The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies
Open accessAbstract
Attenuation correction is an essential requirement for quantification of positron emission tomography (PET) data. In PET/CT acquisition systems, attenuation maps are derived from computed tomography (CT) images. However, in hybrid PET/MR scanners, magnetic resonance imaging (MRI) images do not directly provide a patient-specific attenuation map. The aim of the proposed work is to improve attenuation correction for PET/MR scanners by generating synthetic CTs and attenuation maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MRI/CT pairs, using a local image similarity measure. Results show significant improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an ultrashort-echo-time MRI sequence and to a simplified atlas-based method.
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A nonparametric method for automatic correction of intensity nonuniformity in MRI data
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Fast free-form deformation using graphics processing units
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Self-citationAutomatic anatomical brain MRI segmentation combining label propagation and decision fusion
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Martinez-Möller, A., Souvatzoglou, M., Delso, G., Bundschuh, R. A., Chefd'hotel, C., Ziegler, S. I., Navab, N., Schwaiger, M., & Nekolla, S. G. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.054726 · Cited by 712
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Self-citationNew, faster, image-based scatter correction for 3D PET
Watson, C. C. (2000) · IEEE Transactions on Nuclear Science · doi:10.1109/23.873020 · Cited by 519
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A Generative Model for Image Segmentation Based on Label Fusion
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STIR: software for tomographic image reconstruction release 2
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Self-citationMRI-Based Attenuation Correction for PET/MRI Using Ultrashort Echo Time Sequences
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Cited by
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks
Dar, S. U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., & Cukur, T. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2901750 · Cited by 608
NiftyNet: a deep-learning platform for medical imaging
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552
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Self-citationMultimodal MR Synthesis via Modality-Invariant Latent Representation
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A Synergistic Core for Human Brain Evolution and Cognition
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Holland, N., Fryer, T. D., O’Brien, J. T., Rowe, J. B., Menon, D. K., Bor, D., & Stamatakis, E. A. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01070-0 · Cited by 314
Metadata
- Authors
- Burgos, N., Cardoso, M. J., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Ahmed, R., Mahoney, C. J., Schott, J. M., Duncan, J. S., Atkinson, D., Arridge, S. R., Hutton, B. F., & Ourselin, S.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2014-12
- Volume
- 33
- Issue
- 12
- Pages
- 2332-2341
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2015897296
- Cited by
- 364
- References
- 37
- FWCI
- 23.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Burgos, N., Cardoso, M. J., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Ahmed, R., Mahoney, C. J., Schott, J. M., Duncan, J. S., Atkinson, D., Arridge, S. R., Hutton, B. F., & Ourselin, S. (2014). Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2014.2340135
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