Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations

Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations

PurposeThis study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximizati...

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Bibliographic Details
Main Authors: Mitsuaki Tatsumi, Fumihiko Soeda, Takashi Kamiya, Junpei Ueda, Daisuke Katayama, Keiko Matsunaga, Tadashi Watabe, Hiroki Kato, Noriyuki Tomiyama
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-08-01
Series:Frontiers in Oncology
Subjects:
Bayesian penalized likelihood reconstruction
PET
quantitative imaging
abdomen
malignant tumor
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.707023/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2021.707023/full

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