Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7 T data at young and old age
暂无分享,去创建一个
Mark Jenkinson | Max C. Keuken | Birte U. Forstmann | Eelke Visser | B. Forstmann | M. Jenkinson | M. C. Keuken | E. Visser
[1] Xuemei Huang,et al. Quantitative susceptibility mapping of the midbrain in Parkinson's disease , 2016, Movement disorders : official journal of the Movement Disorder Society.
[2] M. Mallar Chakravarty,et al. Atlas-Based Segmentation of the Subthalamic Nucleus, Red Nucleus, and Substantia Nigra for Deep Brain Stimulation by Incorporating Multiple MRI Contrasts , 2012, IPCAI.
[3] Yi Wang,et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging , 2010, Magnetic resonance in medicine.
[4] Yu-Chung N. Cheng,et al. Susceptibility weighted imaging (SWI) , 2004, Zeitschrift fur medizinische Physik.
[5] Ferdinand Schweser,et al. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? , 2011, NeuroImage.
[6] Yi Wang,et al. Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker , 2014, Magnetic resonance in medicine.
[7] John F Schenck,et al. Magnetic resonance imaging of brain iron , 2003, Journal of the Neurological Sciences.
[8] P Riederer,et al. Selective Increase of Iron in Substantia Nigra Zona Compacta of Parkinsonian Brains , 1991, Journal of neurochemistry.
[9] P. Krack,et al. Mood and behavioural effects of subthalamic stimulation in Parkinson's disease , 2014, The Lancet Neurology.
[10] Robert Turner,et al. Multi-modal ultra-high resolution structural 7-Tesla MRI data repository , 2014, Scientific data.
[11] J. Besag. On the Statistical Analysis of Dirty Pictures , 1986 .
[12] G. Sapiro,et al. Comprehensive in vivo Mapping of the Human Basal Ganglia and Thalamic Connectome in Individuals Using 7T MRI , 2012, PloS one.
[13] Didier Dormont,et al. Is the subthalamic nucleus hypointense on T2-weighted images? A correlation study using MR imaging and stereotactic atlas data. , 2004, AJNR. American journal of neuroradiology.
[14] Xiaojun Xu,et al. Age, gender, and hemispheric differences in iron deposition in the human brain: An in vivo MRI study , 2008, NeuroImage.
[15] Torsten Rohlfing,et al. MRI estimates of brain iron concentration in normal aging: Comparison of field-dependent (FDRI) and phase (SWI) methods , 2009, NeuroImage.
[16] Birte U. Forstmann,et al. Quantifyinginter-individualanatomicalvariabilityinthe subcortexusing 7 T structural MRI , 2014 .
[17] B. Forstmann,et al. Ultra-High 7T MRI of Structural Age-Related Changes of the Subthalamic Nucleus , 2012, The Journal of Neuroscience.
[18] Alberto Gatti,et al. The role of iron and copper molecules in the neuronal vulnerability of locus coeruleus and substantia nigra during aging. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[19] Stephen M. Smith,et al. A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..
[20] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[21] A. Lang,et al. Double-blind evaluation of subthalamic nucleus deep brain stimulation in advanced Parkinson's disease , 1998, Neurology.
[22] Mark Jenkinson,et al. Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool , 2016, NeuroImage.
[23] Nicolas Guizard,et al. Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA‐based analysis , 2014, Human brain mapping.
[24] Ferdinand Schweser,et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study , 2012, NeuroImage.
[25] Christophe Lenglet,et al. Semiautomatic Segmentation of Brain Subcortical Structures From High-Field MRI , 2014, IEEE Journal of Biomedical and Health Informatics.
[26] Stephen M. Smith,et al. Permutation inference for the general linear model , 2014, NeuroImage.
[27] Changqing Jiang,et al. Automated Segmentation and Reconstruction of the Subthalamic Nucleus in Parkinson's Disease Patients , 2016, Neuromodulation : journal of the International Neuromodulation Society.
[28] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[29] E.M. Haacke,et al. Characterizing the Mesencephalon Using Susceptibility-Weighted Imaging , 2009, American Journal of Neuroradiology.
[30] R M Lehman,et al. Comparison of anatomic and neurophysiological methods for subthalamic nucleus targeting. , 2001, Neurosurgery.
[31] Maria Grazia Bruzzone,et al. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. , 2009, Radiology.
[32] S. Lehéricy,et al. Reproducibility of R2* and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects , 2017, NMR in biomedicine.
[33] M. Jenkinson. Non-linear registration aka Spatial normalisation , 2007 .
[34] A. Benabid,et al. Electrical stimulation of the subthalamic nucleus in advanced Parkinson's disease. , 1998, The New England journal of medicine.
[35] Stephen M. Smith,et al. Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.
[36] E. Moro,et al. Chronic subthalamic nucleus stimulation reduces medication requirements in Parkinson’s disease , 1999, Neurology.
[37] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[38] B. Hallgren,et al. THE EFFECT OF AGE ON THE NON‐HAEMIN IRON IN THE HUMAN BRAIN , 1958, Journal of neurochemistry.
[39] Yasin Temel,et al. Differential effects of subthalamic nucleus stimulation in advanced Parkinson disease on reaction time performance , 2005, Experimental Brain Research.
[40] Tobias Kober,et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.
[41] A. McKinney,et al. Age-related Iron Deposition in the Basal Ganglia: Quantitative Analysis in Healthy Subjects , 2010 .
[42] Min Zhang,et al. Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping , 2012, Magnetic resonance in medicine.
[43] P. Boesiger,et al. Age distribution and iron dependency of the T2 relaxation time in the globus pallidus and putamen , 2004, Neuroradiology.
[44] A. Benabid,et al. Effect on parkinsonian signs and symptoms of bilateral subthalamic nucleus stimulation , 1995, The Lancet.
[45] D. Louis Collins,et al. Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease , 2012, International Journal of Computer Assisted Radiology and Surgery.
[46] Anne C. Trutti,et al. Effects of aging on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{1}$$\end{document}T1, \documentclass[12pt]{mini , 2017, Brain Structure and Function.
[47] Max C. Keuken,et al. Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI , 2014, NeuroImage.
[48] Bram Platel,et al. Magnetic resonance imaging techniques for visualization of the subthalamic nucleus. , 2011, Journal of neurosurgery.
[49] Stephen M. Smith,et al. A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.
[50] P J Kelly,et al. Comparison of anatomic and neurophysiological methods for subthalamic nucleus targeting. , 2000, Neurosurgery.
[51] Ferdinand Schweser,et al. Toward online reconstruction of quantitative susceptibility maps: Superfast dipole inversion , 2013, Magnetic resonance in medicine.