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Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data.
Quantitation of intrinsic magnetic susceptibility-related effects in a tissue matrix. Phantom study
Yablonskiy DA · 1998
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Image quality assessment: From error visibility to structural similarity
Zhou Wang, Bovik AC, Sheikh HR, Simoncelli EP · 2004
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BET2: MR-based estimation of brain, skull and scalp surfaces
Jenkinson M, Pechaud M, Smith S · 2005
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Natural linewidth chemical shift imaging (NL-CSI)
Bashir A, Yablonskiy DA · 2006
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Fast multislice mapping of the myelin water fraction using multicompartment analysis of T2* decay at 3T: A preliminary postmortem study
Du YP, Chu R, Hwang D, Brown MS, Kleinschmidt-DeMasters BK, Singel D, Simon JH · 2007
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Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI
Odille F, Vuissoz PA, Marie PY, Felblinger J · 2008
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Prospective real-time correction for arbitrary head motion using active markers
Ooi MB, Krueger S, Thomas WJ, Swaminathan SV, Brown TR · 2009
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PROMO: Real-time prospective motion correction in MRI using image-based tracking
White N, Roddey C, Shankaranarayanan A, Han E, Rettmann D, Santos J, Kuperman J, Dale A · 2010
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Unified reconstruction and motion estimation in cardiac perfusion MRI
Lingala SG, Nadar M, Chefd’hotel C, Zhang L, Jacob M · 2011
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R2* mapping in the presence of macroscopic B0 field variations
Hernando D, Vigen KK, Shimakawa A, Reeder SB · 2012
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Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI
Tisdall MD, Hess AT, Reuter M, Meintjes EM, Fischl B, van der Kouwe AJW · 2012
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Measurement and correction of microscopic head motion during magnetic resonance imaging of the brain
Maclaren J, Armstrong BSR, Barrows RT, Danishad KA, Ernst T, Foster CL, Gumus K, Herbst M, Kadashevich IY, Kusik TP, Li Q, Lovell-Smith C, Prieto T, Schulze P, Speck O, Stucht D, Zaitsev M · 2012
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Neural networks: Tricks of the trade
Bottou L · 2012
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ImageNet classification with deep convolutional neural networks
Krizhevsky A, Sutskevar I, Hinton G · 2012
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Voxel spread function method for correction of magnetic field inhomogeneity effects in quantitative gradient-echo-based MRI
Yablonskiy DA, Sukstanskii AL, Luo J, Wang X · 2013
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Blind retrospective motion correction of MR images
Loktyushin A, Nickisch H, Pohmann R, Schölkopf B · 2013
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Micro-compartment specific T2* relaxation in the brain
Sati P, van Gelderen P, Silva AC, Reich DS, Merkle H, de Zwart JA, Duyn JH · 2013
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On the role of physiological fluctuations in quantitative gradient echo MRI – implications for GEPCI, QSM and SWI
Wen J, Cross AH, Yablonskiy DA · 2015
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Motion correction for free breathing quantitative myocardial t2 mapping: Impact on reproducibility and spatial variability
Roujol S, Basha TA, Weingartner S, Akcakaya M, Berg S, Manning WJ, Nezafat R · 2015
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An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI
Callaghan M, Josephs O, Herbst M, Zaitsev M, Todd N, Weiskopf N · 2015
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Adam: A method for stochastic optimization
Kingma D, Ba J · 2015
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Subcomponents of brain T2* relaxation in schizophrenia, bipolar disorder and siblings: A Gradient Echo Plural Contrast Imaging (GEPCI) study
Mamah D, Wen J, Luo J, Ulrich X, Barch DM, Yablonskiy D · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Ronneberger O, Fischer P, Brox T · 2015
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On the relationship between cellular and hemodynamic properties of the human brain cortex throughout adult lifespan
Zhao Y, Wen J, Cross AH, Yablonskiy DA · 2016
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Network accelerated motion estimation and reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model
Haskell MW, Cauley SF, Bilgic B, Hossbach J, Splitthoff DN, Pfeuffer J, Setsompop K, Wald LL · 2019
