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Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising

Jingzhang Sun, Yu Du, Chien‐Ying Li, Tung‐Hsin Wu, Bang‐Hung Yang, Greta S. P. Mok
2021 Quantitative Imaging in Medicine and Surgery  
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

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
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

Guang-Hong Chen, Joseph Y. Lo, Taly Gilat Schmidt
2018 Medical Imaging 2018: Physics of Medical Imaging  
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

Kazuhiro Koshino, Rudolf A. Werner, Martin G. Pomper, Ralph A. Bundschuh, Fujio Toriumi, Takahiro Higuchi, Steven P. Rowe
2021 Annals of Translational Medicine  
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]

Li Zhang and Mingliang Wang and Mingxia Liu and Daoqiang Zhang
2020 arXiv   pre-print
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

Zhibiao Cheng, Junhai Wen, Gang Huang, Jianhua Yan
2021 Quantitative Imaging in Medicine and Surgery  
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

Hengzhi Xue, Qiyang Zhang, Sijuan Zou, Weiguang Zhang, Chao Zhou, Changjun Tie, Qian Wan, Yueyang Teng, Yongchang Li, Dong Liang, Xin Liu, Yongfeng Yang (+3 others)
2021 Quantitative Imaging in Medicine and Surgery  
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]

Kai-Chieh Liang, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
2021 arXiv   pre-print
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  
., +, 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

Li Zhang, Mingliang Wang, Mingxia Liu, Daoqiang Zhang
2020 Frontiers in Neuroscience  
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]

Nahian Siddique, Paheding Sidike, Colin Elkin, Vijay Devabhaktuni
2020 arXiv   pre-print
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

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
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

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
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

Hossein Arabi, Azadeh AkhavanAllaf, Amirhossein Sanaat, Isaac Shiri, Habib Zaidi
2021 Physica medica (Testo stampato)  
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]

Narges Aghakhan Olia, Alireza Kamali-Asl1, Sanaz Hariri Tabrizi, Parham Geramifar, Peyman Sheikhzadeh, Saeed Farzanefar, Hossein Arabi
2021 arXiv   pre-print
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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