2018
DOI: 10.1148/radiol.2017170700
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Abstract: 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 coregister… Show more

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Cited by 325 publications
(258 citation statements)
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“…The DL framework for lung segmentation (Fig. a) used a 2D convolutional encoder‐decoder (CED) architecture, which has been successfully applied for cartilage and brain tissue segmentation . The encoder network uses the same 13 Visual Geometry Group 16 convolutional layers and the decoder uses a mirrored structure of the encoder network with max‐pooling replaced by an upsampling process.…”
Section: Methodsmentioning
confidence: 99%
“…The DL framework for lung segmentation (Fig. a) used a 2D convolutional encoder‐decoder (CED) architecture, which has been successfully applied for cartilage and brain tissue segmentation . The encoder network uses the same 13 Visual Geometry Group 16 convolutional layers and the decoder uses a mirrored structure of the encoder network with max‐pooling replaced by an upsampling process.…”
Section: Methodsmentioning
confidence: 99%
“…Proof of principle was recently demonstrated for brain MR imaging attenuation correction, with performance superior to that of competing techniques. 20 Another study demonstrated a similar use of MR imaging to create synthetic CT for radiation therapy. 21 In clinical trials, situations arise in which patients may not be able to undergo a certain diagnostic technique, such as patients with MR imaging-incompatible implants.…”
Section: Image Transformationmentioning
confidence: 99%
“…Furthermore, atlas-based methods need to account for inter-individual variability to achieve high accuracy and are computationally demanding, which limits their application in the clinical environment.Recently, machine learning methods have been developed to generate pseudo-CT AC maps(Leynes et al, 2018;Liu, Jang, Kijowski, Bradshaw, & McMillan, 2018). Furthermore, atlas-based methods need to account for inter-individual variability to achieve high accuracy and are computationally demanding, which limits their application in the clinical environment.Recently, machine learning methods have been developed to generate pseudo-CT AC maps(Leynes et al, 2018;Liu, Jang, Kijowski, Bradshaw, & McMillan, 2018).…”
mentioning
confidence: 99%