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Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI

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

Abstract

Objective

This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs).

Methods

A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24–28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared.

Results

DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups.

Conclusion

For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both.

Clinical relevance statement

The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs.

Key Points

• DLIR improved the image quality and lesion conspicuity across a wide range of BMIs.

• DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels.

• On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.

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Abbreviations

ASiR-V:

Adaptive statistical iterative reconstruction-V

BMI:

Body mass index

CNR:

Contrast-to-noise ratio

CTDIvol:

Volume computed tomography dose index

DLIR:

Deep learning image reconstruction algorithm

DLP:

Dose–length product

FBP:

Filtered back projection

IR:

Iterative reconstruction

ROI:

Region of interest

SD:

Standard deviation

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Funding

This study was supported by the Key Scientific Research Project of Higher Education in Henan Province (No. 22A320057) and the Science and Technology Project of Henan province (No.222102310505).

Author information

Author notes

  1. Huixia Wang and Songwei Yue have contributed equally to this work and share first authorship.

  2. Jianbo Gao and Peijie Lyu have contributed equally to this work and share last authorship.

Authors and Affiliations

  1. The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China

    Huixia Wang, Songwei Yue, Nana Liu, Yan Chen, Pengchao Zhan, Xing Liu, Bo Shang, Jianbo Gao & Peijie Lyu

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

    Luotong Wang

  3. The Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China

    Zhen Li

Authors
  1. Huixia Wang
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Corresponding authors

Correspondence to Jianbo Gao or Peijie Lyu.

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Guarantor

The scientific guarantor of this publication is Peijie Lyu.

Conflict of interest

One of the authors (Luotong Wang) is an employee of CT imaging research center from GE Healthcare. The other coauthors who are not employees of or consultants for the company had control of inclusion of all data and information that might present a conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all participates in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• observational

• performed at one institution

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Wang, H., Yue, S., Liu, N. et al. Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI. Eur Radiol 34, 1614–1623 (2024). https://doi.org/10.1007/s00330-023-10179-0

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  • DOI: https://doi.org/10.1007/s00330-023-10179-0

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