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

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

doi:10.1007/s10278-023-00901-1

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

J Imaging Inform Med. 2024 Feb;37(1):123-133. doi: 10.1007/s10278-023-00901-1. Epub 2024 Jan 29.

Authors

Jingyu Zhong^#¹, Zhiyuan Wu^#², Lingyun Wang^#², Yong Chen², Yihan Xia², Lan Wang², Jianying Li³, Wei Lu⁴, Xiaomeng Shi⁵, Jianxing Feng⁶, Haipeng Dong⁷, Huan Zhang⁸, Weiwu Yao⁹

Affiliations

¹ Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
² Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
³ Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China.
⁴ Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China.
⁵ Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
⁶ Haohua Technology Co., Ltd., Shanghai, 201100, China.
⁷ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. dhp40427@rjh.com.cn.
⁸ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. huanzhangy@163.com.
⁹ Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China. yaoweiwuhuan@163.com.

^# Contributed equally.

Abstract

This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.

Keywords: Deep learning; Image enhancement; Image reconstruction; Multidetector computed tomography; Reproducibility of results.

Abstract

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