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Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction

  • Pancreas
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of images reconstructed using hybrid iterative reconstruction (IR).

Methods

Our institutional review board approved this prospective study. Written informed consent was obtained from all patients. Twenty-eight consecutive patients with PDAC undergoing chemotherapy (14 men and 14 women; mean age, 68.4 years) underwent pancreatic LDCT for therapy evaluation. The LDCT images were reconstructed using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H). The image noise, diagnostic acceptability, and conspicuity of PDAC were qualitatively assessed using a 5-point scale. CT numbers of the abdominal aorta, portal vein, pancreas, PDAC, background noise, signal-to-noise ratio (SNR) of the anatomical structures, and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H images.

Results

CT dose-index volumes and dose-length product in pancreatic LDCT were 2.3 ± 1.0 mGy and 74.9 ± 37.0 mGy•cm, respectively. The image noise, diagnostic acceptability, and conspicuity of PDAC were significantly better in DLIR-H than those in hybrid-IR and DLIR-M (all P < 0.001). The background noise was significantly lower in the DLIR-H images (P < 0.001) and resulted in improved SNRs (P < 0.001) and CNR (P < 0.001) compared with those in the hybrid-IR and DLIR-M images.

Conclusion

DLIR significantly reduced image noise and improved image quality in pancreatic LDCT images compared with hybrid-IR.

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Authors and Affiliations

  1. Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan

    Yoshifumi Noda, Yukako Iritani, Nobuyuki Kawai & Masayuki Matsuo

  2. Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan

    Toshiharu Miyoshi

  3. Innovative and Clinical Research Promotion Center, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan

    Takuma Ishihara

  4. Department of Radiology, Frontier Science for Imaging, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan

    Fuminori Hyodo

Authors
  1. Yoshifumi Noda
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  2. Yukako Iritani
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  3. Nobuyuki Kawai
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  4. Toshiharu Miyoshi
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  7. Masayuki Matsuo
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Correspondence to Yoshifumi Noda.

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Noda, Y., Iritani, Y., Kawai, N. et al. Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction. Abdom Radiol 46, 4238–4244 (2021). https://doi.org/10.1007/s00261-021-03111-x

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  • DOI: https://doi.org/10.1007/s00261-021-03111-x

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