S. Cavassila

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 1H human brain with estimation of the background is demonstrated.

Targeted deletion of liver glucose-6 phosphatase mimics glycogen storage disease type 1a including development of multiple adenomas

BACKGROUND AND AIMS Glycogen storage disease type 1a (GSD1a) is an inherited disease caused by a deficiency in the catalytic subunit of the glucose-6 phosphatase enzyme (G6Pase). GSD1a is characterized by hypoglycaemia, hyperlipidemia, and lactic acidosis with associated hepatic (including hepatocellular adenomas), renal, and intestinal disorders. A total G6pc (catalytic subunit of G6Pase) knock-out mouse model has been generated that mimics the human pathology. However, these mice rarely live longer than 3 months and long-term liver pathogenesis cannot be evaluated. Herein, we report the long-term characterization of a liver-specific G6pc knock-out mouse model (L-G6pc(-/-)). METHODS We generated L-G6pc(-/-) mice using an inducible CRE-lox strategy and followed up the development of hepatic tumours using magnetic resonance imaging. RESULTS L-G6pc(-/-) mice are viable and exhibit normoglycemia in the fed state. They develop hyperlipidemia, lactic acidosis, and uricemia during the first month after gene deletion. However, these plasmatic parameters improved after 6 months. L-G6pc(-/-) mice develop hepatomegaly with glycogen accumulation and hepatic steatosis. Using an MRI approach, we could detect hepatic nodules with diameters of less than 1 mm, 9 months after induction of deficiency. Hepatic nodules (1 mm) were detected in 30-40% of L-G6pc(-/-) mice at 12 months. After 18 months, all L-G6pc(-/-) mice developed multiple hepatocellular adenomas of 1-10 mm diameter. CONCLUSIONS This is the first report of a viable animal model of the hepatic pathology of GSD1a, including the late development of hepatocellular adenomas.

The beneficial influence of prior knowledge on the quantitation of in vivo magnetic resonance spectroscopy signals.

RATIONALE AND OBJECTIVES This work concerns quantitation of in vivo magnetic resonance spectroscopy signals and the influence of prior knowledge on the precision of parameter estimates. The authors point out how prior knowledge can be used for experiments. METHODS The Cramer-Rao lower bounds formulae of the noise-related standard deviations on spectral parameters for doublets and triplets were derived. Chemical prior knowledge of the multiplet structures was used. RESULTS The benefit of chemical prior knowledge was estimated for doublet and triplet structures of arbitrary shape. Then, it was used to quantify in vivo 31P time-series signals of rat brain. CONCLUSIONS Analytic expressions of errors on parameter estimates were derived, enabling prediction of the benefit of prior knowledge on quantitation results. These formulae allow us to state, for a given noise level, if the quantitation of strongly overlapping peaks such as adenosine triphosphate multiplets can be performed successfully.

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

In vivo hepatic lipid quantification using MRS at 7 Tesla in a mouse model of glycogen storage disease type 1a

The assessment of liver lipid content and composition is needed in preclinical research to investigate steatosis and steatosis-related disorders. The purpose of this study was to quantify in vivo hepatic fatty acid content and composition using a method based on short echo time proton magnetic resonance spectroscopy (MRS) at 7 Tesla. A mouse model of glycogen storage disease type 1a with inducible liver-specific deletion of the glucose-6-phosphatase gene (L-G6pc−/−) mice and control mice were fed a standard diet or a high-fat/high-sucrose (HF/HS) diet for 9 months. In control mice, hepatic lipid content was found significantly higher with the HF/HS diet than with the standard diet. As expected, hepatic lipid content was already elevated in L-G6pc−/− mice fed a standard diet compared with control mice. L-G6pc−/− mice rapidly developed steatosis which was not modified by the HF/HS diet. On the standard diet, estimated amplitudes from olefinic protons were found significantly higher in L-G6pc−/− mice compared with that in control mice. L-G6pc−/− mice showed no noticeable polyunsaturation from diallylic protons. Total unsaturated fatty acid indexes measured by gas chromatography were in agreement with MRS measurements. These results showed the great potential of high magnetic field MRS to follow the diet impact and lipid alterations in mouse liver.

