Cargando…
Reducing scan time in (177)Lu planar scintigraphy using convolutional neural network: A Monte Carlo simulation study
PURPOSE: The aim of this study was to reduce scan time in (177)Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate personalized dosimetry for (177)Lu‐based peptide receptor radionuclide therapy. METHODS: The CNN model used in this work was based on DenseNet, an...
Autores principales: | Yang, Ching‐Ching, Ko, Kuan‐Yin, Lin, Pei‐Yao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562044/ https://www.ncbi.nlm.nih.gov/pubmed/37261890 http://dx.doi.org/10.1002/acm2.14056 |
Ejemplares similares
-
Monte Carlo modelling of a compact CZT-based gamma camera with application to (177)Lu imaging
por: Roth, Daniel, et al.
Publicado: (2022) -
Estimation of absorbed dose to the kidneys in patients after treatment with (177)Lu-octreotate: comparison between methods based on planar scintigraphy
por: Larsson, Maria, et al.
Publicado: (2012) -
Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved (177)Lu images
por: Rydén, T., et al.
Publicado: (2018) -
Evaluation of Dosimetric Parameters for Tumor Therapy with (177)Lu and (90)Y Radionuclides in Gate Monte Carlo Code
por: Peer-Firozjaei, Milad, et al.
Publicado: (2021) -
The effect of calibration factors and recovery coefficients on (177)Lu SPECT activity quantification accuracy: a Monte Carlo study
por: Ramonaheng, Keamogetswe, et al.
Publicado: (2021)