Confidence images for MR spectroscopic imaging.
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.
SourceAvailable from: hci.iwr.uni-heidelberg.de
[Show abstract] [Hide abstract]
ABSTRACT: In vivo 1H MRS is rapidly developing as a clinical tool for diagnosing and characterizing breast cancers. Many in vivo and in vitro experiments have demonstrated that alterations in concentrations of choline-containing metabolites are associated with malignant transformation. In recent years, considerable efforts have been made to evaluate the role of 1H MRS measurements of total choline-containing compounds in the management of patients with breast cancer. Current technological developments, including the use of high-field MR scanners and quantitative spectroscopic analysis methods, promise to increase the sensitivity and accuracy of breast MRS. This article reviews the literature describing in vivo MRS in breast cancer, with an emphasis on the development of high-field MR scanning and quantitative methods. Potential applications of these technologies for diagnosing suspicious lesions and monitoring response to chemotherapy are discussed.NMR in Biomedicine 01/2009; 22(1):65 - 76. DOI:10.1002/nbm.1217 · 3.56 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Concentration of the neuronal marker, N-acetylaspartate (NAA), a quantitative metric for the health and density of neurons, is currently obtained by integration of the manually defined peak in whole-head proton (1H)-MRS. Our goal was to develop a full spectral modeling approach for the automatic estimation of the whole-brain NAA concentration (WBNAA) and to compare the performance of this approach with a manual frequency-range peak integration approach previously employed. MRI and whole-head 1H-MRS from 18 healthy young adults were examined. Non-localized, whole-head 1H-MRS obtained at 3 T yielded the NAA peak area through both manually defined frequency-range integration and the new, full spectral simulation. The NAA peak area was converted into an absolute amount with phantom replacement and normalized for brain volume (segmented from T1-weighted MRI) to yield WBNAA. A paired-sample t test was used to compare the means of the WBNAA paradigms and a likelihood ratio test used to compare their coefficients of variation. While the between-subject WBNAA means were nearly identical (12.8 ± 2.5 mm for integration, 12.8 ± 1.4 mm for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, p = 0.026). The within-subject variability was 11.7% (±1.3 mm) for integration versus 7.0% (±0.8 mm) for spectral modeling, i.e., a 40% improvement. The (quantifiable) quality of the modeling approach was high, as reflected by Cramer–Rao lower bounds below 0.1% and vanishingly small (experimental - fitted) residuals. Modeling of the whole-head 1H-MRS increases WBNAA quantification reliability by reducing its variability, its susceptibility to operator bias and baseline roll, and by providing quality-control feedback. Together, these enhance the usefulness of the technique for monitoring the diffuse progression and treatment response of neurological disorders. Copyright © 2014 John Wiley & Sons, Ltd.NMR in Biomedicine 11/2014; 27(11). DOI:10.1002/nbm.3185 · 3.56 Impact Factor