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Daniel Wessling

Showing results (1-10 of 16) with videos related to

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Diagnostics (Basel, Switzerland)|October 16, 2024
Factors Influencing Background Parenchymal Enhancement in Contrast-Enhanced Mammography ImagesDaniel Wessling, Simon Männlin, Ricarda Schwarz, et al.
Tomography (Ann Arbor, Mich.)|July 27, 2022
Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) SequenceDaniel Wessling, Judith Herrmann, Saif Afat, et al.
European Journal of Radiology|July 23, 2023
Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRIDaniel Wessling, Sebastian Gassenmaier, Susann-Cathrin Olthof, et al.
La Radiologia Medica|January 7, 2023
Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chestSimon Maennlin, Daniel Wessling, Judith Herrmann, et al.
Cancers|February 11, 2023
Thin-Slice Prostate MRI Enabled by Deep Learning Image ReconstructionSebastian Gassenmaier, Verena Warm, Dominik Nickel, et al.
Insights Into Imaging|March 21, 2022
Single-centre survival analysis over 10 years after MR-guided radiofrequency ablation of liver metastases from different tumour entitiesSusann-Cathrin Olthof, Daniel Wessling, Moritz T Winkelmann, et al.
Investigative Radiology|September 12, 2021
Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image QualitySaif Afat, Daniel Wessling, Carmen Afat, et al.
Diagnostics (Basel, Switzerland)|August 29, 2024
Optimizing Image Quality with High-Resolution, Deep-Learning-Based Diffusion-Weighted Imaging in Breast Cancer Patients at 1.5 TSusann-Cathrin Olthof, Elisabeth Weiland, Thomas Benkert, et al.
Frontiers in Radiology|May 15, 2026
Deep learning-based denoising in cardiac CT: effects on image quality, calcium scoring interchangeability, and reporting workflowDaniel Wessling, Jan Magnus, Jan M Brendel, et al.
Academic Radiology|April 26, 2022
Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 TJudith Herrmann, Daniel Wessling, Dominik Nickel, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
Diagnostics (Basel, Switzerland)|October 16, 2024
Factors Influencing Background Parenchymal Enhancement in Contrast-Enhanced Mammography ImagesDaniel Wessling, Simon Männlin, Ricarda Schwarz, et al.
Tomography (Ann Arbor, Mich.)|July 27, 2022
Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) SequenceDaniel Wessling, Judith Herrmann, Saif Afat, et al.
European Journal of Radiology|July 23, 2023
Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRIDaniel Wessling, Sebastian Gassenmaier, Susann-Cathrin Olthof, et al.
La Radiologia Medica|January 7, 2023
Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chestSimon Maennlin, Daniel Wessling, Judith Herrmann, et al.
Cancers|February 11, 2023
Thin-Slice Prostate MRI Enabled by Deep Learning Image ReconstructionSebastian Gassenmaier, Verena Warm, Dominik Nickel, et al.
Insights Into Imaging|March 21, 2022
Single-centre survival analysis over 10 years after MR-guided radiofrequency ablation of liver metastases from different tumour entitiesSusann-Cathrin Olthof, Daniel Wessling, Moritz T Winkelmann, et al.
Investigative Radiology|September 12, 2021
Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image QualitySaif Afat, Daniel Wessling, Carmen Afat, et al.
Diagnostics (Basel, Switzerland)|August 29, 2024
Optimizing Image Quality with High-Resolution, Deep-Learning-Based Diffusion-Weighted Imaging in Breast Cancer Patients at 1.5 TSusann-Cathrin Olthof, Elisabeth Weiland, Thomas Benkert, et al.
Frontiers in Radiology|May 15, 2026
Deep learning-based denoising in cardiac CT: effects on image quality, calcium scoring interchangeability, and reporting workflowDaniel Wessling, Jan Magnus, Jan M Brendel, et al.
Academic Radiology|April 26, 2022
Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 TJudith Herrmann, Daniel Wessling, Dominik Nickel, et al.
Pageof 2