3D localized 2D ultrafast J-resolved magnetic resonance spectroscopy: in vitro study on a 7 T imaging system.

2D Magnetic Resonance Spectroscopy (MRS) is a well known tool for the analysis of complicated and overlapped MR spectra and was therefore originally used for structural analysis. It also presents a potential for biomedical applications as shown by an increasing number of works related to localized in vivo experiments. However, 2D MRS suffers from long acquisition times due to the necessary collection of numerous increments in the indirect dimension (t(1)). This paper presents the first 3D localized 2D ultrafast J-resolved MRS sequence, developed on a small animal imaging system, allowing the acquisition of a 3D localized 2D J-resolved MRS spectrum in a single scan. Sequence parameters were optimized regarding Signal-to-Noise ratio and spectral resolution. Sensitivity and spatial localization properties were characterized and discussed. An automatic post-processing method allowing the reduction of artifacts inherent to ultrafast excitation is also presented. This sequence offers an efficient signal localization and shows a great potential for in vivo dynamic spectroscopy.

Semi-parametric estimation in magnetic resonance spectroscopy: automation of the disentanglement procedure.

Semi-parametric disentanglement of parametric parts from non-parametric parts of a signal is a universal problem. This study concerns estimation of metabolite concentrations from in vivo Magnetic Resonance Spectroscopy (MRS) signals. Due to in vivo conditions, so-called macro-molecules contribute non-parametric components to the signals. Disentanglement is achieved by exploiting prior knowledge about the parametric and non-parametric parts directly in the measurement domain. Moreover, Cramér-Rao bounds on the non-parametric part are derived. These expressions are used to automate the disentanglement procedure.

Estimation of metabolite T1 relaxation times using tissue specific analysis, signal averaging and bootstrapping from magnetic resonance spectroscopic imaging data.

OBJECT: A novel method of estimating metabolite T1 relaxation times using MR spectroscopic imaging (MRSI) is proposed. As opposed to conventional single-voxel metabolite T1 estimation methods, this method investigates regional and gray matter (GM)/white matter (WM) differences in metabolite T1 by taking advantage of the spatial distribution information provided by MRSI. MATERIAL AND METHODS: The method, validated by Monte Carlo studies, involves a voxel averaging to preserve the GM/WM distribution, a non-linear least squares fit of the metabolite T1 and an estimation of its standard error by bootstrapping. It was applied in vivo to estimate the T1 of N-acetyl compounds (NAA), choline, creatine and myo-inositol in eight normal volunteers, at 1.5 T, using a short echo time 2D-MRSI slice located above the ventricles. RESULTS: WM-T 1,NAA was significantly (P < 0.05) longer in anterior regions compared to posterior regions of the brain. The anterior region showed a trend of a longer WM T1 compared to GM for NAA, creatine and myo-Inositol. Lastly, accounting for the bootstrapped standard error estimate in a group mean T1 calculation yielded a more accurate T1 estimation. CONCLUSION: The method successfully measured in vivo metabolite T1 using MRSI and can now be applied to diseased brain.

Time-domain semi-parametric estimation based on a metabolite basis set.

A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.

Metabolite concentrations of healthy mouse brain by Magnetic Resonance Spectroscopy at 7 Tesla.

In vivo1H short echo-time Magnetic Resonance spectra are made up of overlapping spectral components from many metabolites. Typically, they exibit low signal-to-noise ratio. Metabolite concentrations are obtained by quantitating such spectra. Quantitation is difficult due to the superposition of metabolite resonances, macromolecules, lipids and water residue contributions. A fitting algorithm invoking extensive prior knowledge is needed. We quantitated1H in vivo mouse brain spectra obtained at 7 Tesla using the time-domain QUEST method combined with in vitro metabolite basis set signals. Brain metabolite concentrations estimated from eight mouse brain signals are compared to previously reported results.

Time-domain quantitation of 1H short echo-time signals: background accommodation.

Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. While the model function of the metabolite signal is known, that of the macromolecules is only partially known. We present time-domain semi-parametric estimation approaches based on the QUEST quantitation algorithm (QUantitation based on QUantum ESTimation) and encompassing Cramér-Rao bounds that handle the influence of 'nuisance' parameters related to the background. Three novel methods for background accommodation are presented. They are based on the fast decay of the background signal in the time domain. After automatic estimation, the background signal can be automatically (1) subtracted from the raw data, (2) included in the basis set as multiple components, or (3) included in the basis set as a single entity. The performances of these methods combined with QUEST are evaluated through extensive Monte Carlo studies. They are compared in terms of bias-variance trade-off. Because error bars on the amplitudes are of paramount importance for diagnostic reliability, Cramér-Rao bounds accounting for the uncertainty caused by the background are proposed. Quantitation with QUEST of in vivo short echo-time (1)H human brain with estimation of the background is demonstrated.

Dynamic magnetic resonance imaging paragraph sign with radial scanning: a post-acquisition paragraph sign keyhole approach.

A method - PA-keyhole - for 2D/3D dynamic magnetic resonance imaging with radial scanning is proposed. PA-keyhole exploits the inherent strong oversampling in the center of k-space, which contains crucial temporal information regarding contrast evolution. The method is based on: (1). a rearrangement of the temporal order of 2D/3D isotropic distributions of trajectories during the scan into subdistributions according to the desired time resolution, (2). a new post-acquisition keyhole approach based on the replacement of the central disk/sphere in k-space using data solely from a subdistribution, and (3). reconstruction of 2D/3D dynamic (time-resolved) images using 2D/3D-gridding with Pipe's approach to the sampling density compensation and 2D/3D-IFFT. The scan time is not increased with respect to a conventional 2D/3D radial scan of the same spatial resolution; in addition, one benefits from the dynamic information. The abilities of PA-keyhole and the sliding window techniques to restore simulated dynamic contrast changes are compared. Results are shown both for 2D and 3D dynamic imaging using experimental data. An application to in-vivo ventilation of rat lungs using hyperpolarized helium is demonstrated.