Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.

Journal: Radiology
Published Date: Jan 11, 2022

Abstract

Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act-compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP ( < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) ( < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively ( = .01). Lower lesion confidence was reported with a reduced dose ( < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022

Authors

  • Corey T Jensen
    Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009.
  • Shiva Gupta
    From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S., V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University Medical Center, Durham, NC (E.S.).
  • Mohammed M Saleh
    From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S., V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University Medical Center, Durham, NC (E.S.).
  • Xinming Liu
    Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX.
  • Vincenzo K Wong
    From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S., V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University Medical Center, Durham, NC (E.S.).
  • Usama Salem
    From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S., V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University Medical Center, Durham, NC (E.S.).
  • Wei Qiao
    Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX.
  • Ehsan Samei
    Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.
  • Nicolaus A Wagner-Bartak
    Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009.

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