2019
DOI: 10.3348/kjr.2018.0368
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Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method

Abstract: ObjectiveTo evaluate whether computed tomography (CT) reconstruction algorithms affect the CT texture features of the liver parenchyma.Materials and MethodsThis retrospective study comprised 58 patients (normal liver, n = 34; chronic liver disease [CLD], n = 24) who underwent liver CT scans using a single CT scanner. All CT images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR) (iDOSE4), and model-based IR (IMR). On arterial phase (AP) and portal venous phase (PVP)… Show more

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Cited by 15 publications
(13 citation statements)
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“…Further study is required to validate our results externally and generalize the utility of the radiomics analysis of AVC. Second, the reproducibility of radiomics features was not fully considered, specifically regarding the effects of the CT scanner, image acquisition, and reconstruction parameters on the extracted radiomics features and prediction models ( 34 35 ). Application of deep learning algorithm may help solve the problem of reproducibility in radiomics by reducing variability in scan protocol, for example, conversion of reconstruction kernel or slice thickness ( 36 37 38 ).…”
Section: Discussionmentioning
confidence: 99%
“…Further study is required to validate our results externally and generalize the utility of the radiomics analysis of AVC. Second, the reproducibility of radiomics features was not fully considered, specifically regarding the effects of the CT scanner, image acquisition, and reconstruction parameters on the extracted radiomics features and prediction models ( 34 35 ). Application of deep learning algorithm may help solve the problem of reproducibility in radiomics by reducing variability in scan protocol, for example, conversion of reconstruction kernel or slice thickness ( 36 37 38 ).…”
Section: Discussionmentioning
confidence: 99%
“…Sung et al [8] investigated the influence of three different reconstruction methods (FBP, hybrid IR (iDOSE) and model-based IR (IMR)) on CT first-order texture features of liver parenchyma in both normal and in chronic liver disease on scans acquired with contrast medium. CTTA was performed with the same software applied for our study.…”
Section: Discussionmentioning
confidence: 99%
“…Despite texture analysis being a promising tool for quantitative assessment of images, due to the novelty of the technique applied in medical imaging, CTTA still needs to be standardized and validated. An important aspect is the CTTA reproducibility related to the influence of CT acquisition parameters (e.g., level of radiation dose, slice thickness, reconstruction algorithms) that can affect results and standardization among different Diagnostics 2021, 11, 1000 2 of 10 studies [8][9][10]. This aspect is gaining interest, as shown by Erdal et al [11] who demonstrated that slice thickness influenced reproducibility of radiomic features in lung nodules, and Prezzi et al [12] who showed the influence of iterative reconstruction (IR) algorithm versus traditional filtered back projection (FBP) on radiomics quantification in twenty-eight datasets of colorectal cancer.…”
Section: Introductionmentioning
confidence: 99%
“…In the present study, when iDose4 level 7 was used, the proportion of the obtained images meeting the diagnostic requirements greatly increased. Iterative model reconstruction (IMR) might be able to broaden the experiment results further, although there are different results and opinions (18,19). At the same time, although the thin slices showed details in high resolution, the significant increase in noise harmed the appearance of the ground-glass nodule boundary.…”
Section: Discussionmentioning
confidence: 99%