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Felix Nensa

Showing results (11-20 of 199) with videos related to

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Diagnostic and Interventional Radiology (Ankara, Turkey)|July 11, 2014
Clinical applications of PET/MRI: current status and future perspectivesFelix Nensa, Karsten Beiderwellen, Philipp Heusch, et al.
Plos One|March 26, 2019
Impact of improved attenuation correction on 18F-FDG PET/MR hybrid imaging of the heartMaike E Lindemann, Felix Nensa, Harald H Quick
European Journal of Radiology|September 1, 2024
Beyond accuracy: Reproducibility must lead AI advances in radiologyFelix Nensa, Daniel Pinto Dos Santos, Mathias Dietzel
European Heart Journal|March 1, 2014
Multiparametric assessment of myocarditis using simultaneous positron emission tomography/magnetic resonance imagingFelix Nensa, Thorsten D Poeppel, Peter Krings, et al.
Neuroimage|February 16, 2026
MRI-based Radiomics and volumetrics for predicting the onset of Alzheimer's Disease with explainable machine learningLouise Bloch, Katarzyna Borys, Felix Nensa, et al.
Journal of Clinical Medicine|January 22, 2021
Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification-A Deep Learning Based Approach Using Fully Automated Body Composition AnalysisLennard Kroll, Kai Nassenstein, Markus Jochims, et al.
European Heart Journal|December 25, 2014
Diagnosis and treatment response evaluation of cardiac sarcoidosis using positron emission tomography/magnetic resonance imagingFelix Nensa, Ercan Tezgah, Thorsten Poeppel, et al.
Langenbeck'S Archives of Surgery|April 12, 2019
Surgery for adrenal angiomyelolipoma: an individualized conceptFrank Weber, Azim Shaibekov, Felix Nensa, et al.
Journal of Thoracic Imaging|December 11, 2013
Expert Opinion: which cardiothoracic imaging applications of PET/CT are most likely to be replaced by PET/MRI?Phillip M Boiselle, Felix Nensa, Yoshiharu Ohno, et al.
European Radiology|September 18, 2020
Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networksSven Koitka, Lennard Kroll, Eugen Malamutmann, et al.
Pageof 20

Showing results (11-20 of 199) with videos related to

Sort By:
Pageof 20
Diagnostic and Interventional Radiology (Ankara, Turkey)|July 11, 2014
Clinical applications of PET/MRI: current status and future perspectivesFelix Nensa, Karsten Beiderwellen, Philipp Heusch, et al.
Plos One|March 26, 2019
Impact of improved attenuation correction on 18F-FDG PET/MR hybrid imaging of the heartMaike E Lindemann, Felix Nensa, Harald H Quick
European Journal of Radiology|September 1, 2024
Beyond accuracy: Reproducibility must lead AI advances in radiologyFelix Nensa, Daniel Pinto Dos Santos, Mathias Dietzel
European Heart Journal|March 1, 2014
Multiparametric assessment of myocarditis using simultaneous positron emission tomography/magnetic resonance imagingFelix Nensa, Thorsten D Poeppel, Peter Krings, et al.
Neuroimage|February 16, 2026
MRI-based Radiomics and volumetrics for predicting the onset of Alzheimer's Disease with explainable machine learningLouise Bloch, Katarzyna Borys, Felix Nensa, et al.
Journal of Clinical Medicine|January 22, 2021
Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification-A Deep Learning Based Approach Using Fully Automated Body Composition AnalysisLennard Kroll, Kai Nassenstein, Markus Jochims, et al.
European Heart Journal|December 25, 2014
Diagnosis and treatment response evaluation of cardiac sarcoidosis using positron emission tomography/magnetic resonance imagingFelix Nensa, Ercan Tezgah, Thorsten Poeppel, et al.
Langenbeck'S Archives of Surgery|April 12, 2019
Surgery for adrenal angiomyelolipoma: an individualized conceptFrank Weber, Azim Shaibekov, Felix Nensa, et al.
Journal of Thoracic Imaging|December 11, 2013
Expert Opinion: which cardiothoracic imaging applications of PET/CT are most likely to be replaced by PET/MRI?Phillip M Boiselle, Felix Nensa, Yoshiharu Ohno, et al.
European Radiology|September 18, 2020
Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networksSven Koitka, Lennard Kroll, Eugen Malamutmann, et al.
Pageof 20