Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations in quantification of knee cartilage using learning-based methods.

Journal: Magnetic resonance in medicine
Published Date: Aug 5, 2025

Abstract

PURPOSE: To propose and evaluate an accelerated quantification method that combines -weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment.

Authors

  • Junru Zhong
    CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Chaoxing Huang
    Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Ziqiang Yu
    CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Fan Xiao
    School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China.
  • Thierry Blu
    Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
  • Siyue Li
    Chongqing Institute of Green and Intelligent Technology (CIGIT), Chinese Academy of Sciences (CAS), 266, Fangzheng Avenue, Shuitu High-tech Park, Beibei, Chongqing, 400714, People's Republic of China. syli2006@163.com.
  • Tim-Yun Michael Ong
    Department of Orthopaedics & Traumatology, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Ki-Wai Kevin Ho
    Department of Orthopaedics & Traumatology, The Chinese University of Hong Kong Medical Centre, Hong Kong SAR, China.
  • Queenie Chan
    Philips Healthcare, Hong Kong SAR, People's Republic of China.
  • James F Griffith
    CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Weitian Chen
    Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.

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