Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Oct 27, 2020

Abstract

PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging.

Authors

  • Kuang Gong
  • Paul Kyu Han
    Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
  • Keith A Johnson
    Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
  • Georges El Fakhri
  • Chao Ma
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.

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