Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired                    in Healthy Brain at 26 Sites

Považan, Michal; Mikkelsen, Mark E.; Berrington, Adam; Bhattacharyya, Pallab K.; Brix, Maiken K.; Buur, Pieter F.; Cecil, Kim M.; Chan, Kimberly L.; Chen, David Y.T.; Craven, Alexander R.; Cuypers, Koen; Dacko, Michael; Duncan, Niall W.; Dydak, Ulrike; Edmondson, David; Ersland, Lars; Forbes, Megan; Gao, Fei; Greenhouse, Ian; Harris, Ashley D.; He, Naying; Heba, Stefanie; Hoggard, Nigel; Hsu, Tun-Wei; Jansen, Jacobus F.A.; Kangarlu, Alayar; Lange, Thomas; Li, Yan; Lin, Chien Yuan; Liou, Jy Kang; Lirng, Jiing Feng; Liu, Feng; Long, Joanna; Ma, Ruimin; Maes, Celine; Moreno-Ortega, Marta; Murray, Scott O.; Noah, Sean; Noeske, Ralph; Noseworthy, Michael D.; Oeltzschner, Georg; Porges, Eric C.; Prisciandaro, James J.; Puts, Nicolaas A.J.; Roberts, Timothy P. L.; Sack, Markus; Sailasuta, Napapon; Saleh, Muhammad G.; Schallmo, Michael-Paul; Simard, Nicholas; Stoffers, Diederick; Swinnen, Stephan P.; Tegenthoff, Martin; Truong, Peter; Wang, Guangbin; Wilkinson, Iain D.; Wittsack, Hans Jörg; Woods, Adam J.; Xu, Hongmin; Yan, Fuhua; Zhang, Chencheng; Zipunnikov, Vadim; Zöllner, Helge J.; Edden, Richard A.E.; Barker, Peter B.

doi:10.1148/radiol.2020191037

Cited by 37 publications

(59 citation statements)

References 27 publications

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“…This is also implied by the large algorithm-level effect for mI and Glx in the linear mixed-effects model. Comparing the variance patriation coefficients of this study with the prior single LCModel analysis 17 shows a high agreement in the variance partition for tNAA and tCho. A lower agreement is found for mI and Glx as the estimates, of those metabolites strongly differ between algorithms, introducing a high algorithm-level effect.…”

Section: Discussionsupporting

confidence: 51%

“…277 single-voxel short-TE PRESS brain datasets from healthy volunteers acquired in a recent 3T multisite-study 17 were included in this analysis. Data were acquired at 25 sites (with up to 12 subjects per site) on scanners from three different vendors (GE: 8 sites with n = 91; Philips: 10 sites with n = 112; and Siemens: 7 sites with n = 74) with the following parameters: TR/TE = 2000/35 ms; 64 averages; 2, 4 or 5 kHz spectral bandwidth; 2048-4096 data points; acquisition time = 2.13 min; 3×3×3 cm 3 voxel in the medial parietal lobe ( Figure 1A) .…”

Section: Methodsmentioning

confidence: 99%

“…Reference spectra were acquired with similar parameters, but without water suppression and 8-16 averages. No more acquisition parameters were specified (for more details, please refer to 17 ). Data were saved in vendor-native formats (GE P-files, Philips .sdat, and Siemens TWIX).…”

Section: Methodsmentioning

confidence: 99%

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Comparison of different linear-combination modelling algorithms for short-TE proton spectra

