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Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e.g., MP-RAGE).
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Parameter relations for the Shinnar-Le Roux selective excitation pulse design algorithm
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AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
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Modeling brain deformations in alzheimer disease by fluid registration of serial 3D MR images
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Automated model-based tissue classification of mr images of the brain
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A unified statistical approach to deformation-based morphometry
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Imaging of onset and progression of Alzheimer’s disease with voxel-compression mapping of serial magnetic resonance images
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Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
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Hippocampal volume as an index of Alzheimer neuropathology: findings from the Nun Study
Gosche, K., Mortimer, J., Smith, C., Markesbery, W., & Snowdon, D. (2002) · 2002
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Improved optimization for the robust and accurate linear registration and motion correction of brain images
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · 2002
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Superresolution in MRI?
Scheffler, K. (2002) · 2002
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Super-resolution image reconstruction: a technical overview
Park, S. C., Park, M. K., & Kang, M. G. (2003) · 2003
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Unbiased diffeomorphic atlas construction for computational anatomy
Joshi, S., Davis, B., Jomier, M., & Gerig, G. (2004) · 2004
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Characterizing changes in MR images with color-coded jacobians
Riddle, W. R., Li, R., Fitzpatrick, J. M., DonLevy, S. C., Dawant, B. M., & Price, R. R. (2004) · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · 2004
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Unified segmentation
Ashburner, J., & Friston, K. J. (2005) · 2005
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Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy
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A log-euclidean framework for statistics on diffeomorphisms
Arsigny, V., Commowick, O., Pennec, X., & Ayache, N. (2006) · 2006
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Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer
Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R. et al. (2006) · 2006
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Non-linear registration aka spatial normalisation (technical report TR07JA2)
Andersson, J. L., Jenkinson, M., Smith, S. et al. (2007) · 2007
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A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · 2007
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Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008) · 2008
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Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: an MRI study of 676 AD, MCI, and normal subjects
Hua, X., Leow, A. D., Parikshak, N., Lee, S., Chiang, M.-C., Toga, A. W., Jack Jr, C. R., Weiner, M. W., Thompson, P. M., Initiative, A. D. N. et al. (2008) · 2008
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Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study
de Jong, L. W., van der Hiele, K., Veer, I. M., Houwing, J., Westendorp, R., Bollen, E., de Bruin, P. W., Middelkoop, H., van Buchem, M. A., & van der Grond, J. (2008) · 2008
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Brain morphometry with multiecho MPRAGE
van der Kouwe, A. J., Benner, T., Salat, D. H., & Fischl, B. (2008) · 2008
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Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI
Chupin, M., Gérardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehéricy, S., Benali, H., Garnero, L., & Colliot, O. (2009) · 2009
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Accurate and robust brain image alignment using boundary-based registration
Greve, D. N., & Fischl, B. (2009) · 2009
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Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. P. (2009) · 2009
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MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers
Schuff, N., Woerner, N., Boreta, L., Kornfield, T., Shaw, L., Trojanowski, J., Thompson, P., Jack Jr, C., Weiner, M., & Initiative, A. D. N. (2009) · 2009
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Hippocampal volume and asymmetry in mild cognitive impairment and alzheimer’s disease: Meta-analyses of mri studies
Shi, F., Liu, B., Zhou, Y., Yu, C., & Jiang, T. (2009) · 2009
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Non-local MRI upsampling
Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Collins, D. L., & Robles, M. (2010) · 2010
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MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field
Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P.-F., & Gruetter, R. (2010) · 2010
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Fast free-form deformation using graphics processing units
Modat, M., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Ourselin, S. (2010) · 2010
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Multimodal mr synthesis via modality-invariant latent representation
Chartsias, A., Joyce, T., Giuffrida, M. V., & Tsaftaris, S. A. (2017) · 2017
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Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding
Huang, Y., Shao, L., & Frangi, A. F. (2017) · 2017
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · 2017
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Brain MRI super-resolution using deep 3D convolutional networks
Pham, C.-H., Ducournau, A., Fablet, R., & Rousseau, F. (2017) · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017
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Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer’s disease
Lehmann, M., Crutch, S. J., Ridgway, G. R., Ridha, B. H., Barnes, J., Warrington, E. K., Rossor, M. N., & Fox, N. C. (2011) · 2011
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A bayesian model of shape and appearance for subcortical brain segmentation
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011) · 2011
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A compressed sensing approach for MR tissue contrast synthesis
Roy, S., Carass, A., & Prince, J. (2011) · 2011
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SPM: a history
Ashburner, J. (2012) · 2012
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Freesurfer
Fischl, B. (2012) · 2012
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Discriminant analysis of longitudinal cortical thickness changes in Alzheimer’s disease using dynamic and network features
Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D., Initiative, A. D. N. et al. (2012) · 2012
Cited alongside, same era.
