Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules | Zendy

Kai Ye | Zendy; Min Chen | Zendy; Qiao Zhu | Zendy; Yuliu Lu | Zendy; Huishu Yuan | Zendy

Open Access

Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules

Author(s) -

Kai Ye,

Min Chen,

Qiao Zhu,

Yuliu Lu,

Huishu Yuan

Publication year - 2021

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims-20-932

Subject(s) - medicine , concordance correlation coefficient , radiomics , concordance , multidetector computed tomography , nuclear medicine , reproducibility , computed tomography , feature (linguistics) , radiology , mathematics , statistics , linguistics , philosophy

The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT.

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