Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs-Reference-Cited by-同舟云学术

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Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs

Published:2024-08-21 Issue: Volume: Page:
ISSN:1352-8661
Container-title:Magnetic Resonance Materials in Physics, Biology and Medicine
language:en
Short-container-title:Magn Reson Mater Phy

Author:

Yang Jaewon^ORCID,Afaq Asim,Sibley Robert,McMilan Alan,Pirasteh Ali

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10334-024-01199-y.pdf

Reference105 articles.

1. Currie GM, Leon JL, Nevo E, Kamvosoulis PV (2021) PET/MR Part 4: clinical applications of PET/MRI. J Nucl Med Technol. https://doi.org/10.2967/jnmt.121.263288

2. McMillan AB, Bradshaw TJ (2021) Artificial intelligence-based data corrections for attenuation and scatter in position emission tomography and single-photon emission computed tomography. PET Clin 16(4):543–552

3. Spadea MF, Maspero M, Zaffino P, Seco J (2021) Deep learning based synthetic-CT generation in radiotherapy and PET: a review. Med Phys 48(11):6537–6566

4. Ahangari S, Beck Olin A, Kinggard Federspiel M, Jakoby B, Andersen TL, Hansen AE, Fischer BM, Littrup Andersen F (2022) A deep learning-based whole-body solution for PET/MRI attenuation correction. EJNMMI Phys 9(1):55

5. Schramm G, Rigie D, Vahle T, Rezaei A, Van Laere K, Shepherd T, Nuyts J, Boada F (2021) Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network. Neuroimage 224:117399

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1. Quantitative body magnetic resonance imaging: how to make it work;Magnetic Resonance Materials in Physics, Biology and Medicine;2024-09-11

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