Zöllner

Považan

Hui

et al. 2020

Preprint

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Keywords 21• Magnetic resonance spectroscopy 22 Highlights 25 • Means and CVs for tNAA and tCho are highly consistent across vendors and algorithms. 26• Means and CVs for mI and Glx are less consistent across vendors and algorithms. 27• Agreement between metabolite estimates from different algorithms is moderate at best. 28• Baseline estimation contributes significantly to measurement variance. 29Abstract 30 Short-TE proton magnetic resonance spectroscopy is commonly used to study metabolism in the 31 human brain. Spectral modelling is a crucial analysis step, yielding quantitative estimates of me-32 tabolite levels. Commonly used quantification methods model the data as a linear combination of 33 metabolite basis spectra, maximizing the use of prior knowledge to constrain the model solution. 34 Various linear-combination modelling (LCM) algorithms have been integrated into widely used 35 commercial and open-source analysis programs. 36This large-scale, in-vivo, multi-vendor study compares the levels of four major metabolite com-37 plexes estimated by three LCM algorithms (Osprey, LCModel, and Tarquin). 277 short-TE spec-38 tra from a recent multi-site study were pre-processed with the Osprey software. The resulting 39 spectra were modelled with Osprey, Tarquin and LCModel, using the same vendor-specific basis 40 sets. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI), and gluta-41 mate+glutamine (Glx) were quantified with respect to total creatine (tCr). 42Mean spectra and models showed high agreement between all vendors and LCM algorithms. In 43 contrast, the algorithms differed notably in their baseline estimates and mI models. Group means 44 and CVs of the metabolite estimates agreed well for tNAA and tCho across vendors and algo-45 rithms. For mI and Glx, group means metabolite estimates and CVs agreed less well between 46 algorithms, with mI systematically estimated lower by Tarquin. Across all datasets, the metabo-47 lite-mean CV was 10.4% for Osprey, 12.6% for LCModel, and 14.0% for Tarquin. The grand 48 mean correlation coefficient for all pairs of LCM algorithms across all datasets and metabolites 49 was R 2 = 0.39, indicating generally moderate agreement of individual metabolite estimates be-50 tween algorithms. Stronger correlations were found for tNAA and tCho than for Glx and mI. 51 Correlations between pairs of algorithms were comparably moderate (Tarquin-vs-LCModel: R 2 52 = 0.43; Osprey-vs-LCModel: R 2 = 0.38; Osprey-vs-Tarquin: R 2 = 0.37). There was a signifi-53 cant association between local baseline power and metabolite estimates (grand mean R 2 = 0.10; 54 up to 0.62 for LCModel analysis of Glx in Siemens datasets). Metabolite estimates with stronger 55 associations to the local baseline power (mI and Glx) showed higher variance, suggesting that 56 baseline estimation has a stronger influence on those metabolites than on tNAA and tCho. 57While estimates of major metabolite complexes broadly agree between linear-combination mod-58 elling algorithms at group ...

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Section: Discussionsupporting

confidence: 51%

Section: Methodsmentioning

confidence: 99%

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Comparison of different linear-combination modelling algorithms for short-TE proton spectra

Zöllner

Považan

Hui

et al. 2020

Preprint

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show abstract

“…277 single‐voxel short‐TE PRESS brain datasets from healthy volunteers acquired in a recent 3 T multisite‐study 17 were included in this analysis. Data were acquired at 25 sites (with up to 12 subjects per site) on scanners from three different vendors (GE: eight sites with n = 91; Philips: 10 sites with n = 112; and Siemens: seven sites with n = 74) with the following parameters: TR/TE = 2000/35 ms; 64 averages; 2, 4 or 5 kHz spectral bandwidth; 2048–4096 data points; acquisition time = 2.13 min; 3 × 3 × 3 cm 3 voxels in the medial parietal lobe (Figure 1A ) .…”

Section: Methodsmentioning

confidence: 99%

Comparison of different linear‐combination modeling algorithms for short‐TE proton spectra

et al. 2021

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Short‐TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as a linear combination of metabolite basis spectra. This large‐scale multi‐site study compares the levels of the four major metabolite complexes in short‐TE spectra estimated by three linear‐combination modeling (LCM) algorithms. 277 medial parietal lobe short‐TE PRESS spectra (TE = 35 ms) from a recent 3 T multi‐site study were preprocessed with the Osprey software. The resulting spectra were modeled with Osprey, Tarquin and LCModel, using the same three vendor‐specific basis sets (GE, Philips and Siemens) for each algorithm. Levels of total N‐acetylaspartate (tNAA), total choline (tCho), myo‐inositol (mI) and glutamate + glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and coefficient of variations of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated as lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was trueR2¯= 0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean trueR2¯= 0.10). While mean estimates of major metabolite complexes broadly agree between linear‐combination modeling algorithms at group level, correlations between algorithms are only weak‐to‐moderate, despite standardized preprocessing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.

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“…Despite the strong potential of MRS for cutting-edge biomedical research and clinical applications, deviations from optimal data quality, processing strategy or quantification pipeline reduces the metabolic information that can be extracted. Resulting systematic errors have been suspected to be the key contributing factors for limited multi-site reproducibility 2 .…”

Section: Introductionmentioning

confidence: 99%

INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis

Gajdošík

Landheer

Swanberg

et al. 2021

Sci Rep

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In vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.

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Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites

Cited by 37 publications

References 27 publications

Comparison of different linear-combination modelling algorithms for short-TE proton spectra

Comparison of different linear-combination modelling algorithms for short-TE proton spectra

Comparison of different linear‐combination modeling algorithms for short‐TE proton spectra

INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis

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