Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning.

Journal: Neurology
PMID: 40378378

Abstract

BACKGROUND AND OBJECTIVES: Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique providing fast and reliable tissue property measurements. The aim of this study was to develop an MRF-based deep-learning (DL) framework for whole-brain FCD detection.

Authors

  • Zheng Ding
    Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China.
  • Spencer Morris
    Epilepsy Center, Neurological Institute, Cleveland Clinic, OH.
  • Siyuan Hu
    Department of Biomedical Engineering, Case Western Reserve University, OH.
  • Ting-Yu Su
    Epilepsy Center, Neurological Institute, Cleveland Clinic, OH.
  • Joon Yul Choi
    Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
  • Ingmar Blumcke
    Institute of Neuropathology, University Hospitals, Erlangen, Germany.
  • XiaoFeng Wang
    Indiana University Bloomington.
  • Ken Sakaie
    Imaging Institute, Cleveland Clinic, OH; and.
  • Hiroatsu Murakami
    Epilepsy Center, Neurological Institute, Cleveland Clinic, OH.
  • Andreas V Alexopoulos
    Epilepsy Center, Neurological Institute, Cleveland Clinic, OH.
  • Stephen E Jones
    Imaging Institute, Cleveland Clinic, OH; and.
  • Imad M Najm
    Epilepsy Center, Neurological Institute, Cleveland Clinic, OH.
  • Dan Ma
  • Zhong Irene Wang
    Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA.

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