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
• DOI: 10.1148/radiol.2017170700
• ISSN: 1527-1315; 0033-8419

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 learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared with current MR imaging-based AC approaches. RSNA, 2017 Online supplemental material is available for this article.