Your browser doesn't support javascript.
loading
Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection.
Noda, Yoshifumi; Kaga, Tetsuro; Kawai, Nobuyuki; Miyoshi, Toshiharu; Kawada, Hiroshi; Hyodo, Fuminori; Kambadakone, Avinash; Matsuo, Masayuki.
Affiliation
  • Noda Y; Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Kaga T; Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Kawai N; Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Miyoshi T; Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Kawada H; Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Hyodo F; Department of Radiology, Frontier Science for Imaging, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Kambadakone A; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Matsuo M; Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Br J Radiol ; 94(1121): 20201329, 2021 May 01.
Article in En | MEDLINE | ID: mdl-33571010
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Radiographic Image Interpretation, Computer-Assisted / Tomography, X-Ray Computed / Whole Body Imaging / Deep Learning / Neoplasms Type of study: Diagnostic_studies / Observational_studies / Qualitative_research Limits: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Language: En Journal: Br J Radiol Year: 2021 Document type: Article Affiliation country: Japón

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Radiographic Image Interpretation, Computer-Assisted / Tomography, X-Ray Computed / Whole Body Imaging / Deep Learning / Neoplasms Type of study: Diagnostic_studies / Observational_studies / Qualitative_research Limits: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Language: En Journal: Br J Radiol Year: 2021 Document type: Article Affiliation country: Japón