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Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method

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Abstract

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to −4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7–9.4) HU vs 12.0 (11.4–12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4–7.4) vs 4.3 (3.3–5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.

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Authors and Affiliations

  1. Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507, Tsu, Mie, Japan

    Motonori Nagata, Yasutaka Ichikawa, Kensuke Domae, Kazuya Yoshikawa, Yoshinori Kanii, Akio Yamazaki, Naoki Nagasawa, Masaki Ishida & Hajime Sakuma

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  1. Motonori Nagata
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  2. Yasutaka Ichikawa
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  3. Kensuke Domae
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  4. Kazuya Yoshikawa
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  5. Yoshinori Kanii
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  6. Akio Yamazaki
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  7. Naoki Nagasawa
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  8. Masaki Ishida
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Correspondence to Yasutaka Ichikawa.

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Nagata, M., Ichikawa, Y., Domae, K. et al. Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method. J Digit Imaging 36, 1578–1587 (2023). https://doi.org/10.1007/s10278-023-00808-x

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