Is synthesizing MRI contrast useful for inter-modality analysis?
Iglesias, J. E., Konukoglu, E., Zikic, D., Glocker, B., Van Leemput, K., & Fischl, B. (2013) · 2013
Cited alongside, same era.
Generative diffeomorphic modelling of large mri data sets for probabilistic template construction
Blaiotta, C., Freund, P., Cardoso, M. J., & Ashburner, J. (2018) · 2018
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MRI super-resolution using multi-channel total variation
Brudfors, M., Balbastre, Y., Nachev, P., & Ashburner, J. (2018) · 2018
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Super-resolution musculoskeletal MRI using deep learning
Chaudhari, A. S., Fang, Z., Kogan, F., Wood, J., Stevens, K. J., Gibbons, E. K., Lee, J. H., Gold, G. E., & Hargreaves, B. A. (2018) · 2018
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Brain MRI super resolution using 3D deep densely connected neural networks
Chen, Y., Xie, Y., Zhou, Z., Shi, F., Christodoulou, A. G., & Li, D. (2018c) · 2018
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Distribution matching losses can hallucinate features in medical image translation
Cohen, J. P., Luck, M., & Honari, S. (2018) · 2018
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Medical image imputation from image collections
Dalca, A. V., Bouman, K. L., Freeman, W. T., Rost, N. S., Sabuncu, M. R., & Golland, P. (2018) · 2018
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Medical image synthesis with deep convolutional adversarial networks
Nie, D., Trullo, R., Lian, J., Wang, L., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. (2018) · 2018
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Medical image synthesis for data augmentation and anonymization using generative adversarial networks
Shin, H.-C., Tenenholtz, N. A., Rogers, J. K., Schwarz, C. G., Senjem, M. L., Gunter, J. L., Andriole, K. P., & Michalski, M. (2018) · 2018
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DeepNAT:Deep convolutional neural network for segmenting neuroanatomy
Wachinger, C., Reuter, M., & Klein, T. (2018) · 2018
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Deep embedding convolutional neural network for synthesizing ct image from t1-weighted mr image
Xiang, L., Wang, Q., Nie, D., Zhang, L., Jin, X., Qiao, Y., & Shen, D. (2018) · 2018
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Voxelmorph: a learning framework for deformable medical image registration
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2019) · 2019
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Semi-supervised and task-driven data augmentation
Chaitanya, K., Karani, N., Baumgartner, C. F., Becker, A., Donati, O., & Konukoglu, E. (2019) · 2019
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Image synthesis in multi-contrast MRI with conditional generative adversarial networks
Dar, S. U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., & Çukur, T. (2019) · 2019
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PSACNN: Pulse sequence adaptive fast whole brain segmentation
Jog, A., Hoopes, A., Greve, D. N., Van Leemput, K., & Fischl, B. (2019) · 2019
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Curbing unnecessary and wasted diagnostic imaging
Oren, O., Kebebew, E., & Ioannidis, J. P. (2019) · 2019
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QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Roy, A. G., Conjeti, S., Navab, N., Wachinger, C., Initiative, A. D. N. et al. (2019) · 2019
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A deep learning framework for unsupervised affine and deformable image registration
de Vos, B. D., Berendsen, F. F., Viergever, M. A., Sokooti, H., Staring, M., & Išgum, I. (2019) · 2019
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