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Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging.

C. Poynton, M. Jenkinson, E. Adalsteinsson, E. Sullivan, A. Pfefferbaum, and W. III. IEEE Trans. Medical Imaging, 34 (1): 339-353 (2015)

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Fetal Pose Estimation in Volumetric MRI Using a 3D Convolution Neural Network.J. Xu, M. Zhang, E. Turk, L. Zhang, P. Grant, K. Ying, P. Golland, and E. Adalsteinsson. MICCAI (4), volume 11767 of Lecture Notes in Computer Science, page 403-410. Springer, (2019)Temporal Registration in Application to In-utero MRI Time Series.R. Liao, E. Turk, M. Zhang, J. Luo, E. Adalsteinsson, P. Grant, and P. Golland. CoRR, (2019)3D Fetal Pose Estimation with Adaptive Variance and Conditional Generative Adversarial Network.J. Xu, M. Zhang, E. Turk, P. Grant, P. Golland, and E. Adalsteinsson. ASMUS/PIPPI@MICCAI, volume 12437 of Lecture Notes in Computer Science, page 201-210. Springer, (2020)Deformed2Self: Self-supervised Denoising for Dynamic Medical Imaging.J. Xu, and E. Adalsteinsson. MICCAI (2), volume 12902 of Lecture Notes in Computer Science, page 25-35. Springer, (2021)Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging.E. Kuzmina, A. Razumov, O. Rogov, E. Adalsteinsson, J. White, and D. Dylov. MICCAI (6), volume 13436 of Lecture Notes in Computer Science, page 365-375. Springer, (2022)Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction.D. Weller, J. Polimeni, L. Grady, L. Wald, E. Adalsteinsson, and V. Goyal. IEEE Trans. Medical Imaging, 32 (7): 1325-1335 (2013)Combined compressed sensing and parallel mri compared for uniform and random cartesian undersampling of K-space.D. Weller, J. Polimeni, L. Grady, L. Wald, E. Adalsteinsson, and V. Goyal. ICASSP, page 553-556. IEEE, (2011)Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging.C. Poynton, M. Jenkinson, E. Adalsteinsson, E. Sullivan, A. Pfefferbaum, and W. III. IEEE Trans. Medical Imaging, 34 (1): 339-353 (2015)SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI.J. Xu, D. Moyer, P. Grant, P. Golland, J. Iglesias, and E. Adalsteinsson. MICCAI (6), volume 13436 of Lecture Notes in Computer Science, page 3-13. Springer, (2022)Enhanced Detection of Fetal Pose in 3D MRI by Deep Reinforcement Learning with Physical Structure Priors on Anatomy.M. Zhang, J. Xu, E. Turk, P. Grant, P. Golland, and E. Adalsteinsson. MICCAI (6), volume 12266 of Lecture Notes in Computer Science, page 396-405. Springer, (2020)

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Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging.

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Disambiguation of "Adalsteinsson, Elfar"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging.

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Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging.