Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.

Journal: Medical physics
PMID: 39514841

Abstract

PURPOSE: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.

Authors

  • Zach Eidex
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
  • Mojtaba Safari
    Département de Physique, de Génie Physique et D'optique, et Centre de Recherche sur le Cancer, Université Laval, Québec, Québec, Canada.
  • Jacob Wynne
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
  • Richard L J Qiu
    Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, United States of America.
  • Tonghe Wang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • David Viar-Hernandez
    Department of Radiation Oncology, Emory, University, Atlanta, Georgia, USA.
  • Hui-Kuo Shu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Hui Mao
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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