CEST and AREX data processing based on deep neural network : application to image Alzheimer’s disease at 3T

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review

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Author(s)

  • Jianpan Huang
  • Kai-Hei Tse
  • Gerald W.Y. Cheng
  • Lin Chen
  • Jiadi Xu

Detail(s)

Original languageEnglish
Publication statusPublished - May 2021

Conference

Title2021 International Society for Magnetic Resonance in Medicine (ISMRM) & Society for MR Radiographers & Technologists (SMRT) Annual Meeting
LocationOnline
Period15 - 20 May 2021

Abstract

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a promising molecular imaging technology. Apparent exchange-dependent relaxation (AREX) provides CEST contrast with less influence of T1. Here, deep neural network based CEST/AREX analysis methods (CESTNet/AREXNet) were applied to analyze the CEST data of normal and AD mouse brains at 3T. Significant lower amide proton transfer/magnetization transfer (APT/MT) signals related to amyloid β-peptide (Aβ) plaque depositions, which were validated by immunohistochemistry results, were detected in Alzheimer’s disease (AD) mouse brains compared to age-matched wild type (WT) mouse brains. The well-established CESTNet/AREXNet have great potential to facilitate AD identification at 3T.

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.

Citation Format(s)

CEST and AREX data processing based on deep neural network : application to image Alzheimer’s disease at 3T. / Huang, Jianpan; Lai, Joseph H. C.; Tse, Kai-Hei; Cheng, Gerald W.Y.; Han, Xiongqi; Liu, Yang; Chen, Zilin; Chen, Lin; Xu, Jiadi; Chan, Kannie W. Y.

2021. Paper presented at 2021 International Society for Magnetic Resonance in Medicine (ISMRM) & Society for MR Radiographers & Technologists (SMRT) Annual Meeting, .

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review