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Thomas Pinetz

Showing results (1-10 of 8) with videos related to

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IEEE Transactions on Bio-Medical Engineering|January 19, 2026
PINN-EM: Physics-Guided Disease Progression Model of Geographic AtrophyDmitrii Lachinov, Thomas Pinetz, Hrvoje Bogunovic
Investigative Radiology|February 23, 2023
Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in NeuroradiologyRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Investigative Radiology|July 29, 2024
Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal ExtractionRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Investigative Radiology|February 3, 2023
Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences With Original Full-Dose Images Despite Excellent Quantitative PerformanceRobert Haase, Thomas Pinetz, Zeynep Bendella, et al.
Radiology. Artificial Intelligence|October 16, 2024
Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple SclerosisGianluca Brugnara, Chandrakanth Jayachandran Preetha, Katerina Deike, et al.
Investigative Radiology|December 17, 2024
Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRIRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Investigative Radiology|February 17, 2025
Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRIRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Nature Communications|August 15, 2023
Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic strokeGianluca Brugnara, Michael Baumgartner, Edwin David Scholze, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
IEEE Transactions on Bio-Medical Engineering|January 19, 2026
PINN-EM: Physics-Guided Disease Progression Model of Geographic AtrophyDmitrii Lachinov, Thomas Pinetz, Hrvoje Bogunovic
Investigative Radiology|February 23, 2023
Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in NeuroradiologyRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Investigative Radiology|July 29, 2024
Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal ExtractionRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Investigative Radiology|February 3, 2023
Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences With Original Full-Dose Images Despite Excellent Quantitative PerformanceRobert Haase, Thomas Pinetz, Zeynep Bendella, et al.
Radiology. Artificial Intelligence|October 16, 2024
Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple SclerosisGianluca Brugnara, Chandrakanth Jayachandran Preetha, Katerina Deike, et al.
Investigative Radiology|December 17, 2024
Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRIRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Investigative Radiology|February 17, 2025
Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRIRobert Haase, Thomas Pinetz, Erich Kobler, et al.
Nature Communications|August 15, 2023
Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic strokeGianluca Brugnara, Michael Baumgartner, Edwin David Scholze, et al.
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