Deep Learning MR Imaging–based Attenuation Correction for PET/MR Imaging
Open accessAbstract
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.
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Deep learning is applied in PET-MRI attenuation correction.
“Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90, 91], in radiomics [92, 93]”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [90]
The tremendous amounts of high-impact publications in top journals dealing with deep learning in medical imaging illustrate the value of the methods.
“This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [17, 21, 30, 32, 40, 90, 162, 137, 140, 273, 285], all published in 2018).”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [90]
The VGG16 network has proven to be an effective and efficient network of feature extraction in many medical image applications, including tissue segmentation and image contrast conversion.
“More recently, the VGG16 network has proven to be an effective and efficient network of feature extraction in many medical image applications, including tissue segmentation (14,24–26) and image contrast conversion (27,28).”
From Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. (2018) · cites this paper as [27]
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- Authors
- Liu, F., Jang, H., Kijowski, R., Bradshaw, T., & McMillan, A. B.
- Affiliations
- University of Wisconsin–Madison (US)
- Venue
- Radiology
- Published
- 2018-02
- Volume
- 286
- Issue
- 2
- Pages
- 676-684
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC5790303
- OpenAlex
- W2754132686
- Cited by
- 412
- References
- 3
- Citations by year
- 2026: 12 · 2025: 32 · 2024: 36 · 2023: 33 · 2022: 58 · 2021: 80 · 2020: 69 · 2019: 54 · 2018: 35 · 2017: 1 · 2014: 1
- FWCI
- 33.37
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Radiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01-AR068373)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- MR-based attenuation correction, PET/MR imaging, pseudo-CT generation, convolutional autoencoder, brain PET, tissue segmentation, Dixon MRI, CT attenuation correction
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Air, Bone and Bones, Brain, Connective Tissue, Feasibility Studies, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Prospective Studies, Retrospective Studies, Photons, Stroke, Positron-Emission Tomography, Young Adult, Multimodal Imaging
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