Author Correction: Low-count whole-body PET with deep learning in a multicenter and externally validated study.
作者信息
Chaudhari Akshay S, Mittra Erik, Davidzon Guido A, Gulaka Praveen, Gandhi Harsh, Brown Adam, Zhang Tao, Srinivas Shyam, Gong Enhao, Zaharchuk Greg, Jadvar Hossein
机构信息
Department of Radiology, Stanford University, Palo Alto, CA, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
出版信息
NPJ Digit Med. 2021 Sep 14;4(1):139. doi: 10.1038/s41746-021-00512-6.
Abstract
摘要
相似文献
1
Author Correction: Low-count whole-body PET with deep learning in a multicenter and externally validated study.作者更正：多中心外部验证研究中基于深度学习的低计数全身PET
NPJ Digit Med. 2021 Sep 14;4(1):139. doi: 10.1038/s41746-021-00512-6.
2
Deep-JASC: joint attenuation and scatter correction in whole-body F-FDG PET using a deep residual network.深度联合衰减与散射校正（Deep-JASC）：利用深度残差网络进行全身F-FDG PET中的联合衰减和散射校正
Eur J Nucl Med Mol Imaging. 2020 Oct;47(11):2533-2548. doi: 10.1007/s00259-020-04852-5. Epub 2020 May 15.
3
Deep learning-based metal artefact reduction in PET/CT imaging.基于深度学习的 PET/CT 成像中的金属伪影减少。
Eur Radiol. 2021 Aug;31(8):6384-6396. doi: 10.1007/s00330-021-07709-z. Epub 2021 Feb 10.
4
Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks.使用循环一致生成对抗网络从低计数统计数据估算全身 PET。
Phys Med Biol. 2019 Nov 4;64(21):215017. doi: 10.1088/1361-6560/ab4891.
5
Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction.Dixon-VIBE 深度学习（DIVIDE）用于骨盆 PET/MR 衰减校正的伪 CT 合成。
J Nucl Med. 2019 Mar;60(3):429-435. doi: 10.2967/jnumed.118.209288. Epub 2018 Aug 30.
6
[Generation of the Pseudo CT Image Based on the Deep Learning Technique Aimed for the Attenuation Correction of the PET Image].基于深度学习技术生成用于PET图像衰减校正的伪CT图像
Nihon Hoshasen Gijutsu Gakkai Zasshi. 2020;76(11):1152-1162. doi: 10.6009/jjrt.2020_JSRT_76.11.1152.
7
Generation of PET Attenuation Map for Whole-Body Time-of-Flight F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps.基于同时重建的活性和衰减图训练的深度神经网络生成全身飞行时间 F-FDG PET/MRI 的 PET 衰减图。
J Nucl Med. 2019 Aug;60(8):1183-1189. doi: 10.2967/jnumed.118.219493. Epub 2019 Jan 25.
8
Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.零回波时间和 Dixon 深度伪 CT（ZeDD CT）：使用多参数 MRI 的深度卷积神经网络直接生成用于骨盆 PET/MRI 衰减校正的伪 CT 图像。
J Nucl Med. 2018 May;59(5):852-858. doi: 10.2967/jnumed.117.198051. Epub 2017 Oct 30.
9
Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion.基于深度学习对象补全的 PET/MRI 衰减校正中截断补偿和金属牙种植体伪影减少。
Phys Med Biol. 2020 Sep 25;65(19):195002. doi: 10.1088/1361-6560/abb02c.
10
Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging.基于未校正衰减的 PET 图像的全身 PET 成像的合成 CT 生成。
Phys Med Biol. 2019 Nov 4;64(21):215016. doi: 10.1088/1361-6560/ab4eb7.
引用本文的文献
1
Changes in Radiology Due to Artificial Intelligence That Can Attract Medical Students to the Specialty.人工智能给放射学带来的变化，这些变化可能会吸引医学生投身该专业。
JMIR Med Educ. 2023 Mar 20;9:e43415. doi: 10.2196/43415.
Zotero 插件