Implementation of wavelet encoding spectroscopic imaging technique on a 3 Tesla whole body MR scanner: in vitro results

Proton magnetic resonance spectroscopic imaging (MRSI) provides spatial information about tissue metabolite concentrations used in differentiating diseased from normal tissue. Obtaining metabolic maps with high spatial resolution requires long acquisition time where the patient has to lie still inside the magnet bore (scanner) especially if classical Chemical Shift Imaging (CSI) is used. To reduce acquisition time and obtain a more accurate metabolite distribution with low voxel contamination in MRSI, we have recently proposed and successfully implemented a full Wavelet Encoding-Spectroscopic Imaging (WE-SI) technique on a 1.5 Tesla whole body MR clinical scanner. In this paper we describe the implementation of the WE-SI technique at higher magnetic field strength (B(0)) on a clinical 3 Tesla Siemens scanner equipped with parallel imaging tools for better sensitivity. This increases the signal to noise ratio (SNR) and allows combination of the proposed technique with the so-called parallel imaging approach for further acquisition time reduction.

Water modeled signal removal and data quantification in localized MR spectroscopy using a time-scale postacquistion method

We have previously shown the continuous wavelet transform (CWT), a signal-processing tool, which is based upon an iterative algorithm using a lorentzian signal model, to be useful as a postacquisition water suppression technique. To further exploit this tool we show its usefulness in accurately quantifying the signal metabolites after water removal. However, due to the static field inhomogeneities, eddy currents, and "radiation damping," the water signal and the metabolites may no longer have a lorentzian lineshape. Therefore, another signal model must be used. As the CWT is a flexible method, we have developed a new algorithm using a gaussian model and found that it fits the signal components, especially the water resonance, better than the lorentzian model in most cases. A new framework, which uses the two models, is proposed. The framework iteratively extracts each resonance, starting by the water peak, from the raw signal and adjusts its envelope to both the lorentzian and the gaussian models. The model giving the best fit is selected. As a consequence, the small signals originating from metabolites when selecting, removing, and quantifying the dominant water resonance from the raw time domain signal are preserved and an accurate estimation of their concentrations is obtained. This is demonstrated by analyzing (1H) magnetic resonance spectroscopy unsuppressed water data collected from a phantom with known concentrations at two different field strengths and data collected from normal volunteers using two different localization methods.

Lactate editing and lipid suppression by continuous wavelet transform analysis: application to simulated and (1)H MRS brain tumor time-domain data

Determination of lactate concentrations in vivo is required in the noninvasive diagnosis, staging, and therapeutic monitoring of diseases such as cancer, heart disease, and stroke. An iterative filtering process based on the continuous wavelet transform (CWT) method in the time domain is proposed to isolate the lactate doublet signal from overlapping lipid resonances and estimate the magnetic resonance spectroscopy (MRS) parameters of the lactate methyl signal (signal amplitude, chemical shift, J-coupling and apparent transverse relaxation time (T*(2))). This method offers a number of advantages over the multiple quantum (MQ) and difference spectroscopy approaches, including: 1) full recovery of the lactate methyl signal, whereas the MQ methods usually detect 50% of the signal intensity; 2) in contrast to MQ methods, the lipid signal is retained together with J-coupling data on the lactate peak; 3) the CWT method is much less sensitive to motion artifacts than difference spectroscopy. Application of the method to simulated and real (1)H MRS data collected from human blood plasma and brain tumors demonstrated that this filter provides accurate estimates of the MRS parameters of the lactate doublet and efficiently removes lipid contributions.

Time-domain quantification of multiple-quantum-filtered (23)Na signal using continuous wavelet transform analysis

The application of continuous wavelet transform (CWT) analysis technique is presented to analyze multiple-quantum-filtered (MQF) (23)Na magnetic resonance spectroscopy (MRS) data. CWT acts on the free-induction-decay (FID) signal as a time-frequency variable filter. The signal-to-noise ratio (SNR) and frequency resolution of the output filter are locally increased. As a result, MQF equilibrium longitudinal magnetization and the apparent fast and slow transverse relaxation times are accurately estimated. A developed iterative algorithm based on frequency signal detection and components extraction, already proposed, was used to estimate the values of the signal parameters by analyzing simulated time-domain MQF signals and data from an agarose gel. The results obtained were compared to those obtained by measurement of signal height in frequency domain as a function of MQF preparation time and those obtained by a simple time-domain curve fitting. The comparison indicates that the CWT approach provides better results than the other tested methods that are generally used for MQF (23)Na MRS data analysis, especially when the SNR is low. The mean error on the estimated values of the amplitude signal and the apparent fast and slow transverse relaxation times for the simulated data were 2.19, 6. 63, and 16.17% for CWT, signal height in frequency domain, and time-domain curve fitting methods, respectively. Another major advantage of the proposed technique is that it allows quantification of MQF (23)Na signal from a single FID and, thus, reduces the experiment time dramatically.

Quantification of plasma lipoprotein fractions by wavelet transform time-domain data processing of the proton nuclear magnetic resonance methylene spectral region

Quantitative analysis of lipoprotein major fractions, LDL, VLDL and HDL, is of great interest for medical purposes, for instance in liver or heart diseases, diet management or cancer. The presently available biochemical methods require time consuming ultracentrifugation. A potentially automated method is proposed, using time domain quantification by Wavelet Transform (WT-NMR) method. The aim of the present study was to evaluate, on a preliminary series of nine human plasmas, the potential interest of WT-NMR in the quantification of both NMR-visible lipids and total lipoprotein fractions. The correlation coefficients between low and intermediate density (LDL+IDL), very low density (VLDL) and high density (HDL) lipoprotein visible lipid quantifications, obtained on nine human plasmas with WT-NMR and standard biochemical methods, were 0.79, 0.84 and 0.92, respectively. For the total lipoprotein assay, i.e. including an estimation of non NMR-visible protein and free cholesterol, the correlation between WT-NMR and the biochemistry were 0.87 for LDL+IDL, 0.81 for VLDL and 0.88 for HDL.

Time-domain quanification of amplitude, chemical shift, apparent relaxation time T2, and phase by wavelet-transform analysis. Application to biomedical magnetic resonance spectroscopy

The wavelet-transform method is used to quantify the magnetic resonance spectroscopy (MRS) parameters: chemical shift, apparent relaxation time T2, resonance amplitude, and phase. Wavelet transformation is a time-frequency representation which separates each component from the FID, then successively quantifies it and subtracts it from the raw signal. Two iterative procedures have been developed. They have been combined with a nonlinear regression analysis method and tested on both simulated and real sets of biomedical MRS data selected with respect to the main problems usually encountered in quantifying biomedical MRS, specifically "chemical noise," resulting from overlapping resonances, and baseline distortion. The results indicate that the wavelet-transform method can provide efficient and accurate quantification of MRS data.

Proton nuclear magnetic resonance spectroscopy of plasma lipoprotein: technical problems and potential interest in cancer disease

This paper reviews several methods presently available for analysing lipoprotein NMR spectra. Two main steps can be distinguished: NMR signal processing and data analysis. Time domain (wavelet transform) and frequency domain (curve fitting) signal processing methods are compared. Statistical methods of data analysis (Ascending Hierarchical Classification, Correspondence Analysis and Principal Component Analysis) have been tested on simulated NMR data of plasma lipoprotein with different numbers of sampling points and different noise levels. These few examples clearly attest that the NMR approach to complex "mixture" (such as body fluids) analysis is emerging from its infancy. New interest in plasma lipoprotein analysis in cancer biology is finally discussed in the light of previous clinical and experimental results and of understanding of lipid metabolism in cancer.