Toward a quantitative analysis of in vivo proton magnetic resonance spectroscopic signals using the continuous Morlet wavelet transform

We apply the Morlet wavelet transform (MWT) for quantitatively analyzing proton magnetic resonance spectroscopic (MRS) signals, more precisely signals acquired at short echo time. These signals contain many resonating components whose frequencies are characteristic of the observed metabolites, and amplitudes are directly related to the concentrations of these metabolites. With these powerful properties, in vivo MRS can be considered as a unique non-invasive tool to explore biochemical compounds of living tissues. However, the analysis and quantification of these metabolite contributions are difficult due to the low signal-to-noise ratio, the number of overlapping frequencies and the contamination of the signal of interest with water and a baseline originating from macromolecules and lipids. The baseline is a major obstacle for MRS quantification as its shape and intensity are generally not known a priori. In this paper, we present the methodology to quantify the signals by the MWT. We assess the ability of the proposed method to recover parameters such as metabolite amplitudes, frequencies and damping factors while facing successively quantification challenges arising from the non-Lorentzian lineshapes, overlapping frequencies, and noise or baseline. Tests of the method are performed on simulated signals alone or combined with either in vitro acquisition and/or in vivo macromolecular signal acquired on a horizontal 4.7 T scanner. In presence of the macromolecules, the amplitude parameter is correctly derived by the method, thanks to the time-scale representation of the wavelet which enables us to distinguish the two signals by their time decays and without any additional pre-processing.

Quantification method using the Morlet wavelet for Magnetic Resonance Spectroscopic signals with macromolecular contamination

We study the Morlet wavelet transform on characterizing Magnetic Resonance Spectroscopic (MRS) signals acquired at short echo-time. These signals contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline signal decays faster than the metabolite ones. Therefore, by making use of the time-scale representation of the wavelet, the two signals can be distinguished without any additional pre-processing. This is confirmed by the experimental results which show that the Morlet wavelet can correctly quantify the metabolite contributions even when a baseline is embedded in the MRS signals.

A complete software package for MR signal processing

Magnetic resonance spectroscopy (MRS) offers a wealth of information to the biochemist or radiologist. Metabolite concentrations, J-couplings, pH, ion concentrations and gradients, temperature, etc., can all be obtained, in situ, from well-defined volumes in the human body, and in a totally non-invasive way. However, simple methods such as peak area integration or automatic line fitting in the FT MR spectrum are still relied on for routine MRS data analysis. The disadvantages of such methods are tolerated in order to keep processing fast and simple for the spectroscopist. The authors have developed a graphical user interface, in which advanced time domain signal processing methods are combined. They present a complete software package for routine MR data analysis, called MRUI, enabling the use of advanced parameter estimation algorithms with incorporation of prior knowledge via simple menus and spectral displays, in a fashion similar to the spectroscopists spectrometer software.

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

In vivo 1H short echo-time magnetic resonance spectra are made up of overlapping spectral components from many metabolites. Typically, they exhibit 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 quantitated 1H 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

Morlet wavelet analysis of Magnetic Resonance Spectroscopic signals with macromolecular contamination

We apply theMorlet wavelet transform to characterizing Magnetic Resonance Spectroscopy (MRS) signals acquired at short echo-time. These signals usually contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline accommodation is one of the major obstructions in in vivo short echo-time MRS quantification as its shape and intensity are not known a priori. In this paper, the simulated signal of the N-acetylaspartate (NAA) metabolite is used as a test signal to be recovered after adding the in vivo macromolecular signal. The in vivo macromolecule MRS signal was acquired on a horizontal 4.7T Biospec system. By optimizing the inversion time, which represents the delay between the inversion pulse and the first pulse of the PRESS sequence, the metabolites are nullified while the others are maintained. The metabolite-nullified signal from a volume-of-interest centralized in the hippocampus of a healthy mouse, which was a combination of residual water, baseline and noise, was added to the signal of NAA. The amplitude of the metabolite is also varied to visualize the sensitivity of the wavelet transform at different ratios between the intensity of the macromolecular and the metabolite signals. Compared to the simulated signal of NAA, the signal decays much faster. The time-scale representation of the wavelet can therefore distinguish the two signals without any additional pre-processing. The amplitude of the metabolite is also correctly derived although at earlier time it still has an effect of the baseline.