A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning-Reference-Cited by-同舟云学术

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A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning

Published:2019 Issue: Volume: Page:723-731
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Shi Luyao,Onofrey John A.,Revilla Enette Mae,Toyonaga Takuya,Menard David,Ankrah Joseph,Carson Richard E.,Liu Chi,Lu Yihuan

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-32251-9_79

Reference10 articles.

1. Hunter, C.R., Klein, R., Beanlands, R.S., et al.: Patient motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging. Med. Phys. 43, 1829 (2016)

2. Rezaei, A., Defrise, M., Bal, G., et al.: Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans. Med. Imaging 31, 2224–2233 (2012)

3. Hamill, J., Panin, V.: TOF-MLAA for attenuation correction in thoracic PET/CT. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (2012)

4. Hwang, D., Kang, S.K., Kim, K.Y., et al.: Generation of PET attenuation map for whole-body time-of-flight (18)F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J. Nucl. Med. 60(8), 1183–1189 (2019)

5. You, C., Yang, Q., Gjesteby, L., et al.: Structurally-sensitive multi-scale deep neural network for low-dose CT denoising. IEEE Access 6, 41839–41855 (2018)

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