Hydrogen 1 (1H) magnetic resonance (MR) spectroscopy enables noninvasive in vivo quantification of metabolite concentrations in the brain. Currently, metabolite concentrations are most often presented as ratios (eg, relative to creatine) rather than as absolute concentrations. Despite the success of this approach, it has recently been suggested that relative quantification may introduce substantial errors and can lead to misinterpretation of spectral data and to erroneous metabolite values. The present review discusses relevant methods to obtain absolute metabolite concentrations with a clinical MR system by using single-voxel spectroscopy or chemical shift imaging. Important methodological aspects in an absolute quantification strategy are addressed, including radiofrequency coil properties, calibration procedures, spectral fitting methods, cerebrospinal fluid content correction, macromolecule suppression, and spectral editing. Techniques to obtain absolute concentrations are now available and can be suc...

1H MR Spectroscopy of the Brain: Absolute Quantification of Metabolites

Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo (1)H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. Discrepancies of less than approximately 20% were found between the main metabolite concentrations determined by TARQUIN and LCModel™ from healthy volunteer data. The Monte-Carlo simulation revealed that errors in metabolite concentration estimates were comparable with LCModel™. TARQUIN analyses were also found to be robust to clinical data of variable quality. In conclusion, TARQUIN has been shown to be an accurate and robust algorithm for the analysis of magnetic resonance spectroscopy data making it suitable for use in a clinical setting.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.22579

A constrained least-squares approach to the automated quantitation of in vivo ¹H magnetic resonance spectroscopy data

This work describes a methodology for quantifying levels of total choline-containing compounds (tCho) in the breast using in vivo (1)H MR spectroscopy (MRS) at high field (4 Tesla). Water is used as an internal reference compound to account for the partial volume of adipose tissue. Peak amplitudes are estimated by fitting one peak at a time over a narrow frequency band to allow measurement of small metabolite resonances in spectra with large lipid peaks. This quantitative method significantly improves previously reported analysis methods by accounting for the variable sensitivity of breast (1)H MRS measurements. Using this technique, we detected and quantified a tCho peak in 214 of 500 in vivo spectra. tCho levels were found to be significantly higher in malignancies than in benign abnormalities and normal breast tissues, which suggests that this technique could be used to diagnose suspicious lesions and monitor response to cancer treatments.

http://www.cmrr.umn.edu/~bolan/documents/bolan_MRM_2003_pp.pdf

In vivo quantification of choline compounds in the breast with 1H MR spectroscopy.

In this article an overview of time-domain quantitation methods is given. Advantages of processing the data in the measurement domain are discussed. The basic underlying principles of the methods are outlined and from them the situations under which these algorithms perform well are derived. Also an overview of methods to preprocess the data is given. In that respect, signal-to-noise and/or resolution enhancement, the removal of unwanted components and corrections for model imperfections are discussed.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.695

MR spectroscopy quantitation: a review of time‐domain methods

There has been a vast increase in applications of magnetic resonance spectroscopy (MRS) in biomedical research during the last few years. This is not surprising since MRS provides both in vivo and in vitro a non-invasive tool for various biochemical and biomedical studies. There are also expectations that clinical MRS will have an important role as a diagnostic tool. An essential prerequisite for the future success of MRS for applicability in biomedical sciences will be accurate and biochemically relevant data analysis (at as high a level of automation as possible). This review briefly describes principles of the methodology available for advanced quantitative data analysis in the frequency domain. Various biomedical applications are discussed in order to illustrate the practical aspects of the analyses and to show the applicability and power of biochemical prior knowledge-based lineshape fitting analysis.

MR spectroscopy quantitation: a review of frequency domain methods.

Quantification of NMR visible metabolites by spectral modeling usually assumes a Lorentzian or Gaussian lineshape, despite the fact that experimental lineshapes are neither. To minimize systematic fitting errors, a mixed Lorentzian-Gaussian (Voigt) lineshape model was developed. When tested with synthetic FIDs, the Voigt lineshape model gave more accurate results (maximum error 2%) than either Lorentzian (maximum error 20%) or Gaussian models (maximum error 12%). The three lineshape models gave substantially different peak areas in an in vitro experiment, with the Voigt model having a much lower X 2 (2.1 compared with 5.2 for the Lorentzian model and 6.2 for the Gaussian model). In a group of 10 healthy volunteers, fitting of 1 H spectra from cerebral white matter gave significantly different peak areas between the methods. Even when area ratios were taken, the Lorentzian model gave higher values (+5% for NAA/choline and +2% for NAA/creatine) than the Voigt lineshape model, whereas the Gaussian model gave lower values (-2% and -1%, respectively).

John Higinbotham

Papers

Use of voigt lineshape for quantification of in vivo 1H spectra