Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study

OBJECTIVES: To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). METHODS: Effects of image reconstruction on radiomics feature...

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Detalles Bibliográficos
Autores principales: Michallek, Florian, Genske, Ulrich, Niehues, Stefan Markus, Hamm, Bernd, Jahnke, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Computed Tomography
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213380/
https://www.ncbi.nlm.nih.gov/pubmed/35174400
http://dx.doi.org/10.1007/s00330-022-08592-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213380/
https://www.ncbi.nlm.nih.gov/pubmed/35174400
http://dx.doi.org/10.1007/s00330-022-08592-y

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