Intra-scanner repeatability of quantitative imaging features in a 3D printed semi-anthropomorphic CT phantom

Muenzfeld, Hanna; Nowak, Claus P.; Riedlberger, Stefanie; Hartenstein, Alexander; Hamm, Bernd; Jahnke, Paul; Penzkofer, Tobias

doi:10.1016/j.ejrad.2021.109818

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Poor feature stability across different images is a limitation of radiomics that has been reported in several studies investigating reproducibility [ 4 – 6 , 9 – 13 , 21 – 23 ]. Our experiments show that, even within the same acquisition and reconstruction, feature consistency, a metric of reproducibility within the same image, is low across identical tissue classes in different image positions.…”

Section: Discussionmentioning

confidence: 99%

“…A polyethylene film (8 g/m 2 ) served as thermoplastic adhesive, replacing the toner used in previous work [ 15 ]. The technique enables the manufacture of realistic textured phantoms and was used previously to create phantoms for the analysis of radiomics features [ 12 , 13 ]. The phantom used here consisted of repeated prints of the same template image, which means that all acquired phantom images displayed the same abdominal slice of the patient.…”

Section: Methodsmentioning

confidence: 99%

“…Given these limitations, the present work used advances in 3D printing of realistic textured radiomics phantoms [ 12 , 13 ] to create a phantom simulating a patient with hepatic metastases. The phantom was used to independently evaluate four generations of reconstruction algorithms for fundamental differences in image quality for radiomics.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 features were investigated using a phantom that realistically mimicked a 65-year-old patient’s abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient ≥ 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient ≥ 0.75). Results The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses ≥ 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. Key Points • Image quality of CT images reconstructed with filtered back projection and iterative methods is inadequate for the majority of radiomics features due to inconsistent tissue characterization, low discriminative power, or low repeatability. • Deep learning reconstruction enhances image quality for radiomics and more than doubled the feature yield at doses that are typically used in clinical CT imaging. • Image reconstruction algorithms can optimize image quality for more reliable quantification of tissues in CT images.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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“…However, the lack of lung parenchyma remains a question. To the best of our knowledge, 3Dprinted phantoms [45][46][47] for lung radiomics may be a solution because they could reproduce an individual patient's CT images with high precision of anatomic details and radiation attenuation properties.…”

Section: Discussionmentioning

confidence: 99%

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules

et al. 2021

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Background Radiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications. Objectives The aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies. Materials and Methods A chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research. Results A total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate <0.001). Finally, we obtained a subset of 19 stable, informative, and nonredundant RFs under all 3 sources of variability. These RFs exhibited clinical effectiveness and showed better prediction performance than unstable RFs in the validation dataset ( P = 0.017, Delong test). Conclusions The stability of RFs was affected to different degrees by test-retest and differences in CT manufacturers and models and CT acquisition and reconstruction parameters, but the influences of these factors showed relatively consistent patterns. We also obtained a subset of 19 stable, informative, and nonredundant RFs that should be preferably used to enhance the generalizability of further radiomics research.

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HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images

et al. 2022

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Intra-scanner repeatability of quantitative imaging features in a 3D printed semi-anthropomorphic CT phantom

Cited by 7 publications

References 29 publications

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

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

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules

HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images

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