Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.

Leynes, Andrew P; Ahn, Sangtae; Wangerin, Kristen A; Kaushik, Sandeep S; Wiesinger, Florian; Hope, Thomas A; Larson, Peder E Z

Leynes, Andrew P; Ahn, Sangtae; Wangerin, Kristen A; Kaushik, Sandeep S; Wiesinger, Florian; Hope, Thomas A; Larson, Peder E Z.

Afiliación

Leynes AP; Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA.
Ahn S; UC Berkeley-UC San Francisco Joint Graduate Program in Bioengineering, University of California at Berkeley, Berkeley, CA 94720 USA.
Wangerin KA; Biology and Physics Department, GE Research, Niskayuna, NY 12309 USA.
Kaushik SS; PET/MR, GE Healthcare, Waukesha, WI 53188 USA.
Wiesinger F; MR Applications Science Laboratory Europe, GE Healthcare, 80807 Munich, Germany.
Hope TA; Department of Computer Science, Technical University of Munich, 80333 Munich, Germany.
Larson PEZ; Department of Quantitative Biomedicine, University of Zurich, 8057 Zurich, Switzerland.

IEEE Trans Radiat Plasma Med Sci ; 6(6): 678-689, 2022 Jul.

Article en En | MEDLINE | ID: mdl-38223528

Palabras clave

Bayesian deep learning; deep learning; magnetic resonance-based attenuation correction (MRAC); maximum-likelihood estimation of activity and attenuation (MLAA); synthetic CT

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IEEE Trans Radiat Plasma Med Sci Año: 2022 Tipo del documento: Article