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Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging.

, , , , and . MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 3-11. Springer, (2018)

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Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning., , , , , , , , , and . CoRR, (2020)Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions., , , , , , and . J. Imaging, 4 (1): 13 (2018)Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees., , , , , , , and . MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 83-90. Springer, (2018)Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT., , , , , , , , , and . CoRR, (2019)Comparison of Different Approaches for Measuring Tibial Cartilage Thickness., , , , , , , , , and . J. Integr. Bioinform., (2017)Fourier-based Reduction of Directed Streak Artifacts in Cone-Beam CT., , , and . Bildverarbeitung für die Medizin, page 127-132. Springer Vieweg, (2018)Abstract: Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee., , , , , , and . Bildverarbeitung für die Medizin, page 336. Springer, (2021)SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation., , , , , , , , , and 2 other author(s). NeurIPS Datasets and Benchmarks, (2021)Inertial Measurements for Motion Compensation in Weight-Bearing Cone-Beam CT of the Knee., , , , , , and . MICCAI (3), volume 12263 of Lecture Notes in Computer Science, page 14-23. Springer, (2020)Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements., , , , , , and . IEEE Trans. Biomed. Eng., 69 (5): 1608-1619 (2022)