Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study

Hyun Joo Lee, Jin Sil Kim, Jeong Kyong Lee, Hye Ah Lee, Seongyong Pak

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: This study determined whether image quality and detectability of ultralow-dose hepatic multiphase CT (ULDCT, 33.3% dose) using a vendor-agnostic deep learning model(DLM) are noninferior to those of standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction(MBIR) in patients with chronic liver disease focusing on arterial phase. Methods: Sixty-seven patients underwent hepatic multiphase CT using a dual-source scanner to obtain two different radiation dose CT scans (100%, SDCT and 33.3%, ULDCT). ULDCT using DLM and SDCT using MBIR were compared. A margin of −0.5 for the difference between the two protocols was pre-defined as noninferiority of the overall image quality of the arterial phase image. Quantitative image analysis (signal to noise ratio[SNR] and contrast to noise ratio[CNR]) was also conducted. The detectability of hepatic arterial focal lesions was compared using the Jackknife free-response receiver operating characteristic analysis. Non-inferiority was satisfied if the margin of the lower limit of 95%CI of the difference in figure-of-merit was less than –0.1. Results: Mean overall arterial phase image quality scores with ULDCT using DLM and SDCT using MBIR were 4.35 ± 0.57 and 4.08 ± 0.58, showing noninferiority (difference: −0.269; 95 %CI, −0.374 to −0.164). ULDCT using DLM showed a significantly superior contrast-to-noise ratio of arterial enhancing lesion (p < 0.05). Figure-of-merit for detectability of arterial hepatic focal lesion was 0.986 for ULDCT using DLM and 0.963 for SDCT using MBIR, showing noninferiority (difference: −0.023, 95 %CI: –0.016 to 0.063). Conclusion: ULDCT using DLM with 66.7% dose reduction showed non-inferior overall image quality and detectability of arterial focal hepatic lesion compared to SDCT using MBIR.

Original languageEnglish
Article number110659
JournalEuropean Journal of Radiology
Volume159
DOIs
StatePublished - Feb 2023

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea and funded by the Ministry of Science and ICT (NRF-2021R1G1A1091351).

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea and funded by the Ministry of Science and ICT (NRF-2021R1G1A1091351).

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Deep learning
  • Hepatocellular carcinoma
  • Image reconstruction
  • Multidetector computed tomography
  • Radiation

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