Achievable Accuracy of Parameter Estimation for Multidimensional NMR Experiments

Ober, Raimund J.; Lin, Zhiping; Yu, Hong

doi:10.1006/jmre.2002.2560

Cited by 15 publications

(11 citation statements)

References 5 publications

(3 reference statements)

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“…In addition, they can be estimated without actually acquiring spectra, but purely based on a parameterized model function and the expected signal-to-noise ratio (SNR). This method of experiment optimization has been used in different fields (Anastasiou and Hall, 2004;Brihuega-Moreno et al, 2003;Ober et al, 2002) but only preliminary results of its use for in vivo MRS have been reported (Bolliger et al, 2012;Chong et al, 2007;Snyder and Lange, 2012).…”

Section: Introductionmentioning

confidence: 99%

On the use of Cramér–Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

Bolliger¹,

Boesch²,

Kreis³

2013

NeuroImage

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Localized Magnetic Resonance Spectroscopy (MRS) is in widespread use for clinical brain research. Standard acquisition sequences to obtain one-dimensional spectra suffer from substantial overlap of spectral contributions from many metabolites. Therefore, specially tuned editing sequences or two-dimensional acquisition schemes are applied to extend the information content. Tuning specific acquisition parameters allows to make the sequences more efficient or more specific for certain target metabolites. Cramér-Rao bounds have been used in other fields for optimization of experiments and are now shown to be very useful as design criteria for localized MRS sequence optimization. The principle is illustrated for one-and two dimensional MRS, in particular the 2D separation experiment, where the usual restriction to equidistant echo time spacings and equal acquisition times per echo time can be abolished. Particular emphasis is placed on optimizing experiments for quantification of GABA and glutamate. The basic principles are verified by Monte Carlo simulations and in vivo for repeated acquisitions of generalized two-dimensional separation brain spectra obtained from healthy subjects and expanded by bootstrapping for better definition of the quantification uncertainties. Use of Cramer Rao bound criteria to optimize in vivo MR spectroscopy experiments  Method illustrated for 1D and 2D MRS, in particular 2D separation (2DJ) experiments  2DJ generalized to non-equidistant echo times and unequal numbers of scans per TE  Experiment optimizations discussed for GABA and glutamate as target metabolites  In vivo verification for sets of human brain spectra expanded by bootstrapping

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Section: Introductionmentioning

confidence: 99%

On the use of Cramér–Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

Bolliger¹,

Boesch²,

Kreis³

2013

NeuroImage

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“…Large MRSI datasets (64 × 64 × 256 points) were processed by DSM in <1 min on a Pentium 4 PC computer at 1.8 GHz. The spectral analysis and metabolite image formation package (12) was applied, including calculations of Cramer‐Rao minimum variance bounds (CRBs) (15) to estimate the accuracy of the spectral fitting. The root mean square (RMS) noise of the experimental data was measured after baseline correction in the spectral range between –1.8 and –3.6 ppm, approximately 2.4 ppm downfield from the NAA resonance (16, 17).…”

Section: Methodsmentioning

confidence: 99%

Magnetic resonance spectroscopic imaging reconstruction with deformable shape‐intensity models

Zhu

Jahng

et al. 2003

Magnetic Resonance in Med

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A new method, based on a deformable shape-intensity model (DSM), was developed to improve the signal-to-noise ratio (SNR) of multidimensional magnetic resonance spectroscopic imaging (MRSI) data sets without affecting spectral lineshapes and linewidths. Improvements with DSM, compared to digital filters using conventional signal apodization, were demonstrated on both simulated and experimental in vivo 1 H MRS images from 22 cognitively normal (CN) elderly subjects and 25 patients with Alzheimer's disease (AD). Simulated MRSI data showed that DSM achieved superior noise suppression compared to a matched apodization filter. Experimental MRSI data showed that SNR could be increased 2.1-fold with DSM without distorting spectral resolution, thus maintaining all spectral features of the raw, unfiltered data. In conclusion, DSM should be used to achieve high SNR in reconstructing MRSI data. Key words: deformable shape-intensity model; magnetic resonance spectroscopic imaging; noise reduction; spectral analysisIn vivo magnetic resonance spectroscopic imaging (MRSI) suffers generally from poor signal-to-noise ratio (SNR), because of a combination of a weak MR signal and low metabolite concentrations. Low SNR limits the technique's ability to detect metabolite abnormalities in subjects. Therefore, an increase of SNR is a key factor in the success of many MRSI applications. Signal averaging is one way to improve SNR. However, this approach is often not practical because of long acquisition times. In addition, signal averaging can become inefficient with higher B 0 fields, because physiological noise, which scales with the magnetic field strength, increasingly contributes to variations of the signal (1). Another way to improve SNR is by noise suppression using digital filters. This is conventionally achieved by multiplying MRSI data in the time domain with exponential functions (apodization filters) that are designed to attenuate high-frequency noise in MR spectra (2,3). A carefully designed apodization filter that "matches" linewidth of the metabolite signal can reduce noise without degrading spectral resolution (2). However, matched filtering is difficult to accomplish in MRSI because linewidths, as well as lineshapes, often vary substantially over large regions in the brain (4,5).Methods that model spectral signal characteristics, such as intensity and frequency, may help resolve the difficulties that arise with the use of spectral apodization filters. Deformable shape-intensity models (DSMs) are being used in digital image analysis (6 -8) to maintain essential characteristics of image shape and intensity while accommodating fluctuations. These models have not been applied before to MRSI; however, the frequency and intensity variations of MRSI may be interpreted in a similar manner as for the shape and intensity fluctuations of images, which suggests that DSM might be useful in analyzing MRSI data. In this study, we applied DSM to process MRSI data to improve SNR. The specific goals were to 1) develop DSM for separating ...

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“…The second problem has been addressed without using numerical optimization of sampling times by all of the reduced dimensionality methods mentioned above. In particular, [14] introduces the Cramér-Rao lower bound as a measure of experiment design and uses it to compare linear and nonlinear sampling for small numbers of peaks. Similarly, 2D NOESY spectra were used to extend the results of a 3D NOESY-NOESY experiment [15] by the appropriate use of 2D mixing times.…”

Section: Introductionmentioning

confidence: 99%

Optimized sampling patterns for multidimensional T2 experiments

Anand¹,

Bain²,

Sharma³

2009

Journal of Magnetic Resonance

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Achievable Accuracy of Parameter Estimation for Multidimensional NMR Experiments

Cited by 15 publications

References 5 publications

On the use of Cramér–Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

On the use of Cramér–Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

Magnetic resonance spectroscopic imaging reconstruction with deformable shape‐intensity models

Optimized sampling patterns for multidimensional T2 experiments

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