Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging

• Published in: Radiology Vol. 286; no. 2; pp. 676 - 684
• Main Authors: Liu, Fang, Jang, Hyungseok, Kijowski, Richard, Bradshaw, Tyler, McMillan, Alan B
• Format: Journal Article
• Language: English
• Published: United States Radiological Society of North America 01.02.2018
• Subjects: Adult; Aged; Aged, 80 and over; Air; Bone and Bones - anatomy & histology; Brain - anatomy & histology; Connective Tissue - anatomy & histology; Feasibility Studies; Female; Humans; Image Processing, Computer-Assisted; Machine Learning - standards; Magnetic Resonance Imaging - methods; Male; Middle Aged; Multimodal Imaging - methods; Original Research; Photons; Positron-Emission Tomography - methods; Prospective Studies; Retrospective Studies; Stroke - pathology; Young Adult

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep le...