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.
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The scientific guarantor of this publication is Avinash Kambadakone.
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One author of this manuscript declares relationships, unrelated to the subject matter of the article, with the following companies: GE Healthcare and Philips Healthcare.
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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