Deep learning-based Dosimetry in Radionuclide Therapy: Is It Worth the Effort?-Reference-Cited by-同舟云学术

Deep learning-based Dosimetry in Radionuclide Therapy: Is It Worth the Effort?

Published:2021-10-16 Issue: Volume: Page:
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Container-title:2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
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Author:

Akhavanallaf A.¹,Salimi Y.¹,Shiri I.¹,Arabi H.¹,Hou X.²,Beauregard J. M.²,Rahmim A.²,Zaidi H.¹

Affiliation:

1. Geneva University Hospital,Division of Nuclear Medicine and Molecular Imaging,Department of Medical Imaging,Geneva 4,Switzerland,CH-1211

2. Université Laval,Cancer Research Centre,Department of Radiology and Nuclear Medicine,Quebec City,Quebec,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9875398/9875424/09875814.pdf?arnumber=9875814

Reference12 articles.

1. The promise of artificial intelligence and deep learning in PET and SPECT imaging

2. MIRD pamphlet No. 17: the dosimetry of nonuniform activity distributions--radionuclide S values at the voxel level. Medical Internal Radiation Dose Committee;bolch;J Nucl Med,1999

3. A deep learning approach to radiation dose estimation

4. Personalized brachytherapy dose reconstruction using deep learning

5. Whole-body voxel-based internal dosimetry using deep learning;akhavanallaf;European Journal of Nuclear Medicine and Molecular Imaging,2020

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