Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.

Journal: European radiology
PMID: 36355196

Abstract

OBJECTIVES: To develop a deep learning-based harmonization framework, assessing whether it can improve performance of radiomics models given different kernels in different clinical tasks and additionally generalize to mitigate the effects of new/unobserved kernels on radiomics features.

Authors

  • Dongyang Du
    Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Wenbing Lv
    Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Jieqin Lv
    School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China.
  • Xiaohui Chen
    School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Hubing Wu
    PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Arman Rahmim
  • Lijun Lu
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.

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