Deep Learning MR Imaging–based Attenuation Correction for PET/MR Imaging
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- 1 February 2018
- journal article
- research article
- Published by Radiological Society of North America (RSNA) in Radiology
- Vol. 286 (2), 676-684
- https://doi.org/10.1148/radiol.2017170700
Abstract
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. 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. 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). 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.Keywords
Funding Information
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01-AR068373)
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