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. 2021 May 1;94(1121):20201329.
doi: 10.1259/bjr.20201329. Epub 2021 Feb 22.

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

Yoshifumi Noda  1 , Tetsuro Kaga  1 , Nobuyuki Kawai  1 , Toshiharu Miyoshi  1 , Hiroshi Kawada  1 , Fuminori Hyodo  2 , Avinash Kambadakone  3 , Masayuki Matsuo  1
Affiliations

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

Yoshifumi Noda et al. Br J Radiol. .

Abstract

Objectives: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR).

Methods: The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDIvol) and dose-length product (DLP) were compared between SD and LD scans.

Results: The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28-0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDIvol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001).

Conclusion: LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR.

Advances in knowledge: Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

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Conflict of interest statement

Conflict of Interest: AK: Grant support for research activities from Philips and GE Healthcare. Other authors report no relevant disclosures or conflicts of interest.

Figures

Figure 1.
Figure 1.
Flow chart of included and excluded patients.
Figure 2.
Figure 2.
A 72-year-old male who underwent surgery for esophageal cancer. (a) Axial standard-dose portal venous phase image, (b) low-dose portal venous phase image reconstructed with ASiR-V of 40%, and (c) low-dose portal venous phase image reconstructed with TFI-H show enlarged mediastinal lymph node (arrow).
Figure 3.
Figure 3.
A 46-year-old male who underwent surgery for rectal cancer. (a) Axial standard-dose portal venous phase image, (b) low-dose portal venous phase image reconstructed with ASiR-V of 40%, and (c) low-dose portal venous phase image reconstructed with TFI-H show a tiny liver metastasis (arrow) and a liver cyst (arrowhead).
See this image and copyright information in PMC

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References

    1. Guglielmo FF, Anupindi SA, Fletcher JG, Al-Hawary MM, Dillman JR, Grand DJ, et al. . Small bowel Crohn disease at CT and Mr Enterography: imaging atlas and glossary of terms. Radiographics 2020; 40: 354–75. doi: 10.1148/rg.2020190091 - DOI - PubMed
    1. Kambadakone AR, Eisner BH, Catalano OA, Sahani DV. New and evolving concepts in the imaging and management of urolithiasis: urologists' perspective. Radiographics 2010; 30: 603–23. doi: 10.1148/rg.303095146 - DOI - PubMed
    1. Pickhardt PJ, Graffy PM, Said A, Jones D, Welsh B, Zea R, et al. . Multiparametric CT for noninvasive staging of hepatitis C virus-related liver fibrosis: correlation with the histopathologic fibrosis score. AJR Am J Roentgenol 2019; 212: 547–53. doi: 10.2214/AJR.18.20284 - DOI - PMC - PubMed
    1. Wulff AM, Bolte H, Fischer S, Freitag-Wolf S, Soza G, Tietjen C, et al. . Lung, liver and lymph node metastases in follow-up MSCT: comprehensive volumetric assessment of lesion size changes. Rofo 2012; 184: 820–8. doi: 10.1055/s-0032-1312860 - DOI - PubMed
    1. Tezcan D, Türkvatan A, Türkoğlu MA, Bostancı EB, Sakaoğullları Z. Preoperative staging of colorectal cancer: accuracy of single portal venous phase multidetector computed tomography. Clin Imaging 2013; 37: 1048–53. doi: 10.1016/j.clinimag.2013.08.003 - DOI - PubMed

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