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Retrospective correction of motion-affected MR images using deep learning frameworks
Küstner T, Armanious K, Yang J, Yang B, Schick F, Gatidis S · 2019
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Self-supervised learning for medical image analysis using image context restoration
Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D · 2019
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Self-supervised learning of inverse problem solvers in medical imaging
Senouf O, Vedula S, Weiss T, Bronstein A, Michailovich O, Zibulevsky M · 2019
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Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting
Lu AX, Kraus OZ, Cooper S, Moses AM · 2019
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Ulrich X, Yablonskiy DA · 2016
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Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T
Gallichan D, Marques JP, Gruetter R · 2016
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Abadi M, Agarwal A, et al Barham P · 2016
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3D U-Net: Learning dense volumetric segmentation from sparse annotation
Çiçek Ö, Abdulkadir A, Lienkamp S, Brox T, Ronneberger O · 2016
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In vivo Detection of Microstructural Correlates of Brain Pathology in Preclinical and Early Alzheimer Disease with Magnetic Resonance Imaging
Zhao Y, Raichle ME, Wen J, Benzinger TL, Fagan AM, Hassenstab J, Vlassenko AG, Luo J, Cairns NJ, Christensen JJ, Morris JC, Yablonskiy DA · 2017
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Deep convolutional neural network for inverse problems in imaging
Jin KH, McCann MT, Froustey E, Unser M · 2017
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Deep learning with domain adaptation for accelerated projection reconstruction MR
Han YS, Yoo J, Ye JC · 2017
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Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting
Gurbani SS, Sheriff S, Maudsley AA, Shim H, Cooper LAD · 2019
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Single scan quantitative gradient recalled echo MRI for evaluation of tissue damage in lesions and normal appearing gray and white matter in multiple sclerosis
Xiang B, Wen J, Cross AH, Yablonskiy DA · 2019
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Quantitative assessment of multiple sclerosis tissue damage and partial repair in a biopsy proven demyelinating brain lesion using gradient recalled echo imaging
Xiang B, Jie W, Schmidt R, Yablonskiy D, Cross A · 2020
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MedGAN: Medical image translation using GANs
Armanious K, Jiang C, Fischer M, Küstner T, Hepp T, Nikolaou K, Gatidis S, Yang B · 2020
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Correction of motion artifacts using a multiscale fully convolutional neural network
Sommer K, Saalbach A, Brosch T, Hall C, Cross NM, Andre JB · 2020
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Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB)
Liu J, Kocak M, Supanich M, Deng J · 2020
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Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact-free and denoised R2* images
Torop M, Kothapalli SVVN, Sun Y, Liu J, Kahali S, Yablonskiy DA, Kamilov US · 2020
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Self-supervised physics-based deep learning mri reconstruction without fully-sampled data
Yaman B, Hosseini SAH, Moeller S, Ellermann J, Ugurbil K, Akcakaya M · 2020
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Mumford–Shah loss functional for image segmentation with deep learning
Kim B, Ye JC · 2020
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In vivo evolution of biopsy-proven inflammatory demyelination quantified by R2t* mapping
Xiang B, Wen J, Lu HC, Schmidt RE, Yablonskiy DA, Cross AH · 2020
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Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration that Precedes Tissue Atrophy in Early Alzheimer Disease, April 2021
Kothapalli SVVN, Benzinger TL, Aschenbrenner AJ, Perrin RJ, Hildebolt CF, Goyal MS, Fagan AM, Raichle ME, Morris JC, Yablonskiy DA · 2021
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B0 and B1 inhomogeneities in the liver at 1.5 T and 3.0 T
Roberts NT, Hinshaw LA, Colgan TJ, Ii T, Hernando D, Reeder SB · 2021
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Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation
Oh G, Lee JE, Ye JC, Ye JC · 2021
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The Role of the Human Brain Neuron-Glia-Synapse Composition in Forming Resting-State Functional Connectivity Networks
Kahali S, Raichle ME, Yablonskiy DA · 2021
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