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Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations

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

Abstract

Objectives

To investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V.

Methods

In this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR).

Results

DLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p < 0.05) followed by DLIR-M (1.85 ± 0.52, 1.66 ± 0.57, 1.69 ± 0.59, 1.68 ± 0.46), DLIR-L (2.29 ± 0.58, 1.96 ± 0.61, 1.90 ± 0.65, 1.86 ± 0.46), and ASIR-V (2.86 ± 0.67, 2.55 ± 0.58, 2.34 ± 0.66, 2.01 ± 0.36). Ratings for artifacts were similar for all reconstructions (p > 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38–102.30%) and lower noise (20.64–48.77%) than ASIR-V. DLIR-H had the best objective scores.

Conclusion

Sinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction.

Key Points

• Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques.

• DLIR may be advantageous in patients with large body habitus due to a lower image noise.

• DLIR can enable further optimization of radiation doses used in abdominal CT.

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Abbreviations

ASIR-V:

Adaptive statistical iterative reconstruction-V

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

DLIR:

Deep learning image reconstruction

FBP:

Filtered back projection

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Acknowledgements

The authors would like to thank Fred McNulty for assistance in data management and reconstruction.

Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital, White 270, 55 Fruit Street, Boston, MA, 02114, USA

    Anushri Parakh, Jinjin Cao, Theodore T. Pierce, Michael A. Blake, Cristy A. Savage & Avinash R. Kambadakone

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  1. Anushri Parakh
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  2. Jinjin Cao
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  3. Theodore T. Pierce
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  4. Michael A. Blake
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  6. Avinash R. Kambadakone
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Corresponding author

Correspondence to Avinash R. Kambadakone.

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Guarantor

The scientific guarantor of this publication is Avinash Kambadakone.

Conflict of interest

One author of this manuscript declares relationships, unrelated to the subject matter of the article, with the following companies: GE Healthcare and Philips Healthcare.

The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Mark Vangel from Harvard Catalyst kindly provided some statistical advice for this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University, and its affiliated academic healthcare centers.

One of the authors has adequate statistical expertise.

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

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Parakh, A., Cao, J., Pierce, T.T. et al. Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations. Eur Radiol 31, 8342–8353 (2021). https://doi.org/10.1007/s00330-021-07952-4

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  • DOI: https://doi.org/10.1007/s00330-021-07952-4

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