Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

Journal: AJR. American journal of roentgenology
Published Date:

Abstract

Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images with standard-dose iterative reconstruction (IR) images or explored application of DLR to low-tube-voltage scanning in children. The purpose of this study was to assess whether DLR can be used to reduce radiation dose while maintaining diagnostic image quality in comparison with hybrid IR (HIR) and model-based IR (MBIR) for low-tube-voltage pediatric CT. This retrospective study included children 6 years old or younger who underwent contrast-enhanced 80-kVp CT with a standard-dose or lower-dose protocol. Standard images were reconstructed with HIR, and lower-dose images were reconstructed with HIR, MBIR, and DLR. Size-specific dose estimate (SSDE) was calculated for both protocols. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Two radiologists independently evaluated noise magnitude, noise texture, streak artifact, edge sharpness, and overall quality. Interreader agreement was assessed, and mean values were calculated. To evaluate task-based object detection performance, a phantom was imaged with 80-kVp CT at six doses (SSDE, 0.6-5.3 mGy). Detectability index (') was calculated from the noise power spectrum and task-based transfer function. Reconstruction methods were compared. Sixty-five children (mean age, 25.0 ± 25.2 months) who underwent CT with standard- ( = 31) or lower-dose ( = 34) protocol were included. SSDE was 54% lower for the lower-dose than for the standard-dose group (1.9 ± 0.4 vs 4.1 ± 0.8 mGy). Lower-dose DLR and MBIR yielded lower image noise and higher SNR and CNR than standard-dose HIR ( < .05). Interobserver agreement on subjective features ranged from a kappa coefficient of 0.68 to 0.78. The readers subjectively scored noise texture, edge sharpness, and overall quality lower for lower-dose MBIR than for standard-dose HIR ( < .001), though higher for lower-dose DLR than for standard-dose HIR ( < .001). In the phantom, DLR provided higher ' than HIR and MBIR at each dose. Object detectability was greater for 2.0-mGy DLR than for 4.0-mGy HIR for low-contrast (3.67 vs 3.57) and high-contrast (1.20 vs 1.04) objects. Compared with IR algorithms, DLR results in substantial dose reduction with preserved or even improved image quality for low-tube-voltage pediatric CT. Use of DLR at 80 kVp allows greater dose reduction for pediatric CT than do current IR techniques.

Authors

  • Yasunori Nagayama
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.).
  • Makoto Goto
    From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.).
  • Daisuke Sakabe
    From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.).
  • Takafumi Emoto
    From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.).
  • Shinsuke Shigematsu
    Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan.
  • Seitaro Oda
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.).
  • Shota Tanoue
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
  • Masafumi Kidoh
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.).
  • Takeshi Nakaura
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.). Electronic address: kff00712@nifty.com.
  • Yoshinori Funama
    Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (Y.F.).
  • Ryutaro Uchimura
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
  • Sentaro Takada
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
  • Hidetaka Hayashi
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
  • Masahiro Hatemura
    Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan.
  • Toshinori Hirai
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.).