Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging.

Journal: Magnetic resonance imaging
PMID: 39675686

Abstract

PURPOSE: The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy.

Authors

  • Phillip Martin
    Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, United States of America; Department of Medical Imaging, University of Arizona, Tucson, AZ 85724, United States of America.
  • Maria Altbach
    Department of Biomedical Engineering, University of Arizona, Tucson, AZ 85724, United States of America; Department of Medical Imaging, University of Arizona, Tucson, AZ 85724, United States of America.
  • Ali Bilgin
    Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, United States of America; Department of Biomedical Engineering, University of Arizona, Tucson, AZ 85724, United States of America; Department of Medical Imaging, University of Arizona, Tucson, AZ 85724, United States of America; Program in Applied Mathematics, University of Arizona, Tucson, AZ 85724, United States of America. Electronic address: bilgin@arizona.edu.

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