NMR in biomedicine
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The purpose of this study was to demonstrate the feasibility of biexponential T1ρ relaxation mapping of human knee cartilage in vivo. A three-dimensional, customized, turbo-flash sequence was used to acquire T1ρ -weighted images from healthy volunteers employing a standard 3-T MRI clinical scanner. A series of T1ρ -weighted images was fitted using monoexponential and biexponential models with two- and four-parametric non-linear approaches, respectively. ⋯ The experiments showed good repeatability with a coefficient of variation (CV) of less than 20%. A biexponential relaxation model showed a better fit than a monoexponential model to the T1ρ relaxation decay in knee cartilage. Biexponential T1ρ components could potentially be used to increase the specificity to detect early osteoarthritis by the measurement of different water compartments and their fractions.
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Inversion recovery ultrashort echo time (IR-UTE) imaging holds the potential to directly characterize MR signals from ultrashort T2 tissue components (STCs), such as collagen in cartilage and myelin in brain. The application of IR-UTE for myelin imaging has been challenging because of the high water content in brain and the possibility that the ultrashort T2 * signals are contaminated by water protons, including those associated with myelin sheaths. This study investigated such a possibility in an ovine brain D2 O exchange model and explored the potential of IR-UTE imaging for the quantification of ultrashort T2 * signals in both white and gray matter at 3 T. ⋯ The T2 * values of STCs were 200-300 μs in both white and gray matter (comparable with the values obtained from myelin powder and its mixture with D2 O or H2 O), and showed minimal changes after sequential immersion. The ultrashort T2 * signals seen on IR-UTE images are unlikely to be from water protons as they are exchangeable with deuterons in D2 O. The source is more likely to be myelin itself in white matter, and might also be associated with other membranous structures in gray matter.
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Water-suppressed MRS acquisition techniques have been the standard MRS approach used in research and for clinical scanning to date. The acquisition of a non-water-suppressed MRS spectrum is used for artefact correction, reconstruction of phased-array coil data and metabolite quantification. Here, a two-scan metabolite-cycling magnetic resonance spectroscopic imaging (MRSI) scheme that does not use water suppression is demonstrated and evaluated. ⋯ The achieved spectral quality, signal-to-noise ratio (SNR) > 20 and linewidth <7 Hz allowed reliable metabolic mapping of five major brain metabolites in the motor cortex with an in-plane resolution of 10 × 10 mm2 in 8 min and with a Cramér-Rao lower bound of less than 20% using LCModel analysis. In addition, the high SNR of the water peak of the non-water-suppressed technique enabled voxel-wise single-scan frequency, phase and eddy current correction. These findings demonstrate that our non-water-suppressed metabolite-cycling MRSI technique can perform robustly on 3 T MRI systems and within a clinically feasible acquisition time.
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Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. ⋯ In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.
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Accurate quantification of chemical exchange saturation transfer (CEST) effects, including dipole-dipole mediated relayed nuclear Overhauser enhancement (rNOE) saturation transfer, is important for applications and studies of molecular concentration and transfer rate (and thereby pH or temperature). Although several quantification methods, such as Lorentzian difference (LD) analysis, multiple-pool Lorentzian fits, and the three-point method, have been extensively used in several preclinical and clinical applications, the accuracy of these methods has not been evaluated. Here we simulated multiple-pool Z spectra containing the pools that contribute to the main CEST and rNOE saturation transfer signals in the brain, numerically fit them using the different methods, and then compared their derived CEST metrics with the known solute concentrations and exchange rates. ⋯ Our results demonstrate that all three quantification methods show similar contrasts between tumor and contralateral normal tissue for both APT and the NOE(-3.5). However, the quantified values of the three methods are significantly different. Our work provides insight into the fitting accuracy obtainable in a complex tissue model and provides guidelines for evaluating other newly developed quantification methods.