Quantification ofin vivo¹H magnetic resonance spectroscopy signals with baseline and lineshape estimation

Abstract

The in vivo quantification of magnetic resonance spectroscopy (MRS) signals is a method to estimate metabolite concentrations of living tissue. Obtaining reliable concentrations is still a challenge due to the experimental conditions affecting spectral quality. Additionally, lipids and macromolecules overlap with the metabolites of interest, affecting their reliable estimation. In this study, we propose to combine the self-deconvolution lineshape estimation method, which accounts for spectral shape distortions, with two different approaches for taking into account the macromolecular baseline contribution: (a) based on macromolecules and lipids measured in vivo using an inversion recovery technique, and (b) based on the simulation of macromolecular resonances using prior knowledge from a database of inversion recovery signals. The ultimate goal is to measure macromolecular and lipid data only once as described in (a) to create macromolecular and lipid profiles. These profiles then can be used as described in (b) for data measured under the same conditions. The method is evaluated on in vivo 1H MRS signals at 9.4 T from mouse hippocampus. Results show that better metabolite fits are obtained when lineshape and baseline estimations are simultaneously performed and that baseline estimation based on prior knowledge from macromolecular measured signals can be reliably used to replace time-consuming individual macromolecular and lipid acquisitions.

Different quantification algorithms may lead to different results: a comparison using proton MRS lipid signals

“…Monte Carlo simulated and in vivo MRS signals were analyzed using several quantification methods: LCModel (17), AMARES (21), QUEST (20), AQSES-Lineshape (18) and Peak integration (27). Quantification results from all methods were compared in both simulated and in vivo signals.…”

“…AQSES-Lineshape (18) in SPID software (http://homes.esat. kuleuven.be/ biomed/software.php/SpidGUI) utilizes the same MRS frequency model used in QUEST.…”

“…In fact, the AQSES method (19) is a time-domain quantification method that differs from QUEST only in the handling of the baseline and in some specific implementation issues. As opposed to AQSES and QUEST, the AQSES-Lineshape extension (18) is able to determine a common line-shape distortion of non-Lorentzian type based on a selfdeconvolution algorithm. The same basis set as in QUEST was used.…”

“…Then we compared the methods using oil and in vivo animal spectra. The following quantification algorithms have been tested, in both frequency and time domains: LCModel (16,17), Automated Quantification of Short Echo-time MRS (AQSES)-Lineshape (18,19), quantitation based on QUantum ESTimation (QUEST) (20), AMARES (21) and Integration implemented in MATLAB ®(The MathWorks TM). QUEST and AMARES are implemented in jMRUI version 4.0 (22).…”

ProFit revisited

“…The approach used for the revised ProFit version is based on self-deconvolution as proposed by Maudsley (23) combined with penalized B-splines (24,25) extended to two dimensions by the use of tensor splines.…”