Deep learning for whole-body medical image generation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine,Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00259-021-05413-0.pdf
Reference17 articles.
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3. Armanious K, et al. Unsupervised medical image translation using Cycle-MedGAN. In 2019 27th European Signal Processing Conference (EUSIPCO). 2019. IEEE.
4. Leynes AP, et al. 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. J Nucl Med. 2018;59(5):852–8.
5. Armanious K, et al. Independent attenuation correction of whole body [18 F] FDG-PET using a deep learning approach with Generative Adversarial Networks. EJNMMI Res. 2020;10:1–9.
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