Confidence images for MR spectroscopic imaging

ArticleinMagnetic Resonance in Medicine 44(4):537-45 · November 2000with5 Reads
Source: PubMed
Abstract
Automated spectral analysis and estimation of signal amplitudes from magnetic resonance data generally constitutes a difficult nonlinear optimization problem. Obtaining a measure of the degree of confidence that one has in the estimated parameters is as important as the estimates themselves. This is particularly important if clinical diagnoses are to be based on estimated metabolite levels, as in applications of MR Spectroscopic Imaging for human studies. In this report, a standard method of obtaining confidence intervals for nonlinear estimation is applied to simulated data and short-TE clinical proton spectroscopic imaging data sets of human brain. So-called "confidence images" are generated to serve as visual indicators of how much trust should be placed in interpretation of spatial variations seen in images derived from fitted metabolite parameter estimates. This method is introduced in a Bayesian framework to enable comparison with similar techniques using Cramer-Rao bounds and the residuals of fitted results.
    • A limitation of this study is that the assessment of spectral quality is based on a spectral fitting procedure without accounting for fitting accuracy. As pointed out previously [6], Cramer-Rao bounds and confidence intervals [7] have limits to evaluate fitting accuracy. While these measures are able to assess the fitting performance, they are not useful to reliably determine if spectra suffer from severely distorted lineshapes or strong lipid contamination, and they provide no additional information to the criteria used in this study.
    [Show abstract] [Hide abstract] ABSTRACT: High-spatial-resolution acquisition (HR) was previously proposed for 3D echo-planar spectroscopic imaging (EPSI) in combination with a high-spatial-resolution water reference EPSI data set to minimize inhomogeneous spectral line broadening, allowing for local frequency shift (B(0) shift) correction in human brain metabolite maps at 1.5 T (Ebel A et al., Magn. Reson. Imaging 21:113-120, 2003). At a higher magnetic field strength, B(0), increased field inhomogeneities typically lead to increased line broadening. Additionally, increased susceptibility variations render shimming of the main magnetic field over the whole head more difficult. This study addressed the question whether local B(0)-shift correction still helps limit line broadening in whole-brain 3D EPSI at higher magnetic fields. The combination of HR and local B(0)-shift correction to limit line broadening was evaluated at 4 T. Similar to the results at 1.5 T, the approach provided a high yield of voxels with good spectral quality for 3D EPSI, resulting in improved brain coverage.
    Full-text · Article · May 2007
    • Details about the model function as well as the optimization process can be found in [11,15]. In the paper by Young et al. [5], the confidence intervals of the estimated parameters were proposed as a parameter for the calculation of error images. From the presented theory it followed that the confidence intervals are superior to CRBs in terms of the lower sensitivity to the inaccuracy of the fitting model used.
    [Show abstract] [Hide abstract] ABSTRACT: The results of spectroscopic imaging (SI) measurements are often presented as metabolic images. If the spectra quality is not sufficient, the calculated concentrations are biased and the metabolic images show an incorrect metabolite distribution. To simplify the quality analysis of spectra measured by SI, an error image, reflecting the accuracy of the computed concentrations, can be displayed along with the metabolite image. In this paper the relevance of Cramer-Rao bounds (CRBs) calculated by the LCModel program to describe errors in estimated concentrations is validated using spectra simulations. The relation between the average CRBs and standard deviations (STD) of metabolite concentrations from 100 simulated spectra for various signal to noise ratio and line broadening conditions is evaluated. A parameter for calculating error images for metabolite ratios is proposed and an effective way to display error images is shown. The results suggest that the average CRBs are strongly correlated with the standard deviations and hence that CRB values reflect the relative uncertainty of the calculated concentrations. The error information can be integrated directly into a metabolite image by displaying only those areas of the metabolite image with corresponding CRBs below a selected threshold or by mapping CRBs as a transparency of the metabolite image. The concept of error images avoids extensive examination of each SI spectrum and helps to reject low quality spectra.
    Full-text · Article · Mar 2006
  • Article · NMR in Biomedicine
  • [Show abstract] [Hide abstract] ABSTRACT: In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.
    Article · Feb 2001
  • [Show abstract] [Hide abstract] ABSTRACT: A comparison is made between two optimization procedures and two data models for automated analysis of in vivo proton MR spectra of brain, typical of that obtained using MR spectroscopic imaging at 1.5 Tesla. First, a shift invariant wavelet filter is presented that provides improved performance over a conventional wavelet filter method for characterizing smoothly varying baseline signals. Next, two spectral fitting methods are described: an iterative spectral analysis method that alternates between optimizing a parametric description of metabolite signals and nonparametric characterization of baseline contributions, and a single-pass method that optimizes a complete spectral and baseline model. Both methods are evaluated using wavelet and spline models of the baseline function. Results are shown for Monte Carlo simulations of data representative of both long and short TE, in vivo 1H acquisitions. Magn Reson Med 45:966–972, 2001. © 2001 Wiley-Liss, Inc.
    Full-text · Article · Jun 2001
  • [Show abstract] [Hide abstract] ABSTRACT: This introductory article addresses approaches currently in use to process in vivo spectra. First, a brief overview is given of the information content represented by the parameters of MR signals. Subsequently, common steps in the processing of MR spectra such as pre-processing, normalisation and quantification and the use of prior knowledge are described. Finally, some prospects for more advanced processing are given.
    Full-text · Article · Jul 2001
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