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CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity

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

Abstract

Objective

To quantitatively compare DLIR and ASiR-V with realistic anatomical images.

Methods

CT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (fpeak), mean SSIM (MSSIM), and mean GM (MGM) were used as quantitative metrics to compare image quality, for each anatomical region, protocol, algorithm, dose level, and slice thickness.

Results

Image noise had a strong (R2 > 0.99) power law relationship with dose for all algorithms. For the abdomen and chest, fpeak shifted from 0.3 (FBP) down to 0.15 mm-1 (ASiR-V 100%) with increasing ASiR-V strength but remained 0.3 mm-1 for all DLIR levels. fpeak shifted down for the brain protocol with increasing DLIR levels. Three levels of DLIR produced similar image noise levels as ASiR-V 40%, 80%, and 100%, respectively. DLIR had lower MSSIM but higher MGM than ASiR-V while matching imaging noise.

Conclusion

Compared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment.

Key Points

• DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V.

• DLIR has additional edge enhancement as compared to ASiR-V.

• DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation.

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Abbreviations

ASiR:

Adaptive Statistical Iterative Reconstruction

DLIR:

Deep-learning image reconstruction

FBP:

Filtered-backprojection

MSSIM:

Mean value of SSIM

NPS:

Noise power spectrum

SSIM:

Structure SIMilarity

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Funding

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

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Authors and Affiliations

  1. Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA

    Kai Yang, Theodore Marschall, Xinhua Li & Bob Liu

  2. Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA

    Jinjin Cao, Nisanard Pisuchpen, Avinash Kambadakone & Rajiv Gupta

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  1. Kai Yang
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Correspondence to Kai Yang.

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

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Yang, K., Cao, J., Pisuchpen, N. et al. CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity. Eur Radiol 33, 2439–2449 (2023). https://doi.org/10.1007/s00330-022-09233-0

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