Deep learning-based approaches for attenuation correction in [(18)F]FDG PET: Current advances and future directions.
- DOI
- 10.1016/j.apradiso.2026.112934
- Published
- 2026 Sep 10
- Container
- Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.apradiso.2026.112934,
title = {Deep learning-based approaches for attenuation correction in [(18)F]FDG PET: Current advances and future directions.},
author = {Tahmasebzadeh A and Ghafarian P and Sadeghi M},
year = {2026},
journal = {Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine},
doi = {10.1016/j.apradiso.2026.112934},
url = {https://doi.org/10.1016/j.apradiso.2026.112934}
}RIS
TY - JOUR TI - Deep learning-based approaches for attenuation correction in [(18)F]FDG PET: Current advances and future directions. AU - Tahmasebzadeh A AU - Ghafarian P AU - Sadeghi M PY - 2026 JO - Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine DO - 10.1016/j.apradiso.2026.112934 UR - https://doi.org/10.1016/j.apradiso.2026.112934 ER -
APA
A, T., P, G., & M, S. (2026). Deep learning-based approaches for attenuation correction in [(18)F]FDG PET: Current advances and future directions.. Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine. https://doi.org/10.1016/j.apradiso.2026.112934
Source records
- pubmed · retrieved 2026-09-25T14:40:45.999Z