deep learning

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Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic ¹⁸F-FDG PET Brain Studies
Lalith Kumar Shiyam Sundar, David Iommi, Otto Muzik, Zacharias Chalampalakis, Eva-Maria Klebermass, Marius Hienert, Lucas Rischka, Rupert Lanzenberger, Andreas Hahn, Ekaterina Pataraia, Tatjana Traub-Weidinger, Johann Hummel and Thomas Beyer

Journal of Nuclear Medicine June 1, 2021, 62 (6) 871-880; DOI: https://doi.org/10.2967/jnumed.120.248856
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Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study
Jaewon Yang, Luyao Shi, Rui Wang, Edward J. Miller, Albert J. Sinusas, Chi J. Liu, Grant T. Gullberg and Youngho Seo

Journal of Nuclear Medicine February 26, 2021, jnumed.120.256396; DOI: https://doi.org/10.2967/jnumed.120.256396
Open Access
Deep-Learning ¹⁸F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma
Nicolò Capobianco, Michel Meignan, Anne-Ségolène Cottereau, Laetitia Vercellino, Ludovic Sibille, Bruce Spottiswoode, Sven Zuehlsdorff, Olivier Casasnovas, Catherine Thieblemont and Irène Buvat

Journal of Nuclear Medicine January 1, 2021, 62 (1) 30-36; DOI: https://doi.org/10.2967/jnumed.120.242412
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Projection Space Implementation of Deep Learning–Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space
Amirhossein Sanaat, Hossein Arabi, Ismini Mainta, Valentina Garibotto and Habib Zaidi

Journal of Nuclear Medicine September 1, 2020, 61 (9) 1388-1396; DOI: https://doi.org/10.2967/jnumed.119.239327
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Artificial Intelligence in Nuclear Medicine
Felix Nensa, Aydin Demircioglu and Christoph Rischpler

Journal of Nuclear Medicine September 1, 2019, 60 (Supplement 2) 29S-37S; DOI: https://doi.org/10.2967/jnumed.118.220590
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Radiomics: Data Are Also Images
Mathieu Hatt, Catherine Cheze Le Rest, Florent Tixier, Bogdan Badic, Ulrike Schick and Dimitris Visvikis

Journal of Nuclear Medicine September 1, 2019, 60 (Supplement 2) 38S-44S; DOI: https://doi.org/10.2967/jnumed.118.220582
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Generation of PET Attenuation Map for Whole-Body Time-of-Flight ¹⁸F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps
Donghwi Hwang, Seung Kwan Kang, Kyeong Yun Kim, Seongho Seo, Jin Chul Paeng, Dong Soo Lee and Jae Sung Lee

Journal of Nuclear Medicine August 1, 2019, 60 (8) 1183-1189; DOI: https://doi.org/10.2967/jnumed.118.219493
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Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study
Julian Betancur, Lien-Hsin Hu, Frederic Commandeur, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Guido Germano, Yuka Otaki, Joanna X. Liang, Balaji K. Tamarappoo, Damini Dey, Daniel S. Berman and Piotr J. Slomka

Journal of Nuclear Medicine May 1, 2019, 60 (5) 664-670; DOI: https://doi.org/10.2967/jnumed.118.213538
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Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network
Karl D. Spuhler, John Gardus, Yi Gao, Christine DeLorenzo, Ramin Parsey and Chuan Huang

Journal of Nuclear Medicine April 1, 2019, 60 (4) 555-560; DOI: https://doi.org/10.2967/jnumed.118.214320
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Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction
Angel Torrado-Carvajal, Javier Vera-Olmos, David Izquierdo-Garcia, Onofrio A. Catalano, Manuel A. Morales, Justin Margolin, Andrea Soricelli, Marco Salvatore, Norberto Malpica and Ciprian Catana

Journal of Nuclear Medicine March 1, 2019, 60 (3) 429-435; DOI: https://doi.org/10.2967/jnumed.118.209288

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