Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

Chong, Daniel G.; Kreis, Roland; Bolliger, Christine Sandra; Boesch, Chris; Slotboom, Johannes

doi:10.1007/s10334-011-0246-y

Cited by 75 publications

(111 citation statements)

References 38 publications

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“…Recently, based on the work of Chong et al (46), Bolliger et al (47) have presented a new measurement method/postprocessing method in which the concentration parameters as well as T 1 ‐ and T 2 ‐relaxation time parameters can be estimated from one combined 2DJ/inversion recovery experiment in less than 20 min acquisition time. This method is another important step toward true qMRS as no assumptions need be made on metabolite relaxation times.…”

Section: Discussionmentioning

confidence: 99%

Optimized quantitative magnetic resonance spectroscopy for clinical routine

Jacobs

Wingeier

Su³

et al. 2012

Magnetic Resonance in Med

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Several practical obstacles in data handling and evaluation complicate the use of quantitative localized magnetic resonance spectroscopy (qMRS) in clinical routine MR examinations. To overcome these obstacles, a clinically feasible MR pulse sequence protocol based on standard available MR pulse sequences for qMRS has been implemented along with newly added functionalities to the free software package jMRUI-v5.0 to make qMRS attractive for clinical routine. This enables (a) easy and fast DICOM data transfer from the MR console and the qMRS-computer, (b) visualization of combined MR spectroscopy and imaging, (c) creation and network transfer of spectroscopy reports in DICOM format, (d) integration of advanced water reference models for absolute quantification, and (e) setup of databases containing normal metabolite concentrations of healthy subjects. To demonstrate the work-flow of qMRS using these implementations, databases for normal metabolite concentration in different regions of brain tissue were created using spectroscopic data acquired in 55 normal subjects (age range 6-61 years) using 1.5T and 3T MR systems, and illustrated in one clinical case of typical brain tumor (primitive neuroectodermal tumor). The MR pulse sequence protocol and newly implemented software functionalities facilitate the incorporation of qMRS and reference to normal value metabolite concentration data in daily clinical routine. Techniques for quantitative localized in vivo magnetic resonance spectroscopy (qMRS) have been available for 30 years and were used in countless research projects (1-4). The potential value of qMRS in a clinical setting has especially been demonstrated in the characterization of pathologic tissue changes, where the diagnosis is not evident in MR imaging and in tissues that are not readily available to biopsy (e.g., intracerebral tumors) (5). Despite the fact that localized in vivo MR spectroscopy is noninvasive, and produces quantitative biochemical information (6), its use in clinical routine examinations is still very limited. There are several reasons for this:First, as MR images are closely linked to anatomical structures, their interpretation is for clinical radiologists much more intuitive than that of MR spectra. To interpret MR spectra correctly, knowledge about the signal acquisition and signal artifacts is mandatory. To obtain this knowledge, training is required with experienced spectroscopists, which is not possible in most clinical centers. Furthermore, background knowledge about cellular metabolism is also necessary in order to meaningfully interpret the limited number of metabolites in MR spectra. Compared to the immense amount of different structural proteins, enzymes, and metabolites of a human cell, the very few substances that can be measured using MR spectroscopy yield a low specificity and sensitivity in the context of pathologic tissue changes.Second, clinical MR scanners and manufacturer's spectroscopy postprocessing software have greatly improved and became user-friendly in the recent ye...

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Section: Discussionmentioning

confidence: 99%

Optimized quantitative magnetic resonance spectroscopy for clinical routine

Jacobs

Wingeier

Su³

et al. 2012

Magnetic Resonance in Med

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“…The overall averaged data sets from both series were used to develop a model in FiTAID ; using the averaged data sets, prior knowledge was defined with 12 peaks in the 5–9 ppm region (Fig. ).…”

Section: Methodsmentioning

confidence: 99%

Elucidation of the downfield spectrum of human brain at 7 T using multiple inversion recovery delays and echo times

Fichtner

Henning

Zoelch

et al. 2016

Magnetic Resonance in Med

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“…Magnetic resonace spectra were recorded with water presaturation to determine the lipid and metabolite spectra (32 scans, center frequency at 3 ppm) and without water suppression to acquire the water signal as internal standard (16 scans, center frequency at 4.7 ppm to guarantee the reference signal to originate from the same region as the main lipid signals). Automatic fitting of the MR spectra was performed with a Fitting Tool for Arrays of Interrelated Datasets (FiTAID: Inselspital) by using prior knowledge restraints (19). The lipid spectrum was modeled with 9 Voigt lines.…”

Section: Analytic Proceduresmentioning

confidence: 99%