Open AccessAttenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging
Author(s) -
Kuang Gong,
Paul Kyu Han,
Keith A. Johnson,
Georges El Fakhri,
Chao Ma,
Quanzheng Li
Publication year - 2020
Publication title -
european journal of nuclear medicine and molecular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.313
H-Index - 163
eISSN - 1619-7089
pISSN - 1619-7070
DOI - 10.1007/s00259-020-05061-w
Subject(s) - correction for attenuation , nuclear medicine , deep learning , standardized uptake value , magnetic resonance imaging , medicine , artificial intelligence , positron emission tomography , computer science , radiology
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.