Cargando…
Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study
OBJECTIVES: To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning–based image reconstruction algorithm (DLR, TrueFidelity™). METHODS: Computed tomography (CT) images of a multi-sized image quality phantom...
Autores principales: | Choi, Hyunsu, Chang, Won, Kim, Jong Hyo, Ahn, Chulkyun, Lee, Heejin, Kim, Hae Young, Cho, Jungheum, Lee, Yoon Jin, Kim, Young Hoon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364308/ https://www.ncbi.nlm.nih.gov/pubmed/34390372 http://dx.doi.org/10.1007/s00330-021-08199-9 |
Ejemplares similares
-
Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study
por: Michallek, Florian, et al.
Publicado: (2022) -
Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction
por: Hong, Jung Hee, et al.
Publicado: (2020) -
75% radiation dose reduction using deep learning reconstruction on low-dose chest CT
por: Jo, Gyeong Deok, et al.
Publicado: (2023) -
Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT
por: Yeoh, Hyunjung, et al.
Publicado: (2021) -
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images
por: Ni, Qianqian, et al.
Publicado: (2020)