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Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Deep learning reconstruction (DLR) introduces deep convolutional neural networks into the reconstruction flow. We examined the clinical applicability of drip-infusion cholangiography (DIC) acquired on an ultra-high-resolution CT (U-HRCT) scanner reconstructed with DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).

Methods

This retrospective, single-institution study included 30 patients seen between January 2018 and November 2019. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) in the common bile duct. The overall visual image quality of the bile duct on thick-slab maximum intensity projections was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (not delineated) to 5 (clearly delineated). The difference among hybrid-IR, MBIR, and DLR images was compared.

Results

The image noise was significantly lower on DLR than hybrid-IR and MBIR images and the CNR and the overall visual image quality of the bile duct were significantly higher on DLR than on hybrid-IR and MBIR images (all: p < 0.001).

Conclusion

DLR resulted in significant quantitative and qualitative improvement of DIC acquired with U-HRCT.

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Funding

Kazuo Awai received a research grant from Canon Medical Systems Co. Ltd. (Grant No. A1700878)

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Authors and Affiliations

  1. Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

    Keigo Narita, Yuko Nakamura, Toru Higaki, Motonori Akagi, Yukiko Honda & Kazuo Awai

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  1. Keigo Narita
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  2. Yuko Nakamura
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  3. Toru Higaki
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  4. Motonori Akagi
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Correspondence to Yuko Nakamura.

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Narita, K., Nakamura, Y., Higaki, T. et al. Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography. Abdom Radiol 45, 2698–2704 (2020). https://doi.org/10.1007/s00261-020-02508-4

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