Diffusion time dependence of magnetic resonance diffusion signal decays: an investigation of water exchange in human brain in vivo

2011 ◽  
Vol 25 (4) ◽  
pp. 285-296 ◽  
Author(s):  
Marzieh Nezamzadeh
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Time Dependence ◽  
Water Exchange ◽  
Diffusion Time

In vivo magnetic resonance imaging and 31P spectroscopy of large human brain tumours at 1.5 tesla

1988 ◽  
Vol 29 (1) ◽  
pp. 77-82 ◽  
Author(s):  
C. Thomsen ◽  
K. E. Jensen ◽  
E. Achten ◽  
O. Henriksen
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Brain Tumours ◽  
Resonance Imaging ◽  
Human Brain Tumours

Molar quantitation of in vivo proton metabolites in human brain with 3D magnetic resonance spectroscopic imaging

1994 ◽  
Vol 12 (4) ◽  
pp. 661-667 ◽  
Author(s):  
C.A. Husted ◽  
J.H. Duijn ◽  
G.B. Matson ◽  
A.A. Maudsley ◽  
M.W. Weiner
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Spectroscopic Imaging ◽  
Magnetic Resonance Spectroscopic Imaging

Identification and Morphologic Assessment of Mesocoelic Recess by In Vivo Human Brain Imaging With 7.0-T Magnetic Resonance Imaging

2011 ◽  
Vol 35 (4) ◽  
pp. 486-491 ◽  
Author(s):  
Na Rae Kim ◽  
Je G. Chi ◽  
Sang Han Choi ◽  
Young-Bo Kim ◽  
Hee Young Hwang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Brain Imaging ◽  
Resonance Imaging

scholarly journals Metabolic Maturation of the Human Brain From Birth Through Adolescence: Insights From In Vivo Magnetic Resonance Spectroscopy

2012 ◽  
Vol 23 (12) ◽  
pp. 2944-2955 ◽  
Author(s):  
S. Bluml ◽  
J. L. Wisnowski ◽  
M. D. Nelson ◽  
L. Paquette ◽  
F. H. Gilles ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Human Brain ◽  
Resonance Spectroscopy

scholarly journals In vivo detection of serine in the human brain by proton magnetic resonance spectroscopy (1 H-MRS) at 7 Tesla

2009 ◽  
Vol 62 (4) ◽  
pp. 1042-1046 ◽  
Author(s):  
Changho Choi ◽  
Ivan Dimitrov ◽  
Deborah Douglas ◽  
Chenguang Zhao ◽  
Halima Hawesa ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Human Brain ◽  
Proton Magnetic Resonance ◽  
Proton Magnetic Resonance Spectroscopy ◽  
7 Tesla ◽  
Resonance Spectroscopy ◽  
In Vivo Detection

scholarly journals On the Reconstruction of Magnetic Resonance Current Density Images of the Human Brain: Pitfalls and Perspectives

2021 ◽  
Author(s):  
Hasan H. Eroğlu ◽  
Oula Puonti ◽  
Cihan Göksu ◽  
Fróði Gregersen ◽  
Hartwig R. Siebner ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Current Density ◽  
Signal To Noise Ratio ◽  
Ground Truth ◽  
Statistical Testing ◽  
Signal To Noise ◽  
Testing Approach ◽  
Resonance Current

ABSTRACTMagnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. The reconstruction problem is challenging due to a low signal-to-noise ratio and a limited volume coverage of the MR-based measurements, the lack of data from the scalp and skull regions and because MRCDI is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. Most existing reconstruction approaches have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data.We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

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