Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 30, 2023

Abstract

BACKGROUND: White matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted with WMHs lacks comprehensive investigation. This study developed a WMHs risk prediction model to evaluate WHMs according to Fazekas scales, and to locate potential regions with high risks across the entire brain.

Authors

  • Si Mu
    College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, Shandong, 271000, China.
  • Weizhao Lu
    Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong, 271000, China.
  • Guanghui Yu
    Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong, 271000, China.
  • Lei Zheng
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China.
  • Jianfeng Qiu
    School of Radiology, Shandong First Medical University & Shandong Academy of Medicine Sciences, Tai'an, Shandong, 271000, China; Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China. Electronic address: jfqiu100@163.com.

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