Investigation of proton spin relaxation in water with dispersed silicon nanoparticles for potential magnetic resonance imaging applications

2018 ◽  
Vol 123 (10) ◽  
pp. 104302 ◽  
Author(s):  
Yu. V. Kargina ◽  
M. B. Gongalsky ◽  
A. M. Perepukhov ◽  
A. A. Gippius ◽  
A. A. Minnekhanov ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Spin Relaxation ◽  
Silicon Nanoparticles ◽  
Proton Spin ◽  
Resonance Imaging

scholarly journals Magnetic resonance imaging biomarker calibration service: proton spin relaxation times

2018 ◽  
Author(s):  
Michael A Boss ◽  
Andrew M Dienstfrey ◽  
Zydrunas Gimbutas ◽  
Kathryn E Keenan ◽  
Anthony B Kos ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Spin Relaxation ◽  
Relaxation Times ◽  
Proton Spin ◽  
Imaging Biomarker ◽  
Resonance Imaging

scholarly journals Polyaspartic Acid-Coated Paramagnetic Gadolinium Oxide Nanoparticles as a Dual-Modal T1 and T2 Magnetic Resonance Imaging Contrast Agent

2021 ◽  
Vol 11 (17) ◽  
pp. 8222
Author(s):  
Shanti Marasini ◽  
Huan Yue ◽  
Adibehalsadat Ghazanfari ◽  
Son Long Ho ◽  
Ji Ae Park ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Contrast Agent ◽  
Contrast Agents ◽  
Proton Spin ◽  
Water Proton ◽  
Resonance Imaging ◽  
Polyaspartic Acid ◽  
Mri Contrast ◽  
T1 And T2

Surface-coating polymers contribute to nanoparticle-based magnetic resonance imaging (MRI) contrast agents because they can affect the relaxometric properties of the nanoparticles. In this study, polyaspartic acid (PASA)-coated ultrasmall Gd2O3 nanoparticles with an average particle diameter of 2.0 nm were synthesized using the one-pot polyol method. The synthesized nanoparticles exhibited r1 and r2 of 19.1 and = 53.7 s−1mM−1, respectively, (r1 and r2 are longitudinal and transverse water–proton spin relaxivities, respectively) at 3.0 T MR field, approximately 5 and 10 times higher than those of commercial Gd-chelate contrast agents, respectively. The T1 and T2 MR images could be obtained due to an appreciable r2/r1 ratio of 2.80, indicating their potential as a dual-modal T1 and T2 MRI contrast agent.

Porous silicon nanoparticles as biocompatible contrast agents for magnetic resonance imaging

2015 ◽  
Vol 107 (23) ◽  
pp. 233702 ◽  
Author(s):  
M. B. Gongalsky ◽  
Yu. V. Kargina ◽  
L. A. Osminkina ◽  
A. M. Perepukhov ◽  
M. V. Gulyaev ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Porous Silicon ◽  
Contrast Agents ◽  
Silicon Nanoparticles ◽  
Resonance Imaging ◽  
Porous Silicon Nanoparticles

An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging

1997 ◽  
Vol 11 (08) ◽  
pp. 1189-1211 ◽  
Author(s):  
Dzung Pham ◽  
Jerry L. Prince ◽  
Chenyang Xu ◽  
Azar P. Dagher
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Pulse Sequence ◽  
Joint Probability ◽  
Proton Spin ◽  
Probability Mass Function ◽  
Resonance Imaging ◽  
Data Set ◽  
Statistical Characterization ◽  
The Brain

A procedure for estimating the joint probability density function (pdf) of T1, T2 and proton spin density (PD) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps: (i) preprocess the data to remove extracranial tissue using a sequence of image processing operators; (ii) estimate T1, T2 and PD by fitting the preprocessed data to an imaging equation; (iii) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location; (iv) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify the remaining estimates as either GM, WM or CSF; (v) compute statistics on the classified estimates to obtain a probability mass function and a Gaussian joint pdf of the tissue parameters for each tissue class. Some preliminary results are shown comparing computed pdf's of young, elderly and Alzheimer's subjects. Two brief examples applying the joint pdf's to pulse sequence optimization and generation of computational phantoms are also provided.

Is the magnetic resonance imaging proton spin-lattice relaxation time a reliable noninvasive parameter of developing liver fibrosis?

Hepatology ◽  
1988 ◽  
Vol 8 (2) ◽  
pp. 217-221 ◽  
Author(s):  
Robert A. F. M. Chamuleau ◽  
Joris H. N. Creyghton Ineke De Nie ◽  
Marinus A. Moerland ◽  
Otto R. Van der Lende ◽  
Jaap Smidt
Keyword(s):  
Magnetic Resonance Imaging ◽  
Relaxation Time ◽  
Magnetic Resonance ◽  
Liver Fibrosis ◽  
Lattice Relaxation ◽  
Lattice Relaxation Time ◽  
Proton Spin ◽  
Spin Lattice Relaxation ◽  
Resonance Imaging ◽  
Spin Lattice
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