Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels

Zhong, Jingyu; Wu, Zhiyuan; Wang, Lingyun; Chen, Yong; Xia, Yihan; Wang, Lan; Li, Jianying; Lu, Wei; Shi, Xiaomeng; Feng, Jianxing; Dong, Haipeng; Zhang, Huan; Yao, Weiwu

doi:10.1007/s10278-023-00901-1

Cited by 4 publications

(1 citation statement)

References 52 publications

(44 reference statements)

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“…Second, our study only employed only one fast-kilovoltage-switching DECT scanner since the DLIR algorithm is vendor-specific, and we only compared the vendor-specific Asir-V algorithm with DLIR-M and DLIR-H. The inter-vendor and inter-scanner differences were not assessed [ 5 , 24 – 27 ]. However, we chose thick slice AV-50 iodine maps as the benchmark, to present the improvement accomplished by DLIR compared to the current clinical standard.…”

Section: Discussionmentioning

confidence: 99%

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

Zhong,

Wang,

Yan

et al. 2024

BMC Med Imaging

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Background To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). Methods This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. Results The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). Conclusions DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

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Section: Discussionmentioning

confidence: 99%

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

Zhong,

Wang,

Yan

et al. 2024

BMC Med Imaging

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Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study

Zhu,

Dong,

Sun

et al. 2024

Eur Radiol

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Objectives To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. Methods A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Results The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. Conclusion The radiomics features were non-reproducible with significant variability in values among different CT techniques. Clinical relevance statement Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. Key Points CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

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Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT

Zheng,

Liang,

et al. 2024

J Comput Assist Tomogr

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Objective The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). Methods One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. Results Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. Conclusions DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.

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Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels

Cited by 4 publications

References 52 publications

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study

Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT

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