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Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT.

Methods

Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale.

Results

For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images.

Conclusion

DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT.

Key Points

• DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture.

• In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise.

• Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.

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Abbreviations

AP :

Arterial-phase

ASIR-V :

Adaptive statistical iterative reconstruction-V

ASIR-V 50%:

ASIR-V with blending factors of 50%

CNR:

Contrast-to-noise ratio

DLIR:

Deep learning image reconstruction

DLIR-H:

DLIR with high settings

DLIR-M:

DLIR with medium settings

DNN:

Deep neural networks

ERS:

Edge-rise-slope

FBP:

Filtered back projection

IR:

Iterative reconstruction

NI:

Noise index

PP:

Portal-phase

ROI:

Regions-of-interest

SD:

Standard deviation

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Funding

This study was supported by the Science Development Foundation of the First Affiliated Hospital of Xi’an Jiaotong University (2018QN-14), Key R & D Plan Project of Shaanxi Province - Joint University Project (NO. 2020GXLH-Y-026), 3D Printing Medical Research Support Project of the First Affiliated Hospital of Xi’an Jiaotong University (NO. XJTU1AF-3D-2018-003).

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Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi Province, People’s Republic of China

    Le Cao, Xiang Liu, Tingting Qu, Yannan Cheng, Yanan Li, Lihong Chen, Xinyi Niu, Qian Tian & Jianxin Guo

  2. CT Research Center, GE Healthcare China, Beijing, 100176, China

    Jianying Li

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  1. Le Cao
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  2. Xiang Liu
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Corresponding author

Correspondence to Jianxin Guo.

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Guarantor

The scientific guarantor of this publication is Prof. Jianxin Guo from the Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University.

Conflict of interest

One author (J.L.) is an employee of GE Healthcare, the manufacturer of the CT scanner and the reconstruction algorithms used in this study. All other authors of this manuscript declare no conflict of Interest with any companies, whose products or services may be related to the subject matter of the article. Authors who are not GE Healthcare employees had total control of the data and analysis used in the study.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Informed written consent was obtained from all the patients for undergoing the routine contrast-enhanced abdominal CT scans and for using their CT images in general, but informed consent for this specific retrospective study was waived.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• cohort study

• performed at one institution

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Cao, L., Liu, X., Qu, T. et al. Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT. Eur Radiol 33, 1603–1611 (2023). https://doi.org/10.1007/s00330-022-09146-y

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  • DOI: https://doi.org/10.1007/s00330-022-09146-y

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