Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

Journal: European radiology
Published Date: Mar 15, 2024

Abstract

OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods.

Authors

  • Liang Jiang
    College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China. Electronic address: fredjiang240@126.com.
  • Jiarui Sun
    State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China.
  • Yajing Wang
    Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Haodi Yang
    Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China.
  • Yu-Chen Chen
    Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Mingyang Peng
    Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China.
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Xindao Yin
    Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.

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