Toward Implementing an MRI-Based PET Attenuation-Correction Method for Neurologic Studies on the MR-PET Brain Prototype
By C. Catana, A. V. D. van der Kouwe, T. Benner, Christian Michel, M. Hamm, M. Fenchel, Bruce Fischl, Bruce R. Rosen, M. Schmand, A. Sorensen. Journal of Nuclear Medicine, 2010
372 citations recorded by the source corpus.
Subfields: Radiation, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/4d58afa032133c60e49fec1dd34714a4c6647dca
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Radiation
In Radiology, Nuclear Medicine and Imaging
- Sébastien Ourselin
- Pedro Cavaleiro Miranda
- Ricardo Salvador
- Giulio Ruffini
- Dinggang Shen
- M. Jorge Cardoso
- Simon Robert Arridge
- Marc Modat
Citations in the corpus
- Medical Image Synthesis with Deep Convolutional Adversarial Networks (2018, background): “In [10], the authors proposed to register an atlas to the new subject’s source image and then warp the corresponding target image of the atlas as the estimated target image.”
- Medical Image Synthesis with Context-Aware Generative Adversarial Networks (2016, methodology): “For each patch, the generator is used to predict the corresponding CT patch.”
- Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies (2014, background): “Catana et al. [8], Keereman et al. [9], and more recently Aitken et al. [10], used ultrashort-echo-time (UTE) MRI sequences to distinguish cortical bone, air, and soft tissue, which improves the accuracy of the results.”
- The electric field in the cortex during transcranial current stimulation (2013, background): “Skull segmentation may be improved by including ultra-short TE (UTE) MR data in the model (Catana et al., 2010; Keereman et al., 2010; Wang et al., 2010).”