent Convolutional Network Recurrent neural networks are a type of neural network initially designed to analyze sequential data such as text or audio data. ...
arXiv:2011.01118v1
fatcat:u2blyrazp5hlhnvulidcvbtu64
An overview of deep learning in medical imaging focusing on MRI
2018
Zeitschrift für Medizinische Physik
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Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. ...
The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. ...
Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article. ...
doi:10.1016/j.zemedi.2018.11.002
fatcat:kkimovnwcrhmth7mg6h6cpomjm
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. ...
Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
The promise of artificial intelligence and deep learning in PET and SPECT imaging
2021
Physica medica (Testo stampato)
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emission tomography (PET) imaging. ...
A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. ...
Wang et al. developed a convolutional neural network for left ventricular functional assessment from gated MPI-SPECT images to circumvent the tedious and subjective task of manual segmentation/adjustment ...
doi:10.1016/j.ejmp.2021.03.008
pmid:33765602
fatcat:onw4fm22y5cxndxiwyy5bdw4t4
Deep learning-based noise reduction in low dose SPECT Myocardial Perfusion Imaging: Quantitative assessment and clinical performance
[article]
2021
arXiv
pre-print
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A generative adversarial network was implemented to predict non-gated normal-dose images in the projection space at the different reduced dose levels. ...
Clinical SPECT-MPI images of 345 patients acquired from a dedicated cardiac SPECT in list-mode format were retrospectively employed to predict normal-dose images from low-dose data at the half, quarter ...
Song et al. investigated a 3D residual convolutional neural network (CNN) model to predict standard-dose images from 1/4-dose gated SPECT-MPI images [26] . ...
arXiv:2103.11974v1
fatcat:r6ctdxu7afbkjmmjpnewd6vuaq
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