Deep learning Mr imaging–based attenuation correction for PeT/Mr imaging 1
By Fang Liu, Hyungseok Jang, Richard Kijowski, Tyler J. Bradshaw, Alan B. McMillan. 2017
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Arvid Lundervold
- Berkman Sahiner
- Lubomir M. Hadjiiski
- Rikiya Yamashita
- Aria Pezeshk
- Xiaosong Wang
- Mizuho Nishio
- Ronald M. Summers
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [16,20,29,31,39,89,136,139,161,272,284], all published in 2018).”
- Deep learning in medical imaging and radiation therapy. (2018, methodology): “267 Liu et al. 258 trained a deep network (deep MRAC) to generate sCT from T1-weighted MRI and compared deep MRAC with Dixon MRAC.”
- Deep learning with convolutional neural network in radiology (2018, methodology): “reported that significantly lower PET reconstruction errors were achieved with deep learning based MR attenuation correction compared with Dixon‐based soft‐tissue and air segmentation and anatomic CT‐based template registration [62].”
- Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction (2018, background): “Several other deep-learning methods have previously been described for head ( 21,36 ) and, very recently, pelvis AC ( 26 ).”
- Convolutional neural networks: an overview and application in radiology (2018, background): “Needless to say, there has been a surge of interest in the potential of CNN among radiology researchers, and several studies have already been published in areas such as lesion detection [7], classification [8], segmentation [9], image reconstruction [10, 11], and natural language processing [12].”
- Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network (2018, methodology): “A CNN model depending on T1-weighted MRI data was shown to yield accurate PET reconstructions, although reconstruction analysis was confined to a small cohort and analyzed only short acquisitions of a single radiotracer (24).”