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Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|
December 15, 2015
A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes
Mathew R Divine, Prateek Katiyar, Ursula Kohlhofer, et al.
Molecular Imaging and Biology
|
October 14, 2016
A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation
Prateek Katiyar, Mathew R Divine, Ursula Kohlhofer, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|
November 5, 2016
Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic <sup>18</sup>F-FDG PET: A Complement to the Standard Compartmental Modeling Approach
Prateek Katiyar, Mathew R Divine, Ursula Kohlhofer, et al.
Seminars in Nuclear Medicine
|
June 2, 2018
PET/MRI Hybrid Systems
Julia G Mannheim, Andreas M Schmid, Johannes Schwenck, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|
February 25, 2018
Impact of the Arterial Input Function Recording Method on Kinetic Parameters in Small-Animal PET
Hanna Napieczynska, Armin Kolb, Prateek Katiyar, et al.
Nature Biomedical Engineering
|
June 5, 2023
Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data
Prateek Katiyar, Johannes Schwenck, Leonie Frauenfeld, et al.
Theranostics
|
January 18, 2021
Machine learning identifies stroke features between species
Salvador Castaneda-Vega, Prateek Katiyar, Francesca Russo, et al.
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Search research articles
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Showing results (1-10 of 7) with videos related to
Sort By:
Page
of 1
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|
December 15, 2015
A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes
Mathew R Divine, Prateek Katiyar, Ursula Kohlhofer, et al.
Molecular Imaging and Biology
|
October 14, 2016
A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation
Prateek Katiyar, Mathew R Divine, Ursula Kohlhofer, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|
November 5, 2016
Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic <sup>18</sup>F-FDG PET: A Complement to the Standard Compartmental Modeling Approach
Prateek Katiyar, Mathew R Divine, Ursula Kohlhofer, et al.
Seminars in Nuclear Medicine
|
June 2, 2018
PET/MRI Hybrid Systems
Julia G Mannheim, Andreas M Schmid, Johannes Schwenck, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|
February 25, 2018
Impact of the Arterial Input Function Recording Method on Kinetic Parameters in Small-Animal PET
Hanna Napieczynska, Armin Kolb, Prateek Katiyar, et al.
Nature Biomedical Engineering
|
June 5, 2023
Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data
Prateek Katiyar, Johannes Schwenck, Leonie Frauenfeld, et al.
Theranostics
|
January 18, 2021
Machine learning identifies stroke features between species
Salvador Castaneda-Vega, Prateek Katiyar, Francesca Russo, et al.
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of 1