Automatic Estimation of the Time Origin and Overall Phase of an FID Signal

Vondra, Vlastimil; Boogaart, Aad van den; Graveron‐Demilly, D.; Ormondt, D. van

doi:10.1006/jmra.1996.0086

Cited by 3 publications

(1 citation statement)

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“…Metabolite basis set signals were simulated in jMRUI [54][55][56] with NMRScopeB 55,56 for SPECIAL sequences with parameters corresponding to the rat signal. Because the simulated metabolite basis set signals were not parametrized and the in vivo rat brain signal used exhibited a dead time t 0 = 0.134 ms (estimated by SVD phasing 74 ), the metabolite basis set signals were simulated with the t 0 value found for the rat brain (similar to the experimental conditions, see section 2.3). This was also automatically carried out for the MM basis set.…”

Section: Metabolite Basis Setsmentioning

confidence: 99%

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Starčuková

Su²,

Graveron‐Demilly

2023

NMR in Biomedicine

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Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST‐MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST‐MM and three QUEST‐based strategies for quantifying short echo time signals are compared in terms of bias‐variance trade‐off and Cramér–Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST‐MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST‐MM is shown for 1H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST‐MM is implemented in jMRUI and will be available for public use from version 7.1.

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