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Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency.

, , , , , , , , , and . MLCN@MICCAI, volume 13596 of Lecture Notes in Computer Science, page 83-92. Springer, (2022)

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Motion Compensation Strategies in Magnetic Resonance Imaging, , , , , and . Crit Rev Biomed Eng, (2012)Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling., , , , , and . MICCAI (3), volume 10435 of Lecture Notes in Computer Science, page 142-149. Springer, (2017)Accuracy and Precision of Head Motion Information in Multi-Channel Free Induction Decay Navigators for Magnetic Resonance Imaging., , , , , , and . IEEE Trans. Medical Imaging, 34 (9): 1879-1889 (2015)Shallow vs Deep Learning Architectures for White Matter Lesion Segmentation in the Early Stages of Multiple Sclerosis., , , , , , and . BrainLes@MICCAI (1), volume 11383 of Lecture Notes in Computer Science, page 142-151. Springer, (2018)Evaluating reproducibility and subject-specificity of microstructure-informed connectivity., , , , , , , , , and 8 other author(s). NeuroImage, (2022)T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions., , , , , , , , , and 5 other author(s). MICCAI (2), volume 12262 of Lecture Notes in Computer Science, page 114-124. Springer, (2020)Multivariate and predictive modelling of neural variability in mild cognitive impairment., , , , , , , and . PRNI, page 1-4. IEEE, (2018)Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency., , , , , , , , , and . MLCN@MICCAI, volume 13596 of Lecture Notes in Computer Science, page 83-92. Springer, (2022)Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction., , , , , , , , , and 6 other author(s). UNSURE/PIPPI@MICCAI, volume 12959 of Lecture Notes in Computer Science, page 157-167. Springer, (2021)Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency., , , , , and . CoRR, (2023)